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Differential mechanisms are remarkable mechanical elements that are widely utilized in various systems; nevertheless, conventional differential mechanisms are heavy and difficult to use in applications with limited design space. In this paper, a curved lightweight compliant type of differential mechanism is presented. This mechanism acquires its differential characteristic by having a high rotational stiffness when the mechanism is symmetrically actuated on two sides, while having a low rotational stiffness when actuated only on one side. The intrinsic elastic strain energy required for deformation of the compliant differential is compensated for by reintroduction of potential energy to make the mechanism neutrally stable. For the storage of potential energy, two preloaded linear springs were used. The rotational stiffness of the one-sided actuation around the neutral position of the compliant differential mechanism is hypothesized to be adjustable by changing the preload of the springs. The stiffness can be positive, zero, and negative, meaning that the mechanism can have neutral stability and bistability. The hypothesis is investigated using a simulated model in Ansys Parametric Design Language using optimized parameters to achieve the desired stiffness for the mechanism. The simulated model is validated using an experimental setup for both the one-sided and symmetrical actuation stages. The experimental results showed a high correlation with the simulations. The mechanism with optimized dimensions and preload showed neutral stability for a range of 16°. Bistability was found for preloads higher than the aforementioned optimized preload. A linear trend was found between the preload of the springs and the rotational stiffness of the mechanism at θ = 0. Furthermore, an output/input kinematic performance of 0.97 was found for the simulated results and 0.95 for the experimental results.

Wearable sensor data can be integrated and interpreted to improve the treatment of chronic conditions, such as diabetes, by enabling adjustments in treatment decisions based on physical activity and psychological stress assessments. The challenges in using biological analytes to frequently detect physical activity (PA) and acute psychological stress (APS) in daily life necessitate the use of data from noninvasive sensors in wearable devices, such as wristbands. We developed a recurrent multi-task deep neural network (NN) with long-short-term-memory architecture to integrate data from multiple sensors (blood volume pulse, skin temperature, galvanic skin response, three-axis accelerometers) and simultaneously detect and classify the type of PA, namely, sedentary state, treadmill run, stationary bike, and APS, such as non-stress, emotional anxiety stress, mental stress, and estimate the energy expenditure (EE). The objective was to assess the feasibility of using the multi-task recurrent NN (RNN) rather than independent RNNs for detection and classification of AP and APS. The multi-task RNN achieves comparable performance to independent RNNs, with the multi-task RNN having F1 scores of 98.00% for PA and 98.97% for APS, and a root mean square error (RMSE) of 0.728 calhr.kg for EE estimation for testing data. The independent RNNs have F1 scores of 99.64% for PA and 98.83% for APS, and an RMSE of 0.666 calhr.kg for EE estimation. The results indicate that a multi-task RNN can effectively interpret the signals from wearable sensors. Additionally, we developed individual and multi-task extreme gradient boosting (XGBoost) for separate and simultaneous classification of PA types and APS types. Multi-task XGBoost achieved F1 scores of 99.89% and 98.31% for the classification of PA types and APS types, respectively, while the independent XGBoost achieved F1 scores of 99.68% and 96.77%, respectively. The results indicate that both multi-task RNN and XGBoost can be used for the detection and classification of PA and APS without loss of performance with respect to individual separate classification systems. People with diabetes can achieve better outcomes and quality of life by including physical activity and psychological stress assessments in treatment decision-making.

Monitoring activities of daily living (ADLs) for wheelchair users, particularly spinal cord injury individuals is important for understanding the rehabilitation progress, customizing treatment plans, and observing the onset of sec- ondary health conditions. This work proposes an innovative sensory system for measuring and classifying ADLs relevant to secondary health conditions. We systematically evaluated multiple wearable sensors such as pressure distribution mats on the wheelchair seat, accelerometer data from the ear and wrists, and IMU data from the wheelchair wheels to achieve the best unobtrusive combination of sensors that successfully distinguished ADLs. Our work resulted in an XGBoost clas- sifier with a 20-second window size and extracted features in statistical, time, frequency, and wavelet domains, with an average class-wise F1 score of 82% (with only 3 out of 12 classes being mislabeled). Our study results demonstrate that the newly investigated modality of the bottom pressure mat emerges as the most relevant information source for recognizing ADLs, while heart and respiratory rates did not provide added value for the selected set of ADLs. The proposed sensory system and methodology proved high quality in most classes and easily extendable for long-term monitoring in outpatient rehabilitation, with the need for an extended database of activities.

The important role of ventilation in underground mines is to ensure safety and adequate environmental conditions in all accessible areas of a mine. This research aims to develop low-cost solutions for monitoring ventilation parameters in underground mines using the Internet of Things (IoT). A comparison between standard measuring equipment and a new low-cost wearable monitoring device prototype was performed, and the variables measured in an underground mine were pressure, temperature, and relative humidity. The results in all surveys indicate that the wearable monitoring device prototype can properly be used for continuous monitoring of the underground environment. The standard measurement devices for underground mines should continuously be used by mining companies as requested by local legislation. The low-cost wearable monitoring device developed should be viewed as a redundant measurement device for operators’ safety. The constant innovations in technology can support mining operators in anticipating problems, improving productivity, ensuring safety, and meeting standards at low investments.

Cardiovascular diseases are the primary cause of death around the world. Cardiovascular ailments can be monitored continuously using wearable devices, which are resource-constrained and battery-operated. Efficient data compression is a promising method to reduce transmission energy cost and extend battery lifetime. In this paper, we first propose two novel pruning methods with pruning matrix's entries taken from the set {−1, 0, 1}, which achieve similar reconstruction quality as compared to DCT pruning but with far less computational cost due to multiplierless operation. Reconstruction for these pruning methods is proposed using Gaussian elimination. Then we design two lightweight compression algorithms based on the pruning methods, i.e., sign compression algorithm and binary compression algorithm. The proposed compression algorithms and pruning methods are evaluated using performance metrics such as compression ratio, percentage root mean square difference, quality score, etc. Furthermore, The pruning methods are implemented in TelosB mote and execution time is evaluated. The experiments are conducted on the MIT-BIH arrhythmia database. The experimental results show that the proposed compression algorithms outperform the state-of-the-art methods.

According to the findings of a pilot investigation, women who have type 2 diabetes (T2DM) have an increased chance of developing breast cancer. In this study, we compared the accuracy of three different models for determining the likelihood of developing breast cancer in type 2 diabetics who had different characteristics at the outset. The information was included into a model with the goal of determining whether or not a patient with type 2 diabetes also had a higher risk of acquiring breast cancer. In addition, we conducted research to identify potential breast cancer risk factors in order to incorporate their influence into our study and take appropriate action. Researchers have turned to a method known as synthetic minority oversampling in order to gather more information from samples of communities who are underrepresented in their studies. The ratio of training data to test data was close to 39 to 1 for each question in the survey. The effectiveness of the Logistic Regression (LR), Artificial Neural Network (ANN), and Random Forest (RF) models were evaluated using metrics such as recall, accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), respectively. The area under the curve (AUC) for each of the three models was comparable (0.83 for LR, 0.865 for ANN, and 0.959 for RF). The RF model has the greatest AUC out of the three that were taken into consideration. When it came to forecasting whether or not T2DM patients will get breast cancer, the RF model performed the best out of the LR, ANN, and RF models.

Sudden cardiac death is a sudden and highly fatal condition. Implementing high-quality emergency cardiopulmonary resuscitation (CPR) early on is an effective rescue method for this disease. However, the rescue steps of CPR are complicated and difficult to remember, and the quantitative indicators are difficult to control, which leads to a poor quality of CPR emergency actions outside the hospital setting. Therefore, we have developed CPR emergency equipment with a multisensory feedback function, aiming to guide rescuers in performing CPR through visual, auditory, and tactile interaction. This equipment consists of three components: first aid clothing, an audio-visual integrated terminal, and a vital sign detector. These three components are based on a micro-power WiFi-Mesh network, enabling the long-term wireless transmission of the multisensor data. To evaluate the impact of the multisensory feedback CPR emergency equipment on nonprofessionals, we conducted a controlled experiment involving 32 nonmedical subjects. Each subject was assigned to either the experimental group, which used the equipment, or the control group, which did not. The main evaluation criteria were the chest compression (CC) depth, the CC rate, the precise depth of the CC ratio (5–6 cm), and the precise rate of the CC ratio -(100–120 times/min). The results indicated that the average CC depth in the experimental group was 51.5 ± 1.3 mm, which was significantly better than that of the control group (50.2 ± 2.2 mm, p = 0.012). Moreover, the average CC rate in the experimental group (110.1 ± 6.2 times/min) was significantly higher than that of the control group (100.4 ± 6.6 times/min) (p < 0.001). Compared to the control group (66.37%), the experimental group showed a higher proportion of precise CC depth (82.11%), which is closer to the standard CPR rate of 100%. In addition, the CC ratio of the precise rate was 93.75% in the experimental group, which was significantly better than that of 56.52% in the control group (p = 0.024). Following the experiment, the revised System Availability Scale (SUS) was utilized to evaluate the equipment’s usability. The average total SUS score was 78.594, indicating that the equipment’s acceptability range was evaluated as ‘acceptable’, and the overall adjective rating was ‘good’. In conclusion, the multisensory feedback CPR emergency equipment significantly enhances the CC performance (CC depth, CC rate, the precise depth of CC ratio, the precise rate of CC ratio) of nonprofessionals during CPR, and the majority of participants perceive the equipment as being easy to use.

Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model’s performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance.

Photoplethysmography (PPG) is used to measure blood volume changes in the microvascular bed of tissue. Information about these changes along time can be used for estimation of various physiological parameters, such as heart rate variability, arterial stiffness, and blood pressure, to name a few. As a result, PPG has become a popular biological modality and is widely used in wearable health devices. However, accurate measurement of various physiological parameters requires good-quality PPG signals. Therefore, various signal quality indexes (SQIs) for PPG signals have been proposed. These metrics have usually been based on statistical, frequency, and/or template analyses. The modulation spectrogram representation, however, captures the second-order periodicities of a signal and has been shown to provide useful quality cues for electrocardiograms and speech signals. In this work, we propose a new PPG quality metric based on properties of the modulation spectrum. The proposed metric is tested using data collected from subjects while they performed various activity tasks contaminating the PPG signals. Experiments on this multi-wavelength PPG dataset show the combination of proposed and benchmark measures significantly outperforming several benchmark SQIs with improvements of 21.3% BACC (balanced accuracy) for green, 21.6% BACC for red, and 19.0% BACC for infrared wavelengths, respectively, for PPG quality detection tasks. The proposed metrics also generalize for cross-wavelength PPG quality detection tasks.

Paroksizminis prieširdžių virpėjimas (PPV) gali būti veikiamas įvairių provokuojančių \linebreak veiksnių, kurių dažnu atveju pacientas gali išvengti ar sumažinti. Dažniausiai įvardinti provokuojantys veiksniai: alkoholis, fizinė perkrova, miego trūkumas, stresas, persivalgymas, šaltas maistas ir šalti gėrimai. Šio darbo tikslas - ištirti galimybes vertinti PPV provokuojančių veiksnių sąryšį su ilgalaikiais fiziologiniais duomenimis, užregistruotais kasdienės gyvensenos sąlygomis, pacientams kuriems diagnozuotas PPV. Darbe analizuoti 16 pacientų realūs PPV įrašai, užregistruoti kasdienės gyvensenos sąlygomis. Siekta ištirti fotopletizmogramos galimybes atpažinti PPV epizodus ir įvertinti diagnostiniai teisingumo vertinimo įverčiai. Įgyvendinti trys galimai paroksizminį prieširdžių virpėjimą provokuojančių veiksnių atpažinimo algoritmai: psichologinio streso, gulėjimo ant kairiojo šono ir fizinė perkrovos. Provokuojančių veiksnių atpažinimas leidžia objektyviai vertinti PPV sąryšį su epizodų pasireiškimu, bei nenaudoti subjektyvių klausimynų. Analizuojami įvedami ir atpažįstami provokuojantys PPV epizodus veiksniai. Provokuojančių veiksnių vertinimas atliktas lyginant santykines PV trukmes prieš ir po įvestų ar atpažintų provokuojančių veiksnių. PPV atpažinimo galimybių ilgalaikiuose fotopletizmogramos signaluose tyrimas parodė, kad nepaisant aukšto specifiškumo (0,98), jautrumas nėra pakankamas (0,58). Bendras diagnostinis tikslumas pagerėja naktį: jautrumas -- 0,69, specifiškumas - 0,98, o Metjuzo koreliacijos koeficientas - 0,86. Vertinant anotuotus aritmijos profilius, bendra santykinė PPV trukmė sudarė 25,6 %, o analizuojant gautus iš fotopletizmogramos - 15,6 %. Pastebėta, kad po psichologinio streso ir gulėjimo ant kairiojo šono atpažįstamų provokuojančių veiksnių, santykinė PPV trukmė gauta didesnė, o po fizinės perkrovos – mažesnė nei prieš provokuojantį veiksnį., Paroxysmal atrial fibrillation (PAF) can be triggered by various factors, including alcohol consumption, physical exertion, lack of sleep, stress, overeating, consuming cold food, and cold drinks. In most cases, these triggers can be avoided or minimized by modifying the patient's behavior. This study aims to examine the relationship between PAF triggers and long-term physiological data recorded in patients diagnosed with PAF under everyday conditions. The study analyzed the records of 16 patients who experienced PAF episodes in their daily lives. The objective was twofold: first, to determine the feasibility of photoplethysmography to detect PAF episodes, and second, to evaluate the performance measurements. Three algorithms were implemented to identify potential triggers of PAF: psychological stress, lying on the left side, and physical overload. By recognizing the triggers that provoke PAF episodes, the study aimed to objectively assess the relationship between photoplethysmography and the occurrence of episodes, without relying on subjective questionnaires. The study analyzed and identified the triggers that provoke episodes of PAF by comparing the relative durations of PAF before and after the introduction or recognition of the triggers. The investigation of the detection of PAF in long-term photoplethysmograph signals revealed that although specificity was high (0.98), the sensitivity was inadequate (0.58). However, overall diagnostic accuracy improved during the night, with a sensitivity of 0.69, specificity of 0.98, and Matthews correlation coefficient of 0.86. When evaluating the annotated arrhythmia profiles, the burden of atrial fibrillation was 25.6 %, while the analysis of the photoplethysmogram indicated a burden of 15.6 %. Furthermore, it was observed that the AF burden increased after psychological stress and lying on the left side, and decreased after physical overload.

This paper presents a healthcare information and medical resource management platform utilizing wearable devices, physiological sensors, and an indoor positioning system (IPS). This platform provides medical healthcare information management based on the physiological information collected by wearable devices and Bluetooth data collectors. The Internet of Things (IoT) is constructed for this medical care purpose. The collected data are classified and used to monitor the status of patients in real time with a Secure MQTT mechanism. The measured physiological signals are also used for developing an IPS. When the patient is out of the safety zone, the IPS will send an alert message instantly by pushing the server to remind the caretaker, easing the caretaker’s burden and offering extra protection for the patient. The presented system also provides medical resource management with the help of IPS. The medical equipment and devices can be tracked by IPS to tackle some equipment rental problems, such as lost and found. A platform for the medical staff work coordination information exchange and transmission is also developed to expedite the maintenance of medical equipment, providing the shared medical information to healthcare and management staff in a timely and transparent manner. The presented system in this paper will finally reduce the loading of medical staff during the COVID-19 pandemic period.

Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the American Heart Association, approximately 19.1 million deaths were attributed to CVDs in 2020, in particular, ischemic heart disease and stroke. Several known risk factors for CVDs include smoking, alcohol consumption, lack of regular physical activity, and diabetes. The last decade has been characterized by widespread diffusion in the use of wristband-style wearable devices which can monitor and collect heart rate data, among other information. Wearable devices allow the analysis and interpretation of physiological and activity data obtained from the wearer and can therefore be used to monitor and prevent potential CVDs. However, these data are often provided in a manner that does not allow the general user to immediately comprehend possible health risks, and often require further analytics to draw meaningful conclusions. In this paper, we propose a disentangled variational autoencoder (β-VAE) with a bidirectional long short-term memory network (BiLSTM) backend to detect in an unsupervised manner anomalies in heart rate data collected during sleep time with a wearable device from eight heterogeneous participants. Testing was performed on the mean heart rate sampled both at 30 s and 1 min intervals. We compared the performance of our model with other well-known anomaly detection algorithms, and we found that our model outperformed them in almost all considered scenarios and for all considered participants. We also suggest that wearable devices may benefit from the integration of anomaly detection algorithms, in an effort to provide users more processed and straightforward information.

Wearable and mobile technology has advanced in leaps and bounds in the last decade with technological advances creating a role from enhancing healthy living to monitoring and treating disease. However, the discussion about the ethical use of such commercial technology in the community, especially in minors, is lacking behind. In this paper, we first summarize the major ethical concerns that arise from the usage of commercially available wearable technology in children, with a focus on smart watches, highlighting issues around the consent process, mitigation of risk and potential confidentiality and privacy issues, as well as the potential for therapeutic misconceptions when used without medical advice. Then through a relevant thought experiment we move on to outline some further ethical concerns that are connected to the use of wearables by minors, to wit the issue of informed consent in the case of minors, forcing them to live in the spotlight, and compromising their right to an open future. We conclude with the view that mitigating potential pitfalls and enhancing the benefits of wearable technology especially for minors requires brave and comprehensive moral debates.

Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.

【はじめに】若年者では夜型のライフスタイルが多く、特に大学生では、他の世代と比較して、睡眠習慣の個人差が大きく、睡眠の質・量の低下による授業・勉学への悪影響がある。【目的】看護学生の睡眠ステージについて、ウェアラブルデバイスを用いて学年別の特性を明らかにすることである。【方法】A大学の2年～4年生までの各10名程度、計30名程度とした。対象者に1週間、ウェアラブル端末心拍計を装着してもらった。睡眠ステージ、心拍数、歩数、消費カロリーなどの測定結果を記載、睡眠時間を確保する工夫について自由記載を求めた。【倫理的配慮】対象者にとって回答しなくても不利益にならないこと、成績には反映されないこと、個人が特定されないことなどを口頭および書面で説明した。なお本研究は、日本赤十字九州国際看護大学の研究倫理審査委員会の承認を得て実施した（承認番号；19-008-①）。【結果】対象者は、2年生10名、3年生11名、4年生10名、計31名であった。睡眠ステージは、学年に関わらず、最も長かったのは浅い睡眠で50％程度、次にレム睡眠が20％、深い睡眠が17％程度、最も短かったのは夜間覚醒時間12％程度であった。2年生が浅い睡眠の比率が高く、3年生と4年生で深い睡眠の比率が同程度、レム睡眠は各学年とも同じ比率であり、3年生が夜間覚醒時間の比率が高かった。【考察】睡眠ステージの比率は、3年生の浅い睡眠がやや短いが、平均的な比率であることがわかった。, 【Background】Many young people often have a pattern of staying up late at night. Especially for college students, the individual differences in sleep habits are large compared with ages groups other than young people, and the deterioration of sleep quality and quantity has a large adverse effect on lessons and study.【Objective】The aim of this study is to clarify the characteristics of the sleep stage of nursing students by each grade using a wearable device.【Methods】A total of about 30 students, about 10 students each from the 2nd to 4th grades of University A, were targeted. The subject wore a wearable device terminal heart rate monitor for one week. The subjects described the measurement results such as sleep stage, heart rate, steps, and calories burned, and described the measures to secure sleep time. We explained to the subjects, verbally and in writing, which they would not be disadvantageous situation, that they would not affect their grades, and that no individual would be identified. This study was approved by the Research Ethics Review Committee of the Japan Red Cross Kyushu International Nursing University (approval number; 19-008-①).【Results】The subjects were 10 second graders, 11 third graders, and 10 fourth graders, for a total of 31 people. Regardless of grade, the longest was about 50% for light sleep, followed by REM sleep at about 20%, deep sleep at about 17%, and the shortest was about 12% at night awakening time. The percentage of light sleep was high in the second grade, the percentage of deep sleep was the same in the third and fourth grades, the percentage of REM sleep was the same in each grade, and the ratio of night awakening time was high in the third grade.【Conclusion】We found that the ratio of sleep stages was an average percentage, although light sleep in the third grade was a little short., 報告 = report

Wearable devices equipped with inertial and magnetic sensors are increasingly being employed for the monitoring and assessment of physical activity. The affordability, accuracy, and portability of such devices enable real-time monitoring of human movement during exercise, without requiring direct supervision by a trained professional. Furthermore, the data collected by these devices can be analyzed over time, providing valuable insights into an individual's patterns of physical activity and progress. While the initial interest focused mainly on aerobic exercises, research has recently begun to focus on strength exercises as well. The challenges researchers are facing with are primarily related to the minimization of the number and position of wearable devices on individual human body segments and the development of algorithms that will provide appropriate assessment and feedback. Addressing existing limitations and aiming for simplicity, generalizability and reliability, this thesis presents a comprehensive procedure for quantitative and qualitative monitoring of exercise performance, along with a measurement method for assessing human movement variability and movement execution metrics. The proposed method enables the detection, counting, and segmentation of repetitive movements during strength exercises without requiring prior knowledge of the specific exercise being performed. Additionally, the newly developed movement execution metric can identify incorrect repetitions during strength training exercises and distinguishing between experienced and inexperienced individuals. The scientific contributions are: Measurement method of human movement variability during exercise using sensor nodes with inertial and magnetic sensors, Movement execution metrics during exercise based on measurements using sensor nodes with inertial and magnetic sensors, Procedure for quantitative and qualitative monitoring of exercise performance using sensor nodes with inertial and magnetic sensors and measurement of heart rate. Nosivi uređaji opremljeni inercijskim i magnetskim senzorima sve se više koriste za praćenje i procjenu fizičke aktivnosti. Pristupačnost, točnost i prenosivost takvih uređaja omogućuju praćenje ljudskih pokreta tijekom tjelovježbe u stvarnom vremenu, bez potrebe za izravnim nadzorom stručnjaka. Nadalje, tako prikupljeni podaci mogu se analizirati tijekom vremena, pružajući vrijedne uvide u obrasce fizičke aktivnosti i napredak pojedinca. Iako je početni interes istraživačkih grupa uglavnom bio usmjeren na aerobne vježbe, istraživanja su se nedavno počela usmjeravati i na vježbe snage. Izazovi s kojima se istraživači suočavaju primarno su vezani uz minimiziranje broja i položaja nosivih uređaja na pojedinim dijelovima tijela te razvoj algoritama koji će pružiti odgovarajuću procjenu i povratne informacije. Imajući na umu postojeća ograničenja i težeći jednostavnosti, generaliziranosti i pouzdanosti, ova disertacija predstavlja sveobuhvatan postupak za kvantitativno i kvalitativno praćenje izvedbe vježbanja, zajedno s mjernom metodom za procjenu varijabilnosti ljudskog pokreta i metrikom izvođenja pokreta. Predložena metoda omogućuje detekciju, brojanje i segmentaciju ponavljajućih pokreta tijekom vježbi snage, bez potrebe za prethodnim poznavanjem specifične vježbe koja se izvodi. Također, novo razvijena metrika može identificirati neispravna ponavljanja tijekom vježbi snage te razlikovati iskusne i neiskusne pojedince. Ostvareni znanstveni doprinosi su: Metoda mjerenja varijabilnosti ljudskog pokreta tijekom tjelovježbe uporabom senzorskih čvorova s inercijskim i magnetskim senzorima, Metrika izvođenja pokreta tijekom tjelovježbe temeljena na mjerenjima senzorskim čvorovima s inercijskim i magnetskim senzorima, Postupak kvantitativnog i kvalitativnog praćenja uspješnosti vježbanja uporabom senzorskih čvorova s inercijskim i magnetskim senzorima i mjerenjem frekvencije srčanog ritma.

Estimating the amount of urine in the bladder is a major challenge for patients, such as those with spinal cord injuries and some elderly people, who lack the ability to feel when their bladders are full or who have difficulty getting to the toilet on time. For those who experience urine incontinence, real-time bladder monitoring can be quite beneficial. The two main types of invasive and non-invasive bladder volume monitoring devices can be separated. For precise urine volume measurement, invasive procedures involve directly inserting a catheter into the urethra. The urinary tract may be harmed, and it is uncomfortable and restricts the user's normal movements. [1]This study will investigate the measurement of bladder volume using non-invasive techniques like ultrasound. Urinary incontinence can consequently greatly benefit from real-time bladder monitoring. A variety of wearable devices with various sensing and communication technologies have recently been developed in an effort to produce both invasive and non-invasive approaches., IJSRED-International Journal of Scientific Research and Engineering Development

As an indispensable part of wearable devices and mechanical arms, stretchable conductors have received extensive attention in recent years. The design of a high-dynamic-stability, stretchable conductor is the key technology to ensure the normal transmission of electrical signals and electrical energy of wearable devices under large mechanical deformation, which has always been an important research topic domestically and abroad. In this paper, a stretchable conductor with a linear bunch structure is designed and prepared by combining numerical modeling and simulation with 3D printing technology. The stretchable conductor consists of a 3D-printed bunch-structured equiwall elastic insulating resin tube and internally filled free-deformable liquid metal. This conductor has a very high conductivity exceeding 104 S cm−1, good stretchability with an elongation at break exceeding 50%, and great tensile stability, with a relative change in resistance of only about 1% at 50% tensile strain. Finally, this paper demonstrates it as a headphone cable (transmitting electrical signals) and a mobile phone charging wire (transmitting electrical energy), which proves its good mechanical and electrical properties and shows good application potential.

Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (

With wearable sensors, the acquisition of physiological signals has become affordable and feasible in everyday life. Specifically, Photoplethysmography (PPG), being a low-cost and highly portable technology, has attracted notable interest for measuring and diagnosing cardiac activity, one of the most important physiological and autonomic indicators. In addition to the technological development, several specific signal-processing algorithms have been designed to enable reliable detection of heartbeats and cope with the lower quality of the signals. In this study, we compare three heartbeat detection algorithms: Derivative-Based Detection (DBD), Recursive Combinatorial Optimization (RCO), and Multi-Scale Peak and Trough Detection (MSPTD). In particular, we considered signals from two datasets, namely, the PPG-DALIA dataset (N = 15) and the FANTASIA dataset (N = 20) which differ in terms of signal characteristics (sampling frequency and length) and type of acquisition devices (wearable and medical-grade). The comparison is performed both in terms of heartbeat detection performance and computational workload required to execute the algorithms. Finally, we explore the applicability of these algorithms on the cardiac component obtained from functional Near InfraRed Spectroscopy signals (fNIRS).The results indicate that, while the MSPTD algorithm achieves a higher F1 score in cases that involve body movements, such as cycling (MSPTD: Mean = 74.7, SD = 14.4; DBD: Mean = 54.4, SD = 21.0; DBD + RCO: Mean = 49.5, SD = 22.9) and walking up and down the stairs (MSPTD: Mean = 62.9, SD = 12.2; DBD: Mean = 50.5, SD = 11.9; DBD + RCO: Mean = 45.0, SD = 14.0), for all other activities the three algorithms perform similarly. In terms of computational complexity, the computation time of the MSPTD algorithm appears to grow exponentially with the signal sampling frequency, thus requiring longer computation times in the case of high-sampling frequency signals, where the usage of the DBD and RCO algorithms might be preferable. All three algorithms appear to be appropriate candidates for exploring the applicability of heartbeat detection on fNIRS data.

Monitoring sweat secretion rate is essential for uncovering underlying physical conditions like hyperhidrosis, mental stress, and neural disorders. Often, flexible microfluidic sweat rate monitoring devices use tape as a means of attachment to the skin to tightly seal the collection area. While these single-use, adhesive-backed devices have lightweight and thin interfaces for mounting on the skin, their form factor complicates their potential integration with available commercial wearables, such as smartwatches. Here, a tape-free device, consisting of a 3D-printed sweat collector with a concave surface that is strapped onto the skin to form an effective seal, is presented. The materials, structure, and dimensions of the sweat collector are optimized for conformal device-to-skin contact and efficient capture of sweat. The collector is interfaced with a fluidic microchannel with embedded electrodes for continuous digital monitoring of sweat rate. Long-term exercise-induced local sweat rate from multiple body locations in both multi-subject and longitudinal studies is measured, depicting the correlation between the measured sweat profile and total body fluid loss. The simple installation procedure and reusability of this tape-free device make it a good candidate for integration with the band of a watch.

High-quality, uniaxially oriented, and flexible PbZr0.52Ti0.48O3 (PZT) films were fabricated on flexible RbLaNb2O7/BaTiO3 (RLNO/BTO)-coated polyimide (PI) substrates. All layers were fabricated by a photo-assisted chemical solution deposition (PCSD) process using KrF laser irradiation for photocrystallization of the printed precursors. The Dion–Jacobson perovskite RLNO thin films on flexible PI sheets were employed as seed layers for the uniaxially oriented growth of PZT films. To obtain the uniaxially oriented RLNO seed layer, a BTO nanoparticle-dispersion interlayer was fabricated to avoid PI substrate surface damage under excess photothermal heating, and the RLNO has been orientedly grown only at around 40 mJ·cm−2 at 300 °C. The prepared RLNO seed layer on the BTO/PI substrate showed very high (010)-oriented growth with a very high Lotgering factor (F(010) = 1.0). By using the flexible (010)-oriented RLNO film on BTO/PI, PZT film crystal growth was possible via KrF laser irradiation of a sol–gel-derived precursor film at 50 mJ·cm−2 at 300 °C. The obtained PZT film showed highly (001)-oriented growth on the flexible plastic substrates with F(001) = 0.92 without any micro-cracks. The RLNO was only uniaxial-oriented grown at the top part of the RLNO amorphous precursor layer. The oriented grown and amorphous phases of RLNO would have two important roles for this multilayered film formation: (1) triggering orientation growth of the PZT film at the top and (2) the stress relaxation of the underneath BTO layer to suppress the micro-crack formation. This is the first time that PZT films have been crystallized directly on flexible substrates. The combined processes of photocrystallization and chemical solution deposition are a cost-effective and highly on-demand process for the fabrication of flexible devices.

In late 2019, a new genre of coronavirus (COVID-19) was first identified in humans in Wuhan, China. In addition to this, COVID-19 spreads through droplets, so quarantine is necessary to halt the spread and to recover physically. This modern urgency creates a critical challenge for the latest technologies to detect and monitor potential patients of this new disease. In this vein, the Internet of Things (IoT) contributes to solving such problems. This paper proposed a wearable device that utilizes real-time monitoring to detect body temperature and ambient conditions. Moreover, the system automatically alerts the concerned person using this device. The alert is transmitted when the body exceeds the allowed temperature threshold. To achieve this, we developed an algorithm that detects physical exercise named “Continuous Displacement Algorithm” based on an accelerometer to see whether a potential temperature rise can be attributed to physical activity. The people responsible for the person in quarantine can then connect via nRF Connect or a similar central application to acquire an accurate picture of the person’s condition. This experiment included an Arduino Nano BLE 33 Sense which contains several other sensors like a 9-axis IMU, several types of temperature, and ambient and other sensors equipped. This device successfully managed to measure wrist temperature at all states, ranging from 32 °C initially to 39 °C, providing better battery autonomy than other similar devices, lasting over 12 h, with fast charging capabilities (500 mA), and utilizing the BLE 5.0 protocol for data wireless data transmission and low power consumption. Furthermore, a 1D Convolutional Neural Network (CNN) was employed to classify whether the user is feverish while considering the physical activity status. The results obtained from the 1D CNN illustrated the manner in which it can be leveraged to acquire insight regarding the health of the users in the setting of the COVID-19 pandemic.

Assessment of physical performance is essential to predict the frailty level of older adults. The modified Physical Performance Test (mPPT) clinically assesses the performance of nine activities: standing balance, chair rising up & down, lifting a book, putting on and taking off a jacket, picking up a coin, turning 360°, walking, going upstairs, and going downstairs. The activity performing duration is the primary evaluation standard. In this study, wearable devices are leveraged to recognize and predict mPPT items' duration automatically. This potentially allows frequent follow up of physical performance, and facilitates more appropriate interventions. Five devices, including accelerometers and gyroscopes, were attached to the waist, wrists and ankles of eight younger adults. The system was experimented within three aspects: machine learning models, sensor placement, and sampling frequencies, to which the non-causal six-stages temporal convolutional network using 6.25 Hz signals from the left wrist and right ankle obtained the optimal performance. The duration prediction error ranged from 0.63±0.29 s (turning 360°) to 8.21±16.41 s (walking). The results suggest the potential for the proposed system in the automatic recognition and segmentation of mPPT items. Future work includes improving the recognition performance of lifting a book and implementing the frailty score prediction. ispartof: Ieee Transactions On Neural Systems And Rehabilitation Engineering vol:30 ispartof: location:United States status: published

Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by movements of the subject's arm affect the performance of PPG-based HR tracking. This is typically addressed coupling the PPG signal with acceleration measurements from an inertial sensor. Unfortunately, most standard approaches of this kind rely on hand-tuned parameters, which impair their generalization capabilities and their applicability to real data in the field. In contrast, methods based on deep learning, despite their better generalization, are considered to be too complex to deploy on wearable devices. In this work, we tackle these limitations, proposing a design space exploration methodology to automatically generate a rich family of deep Temporal Convolutional Networks (TCNs) for HR monitoring, all derived from a single "seed" model. Our flow involves a cascade of two Neural Architecture Search (NAS) tools and a hardware-friendly quantizer, whose combination yields both highly accurate and extremely lightweight models. When tested on the PPG-Dalia dataset, our most accurate model sets a new state-of-the-art in Mean Absolute Error. Furthermore, we deploy our TCNs on an embedded platform featuring a STM32WB55 microcontroller, demonstrating their suitability for real-time execution. Our most accurate quantized network achieves 4.41 Beats Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of 47.65 mJ and a memory footprint of 412 kB. At the same time, the smallest network that obtains a MAE < 8 BPM, among those generated by our flow, has a memory footprint of 1.9 kB and consumes just 1.79 mJ per inference.

Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices. Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT. Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal. Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement., Frontiers in Physiology, 14, ISSN:1664-042X

Wearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletes’ performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In this paper, we present a wearable and a set of machine learning models that are able to deduce runners’ emotions during their training. The solution is based on the analysis of runners’ electrodermal activity, a physiological parameter widely used in the field of emotion recognition. As part of the DJ-Running project, we have used these emotions to increase runners’ motivation through music. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training.

Electrochromic technologies that exhibit low power consumption have been spotlighted recently. In particular, with the recent increase in demand for paper-like panel displays, faster coloration time has been focused on in researching electrochromic devices. Tungsten trioxide (WO3) has been widely used as an electrochromic material that exhibits excellent electrochromic performance with high thermal and mechanical stability. However, in a solid film-type WO3 layer, the coloration time was long due to its limited surface area and long diffusion paths of lithium ions (Li-ions). In this study, we attempted to fabricate a fibrous structure of WO3@poly(ethylene oxide) (PEO) composites through electrospinning. The fibrous and porous layer showed a faster coloration time due to a short Li-ion diffusion path. Additionally, PEO in fibers supports Li-ions being quickly transported into the WO3 particles through their high ionic conductivity. The optimized WO3@PEO fibrous structure showed 61.3 cm2/C of high coloration efficiency, 1.6s fast coloration time, and good cycle stability. Lastly, the electrochromic device was successfully fabricated on fabric using gel electrolytes and a conductive knitted fabric as a substrate and showed a comparable color change through a voltage change from −2.5 V to 1.5 V.

Background and Objective: The prevalence of chronic cardiovascular diseases (CVDs) has risen globally, nearly doubling from 1990 to 2019. ECG is a simple, non-invasive measurement that can help identify CVDs at an early and treatable stage. A multi-lead ECG, up to 15 leads in a wearable form factor, is desirable. We seek to derive multiple ECG leads from a select subset of leads so that the number of electrodes can be reduced in line with a patient-friendly wearable device. We further compare personalized derivations to generalized derivations. Methods: Long-Short Term Memory (LSTM) networks using Lead II, V2, and V6 as input are trained to obtain generalized models using Bayesian Optimization for hyperparameter tuning for all patients and personalized models for each patient by applying transfer learning to the generalized models. We compare quantitatively using error metrics Root Mean Square Error (RMSE), R2, and Pearson correlation (ρ). We compare qualitatively by matching ECG interpretations of board-certified cardiologists. Results: ECG interpretations from personalized models, when corrected for an intra-observer variance, were identical to the original ECGs, whereas generalized models led to errors. Mean performance values for generalized and personalized models were (RMSE-74.31 µV, R2-72.05, ρ-0.88) and (RMSE-26.27 µV, R2-96.38, ρ-0.98), respectively. Conclusions: Diagnostic accuracy based on derived ECG is the most critical validation of ECG derivation methods. Personalized transformation should be sought to derive ECGs. Performing a personalized calibration step to wearable ECG systems and LSTM networks could yield ambulatory 15-lead ECGs with accuracy comparable to clinical ECGs.

Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems.

Background: Wearable devices are shown to be an advanced tool for chronic disease management, but their impacts on physical activity remain uninvestigated. This study aims to examine the effect of wearable devices on physical activity in general people and chronic patients. Methods: Our sample was from the third cycle of the fifth iteration of the Health Information National Trends Survey (HINTS), which includes a total of 5438 residents. Genetic matching was used to evaluate the effect of wearable devices on physical activity in different populations. Results: (1) Both using wearable devices and using them with high frequency will improve physical activity for the whole population. (2) Wearable devices may have greater positive effects on physical activity for chronic patients. (3) Especially in patients with hypertension, high-frequency use of wearable devices can significantly improve the duration and frequency of physical activity. Conclusions: Wearable devices lead to more physical activity, and the benefit is more noticeable for chronic patients, particularly those with hypertension.

Gait quality characteristics obtained from daily-life accelerometry are clinically relevant for fall risk in older adults but it is unknown whether these characteristics are responsive to changes in gait quality. We aimed to test whether accelerometry-based daily-life gait quality characteristics are reliable and responsive to changes over one year in older adults who experienced a fall or an exercise intervention. One-week trunk acceleration data were collected from 522 participants (65–97 years), at baseline and after one year. We calculated median values of walking speed, regularity (sample entropy), stability (logarithmic rate of divergence per stride), and a gait quality composite score, across all 10-s gait epochs derived from one-week gait episodes. Intraclass correlation coefficients (ICC) and limits of agreement (LOA) were determined for 198 participants who did not fall nor participated in an exercise intervention during follow-up. For responsiveness to change, we determined the number of participants who fell (n = 209) or participated in an exercise intervention (n = 115) that showed a change beyond the LOA. ICCs for agreement between baseline and follow-up exceeded 0.70 for all gait quality characteristics except for vertical gait stability (ICC = 0.69, 95% CI [0.62, 0.75]) and walking speed (ICC = 0.68, 95% CI [0.62, 0.74]). Only walking speed, vertical and mediolateral gait stability changed significantly in the exercisers over one year but effect sizes were below 0.2. The characteristic associated with most fallers beyond the LOA was mediolateral sample entropy (4.8% of fallers). For the exercisers, this was gait stability in three directions and the gait quality composite score (2.6% of exercisers). The gait quality characteristics obtained by median values over one week of trunk accelerometry were not responsive to presumed changes in gait quality after a fall or an exercise intervention in older people. This is likely due to large (within subjects) differences in gait behaviour that participants show in daily life.

In clinical practice and research, innovative digital technologies have been proposed for the characterization of neuromuscular and movement disorders through objective measures. Among these, wearable devices prove to be a suitable solution for tele-monitoring, tele-rehabilitation, and daily activities monitoring. Inertial Measurement Units (IMUs) are low-cost, compact, and easy-to-use wearable devices that evaluate kinematics during different movements. Kinematic variables could support the clinical evaluation of the progression of some neuromuscular diseases and could be used as outcome measures. The current review describes the use of IMUs for the biomechanical assessment of meaningful outcome measures in individuals affected by Duchenne muscular dystrophy (DMD). The PRISMA methodology was used and the search was conducted in different databases (Scopus, Web of Science, PubMed). A total of 23 articles were examined and classified according to year of publication, ambulatory/non-ambulatory subjects, and IMU positioning on human body. The analysis points out the recent regulatory identification of Stride Velocity 95th Centile as a new endpoint in therapeutic DMD trials when measured continuously from a wearable device, while only a few studies proposed the use of IMUs in non-ambulatory patients. Clinical recognition of reliable and accurate outcome measures for the upper body is still a challenge.

The aim of this thesis is to highlight the existing body of literature on the utility of wearable and handheld devices in the diagnosis and management of cardiac arrhythmias. Furthermore, the thesis investigates the accuracy and utility of the AliveCor Kardia for the detection of cardiac arrhythmias in a systematic fashion.

Heart failure (HF) is a severe disease and one of the most important causes of death in our society nowadays. A significant percentage of patients hospitalized for decompensation of heart failure are readmitted after some weeks or months due to an expected bad and uncontrolled HF evolution due to the lack of the patient supervision in real time. Herein is presented a straightforward electric model useful for volume leg section calculus based on the bioimpedance test as a way to assist with the acute HF patient’s supervision. The method has been developed for time-evolution edema evaluation in patients’ corresponding legs. The data are picked up with a wearable device specifically developed for acute heart failure patients. As an initial step, a calibration method is proposed to extract the extracellular volume component from bioimpedance measurements done in healthy subjects, and then applied to unhealthy ones. The intra- and extracellular resistance components are calculated from fitted Cole–Cole model parameters derived from BI spectroscopy measurements. Results obtained in a pilot assay, with healthy subjects and heart failure subjects, show sensitivities in leg volume [mL/Ω], with much lower values for healthy than in unhealthy people, being an excellent biomarker to discriminate between both. Finally, circadian cycle evolution for leg volume has been measured from the bioimpedance test as an extension of the work, enabling an alternative parameter for the characterization of one day of human activity for any person. Ministerio de Ciencia, Innovación y Universidades DTS19/00134, DTS19/00137 Plan Andaluz de Investigación, Desarrollo e Innovación AT21-00010-USE

Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans. info:eu-repo/semantics/publishedVersion

Kinematic gait assessment is essential to the gait rehabilitation program after stroke. Portable devices composed of inertial sensors are an alternative for this evaluation. However, knowledge regarding the psychometric properties of these devices is needed to understand their accuracy, especially in evaluation of individuals with movement disorders (e.g., people post stroke). This systematic review aims to analyze the psychometric properties of portable devices that use inertial sensors to assess kinematic gait parameters in people post stroke. We will also investigate which portable device assesses alterations in lower limb angular movements during gait.

Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms. As disease progresses, fluctuations in the response to levodopa treatment may develop, along with emergence of freezing of gait (FoG) and levodopa induced dyskinesia (LiD). The optimal management of the motor symptoms and their complications, depends, principally, on the consistent detection of their course, leading to improved treatment decisions. During the last few years, wearable devices have started to be used in the clinical practice for monitoring patients' PD-related motor symptoms, during their daily activities. This work describes the results of 2 multi-site clinical studies (PDNST001 and PDNST002) designed to validate the performance and the wearability of a new wearable monitoring device, the PDMonitor®, in the detection of PD-related motor symptoms. For the studies, 65 patients with Parkinson's disease and 28 healthy individuals (controls) were recruited. Specifically, during the Phase I of the first study, participants used the monitoring device for 2–6 h in a clinic while neurologists assessed the exhibited parkinsonian symptoms every half hour using the Unified Parkinson's Disease Rating Scale (UPDRS) Part III, as well as the Abnormal Involuntary Movement Scale (AIMS) for dyskinesia severity assessment. The goal of Phase I was data gathering. On the other hand, during the Phase II of the first study, as well as during the second study (PDNST002), day-to-day variability was evaluated, with patients in the former and with control subjects in the latter. In both cases, the device was used for a number of days, with the subjects being unsupervised and free to perform any kind of daily activities. The monitoring device produced estimations of the severity of the majority of PD-related motor symptoms and their fluctuations. Statistical analysis demonstrated that the accuracy in the detection of symptoms and the correlation between their severity and the expert evaluations were high. As a result, the studies confirmed the effectiveness of the system as a continuous telemonitoring solution, easy to be used to facilitate decision-making for the treatment of patients with Parkinson's disease.

Wearable devices are increasingly popular in clinical and non-clinical populations as a tool for exercise prescription, monitoring of daily physical activity and nutrition, and health-related parameters management. In this regard, smart devices not only assist people in pursuing a healthier lifestyle, but also provide a constant stream of physiological and metabolic data for management of non-communicable diseases (NCDs). Although the benefits of lifestyle-based interventions (exercise and nutrition) for NCDs are well known, the potential of wearable devices to promote healthy behaviors in clinical populations is still controversial. In this narrative review, we aimed to discuss the current application of wearable devices in NCDs, highlighting their role in prescribing and monitoring daily physical activity and dietary habits in the population living with chronic diseases. None of the studies considered specifically addressed the efficacy of the use of wearable devices, and limited are those that incorporate monitoring of both physical activity and nutrition for NCDs. However, there is evidence that such devices have helped improve physical activity levels, physical fitness, body composition, and metabolic and psychological parameters. Therefore, the authors believe that the benefits obtained from the use of wearable devices are likely to translate to public health and represent one of the important tools for the development of prevention plans in everyday life and clinical practice for optimal patient management.

Advances in wearable device technology pave the way for wireless health monitoring for medical and non-medical applications. In this work, we present a wearable heart rate monitoring platform communicating in the sub-6GHz 5G ISM band. The proposed device is composed of an Aluminium Nitride (AlN) piezoelectric sensor, a patch antenna, and a custom printed circuit board (PCB) for data acquisition and transmission. The experimental results show that the presented system can acquire heart rate together with diastolic and systolic duration, which are related to heart relaxation and contraction, respectively, from the posterior tibial artery. The overall system dimension is 20 mm by 40 mm, and the total weight is 20 g, making this device suitable for daily utilization. Furthermore, the system allows the simultaneous monitoring of multiple subjects, or a single patient from multiple body locations by using only one reader. The promising results demonstrate that the proposed system is applicable to the Internet of Healthcare Things (IoHT), and particularly Integrated Clinical Environment (ICE) applications.

Contains fulltext : 293211.pdf (Publisher’s version ) (Open Access) BACKGROUND: Nurse engagement, perceived need and usefulness affect healthcare technology use, acceptance and improvements in quality, safety and accessibility of healthcare. Nurses' opinions regarding continuous monitoring appear to be positive. However, facilitators and barriers were little studied. This study explored nurses' post-implementation experiences of the facilitators and barriers to continuously monitoring patients' vital signs using a wireless device on general hospital wards. METHODS: This study employed a cross-sectional survey. Vocational and registered nurses from three general wards in a Dutch tertiary university hospital participated in a survey comprising open and closed questions. The data were analysed using thematic analysis and descriptive statistics. RESULTS: Fifty-eight nurses (51.3%) completed the survey. Barriers and facilitators were identified under four key themes: (1) timely signalling and early action, (2) time savings and time consumption, (3) patient comfort and satisfaction and (4) preconditions. CONCLUSIONS: According to nurses, early detection and intervention for deteriorating patients facilitate the use and acceptance of continuously monitoring vital signs. Barriers primarily concern difficulties connecting patients correctly to the devices and system.

Contains fulltext : 292354.pdf (Publisher’s version ) (Open Access) BACKGROUND: The emerging study of wearable devices (WDs) in patients with cancer provides opportunities to harness real-time patient data for predicting clinical outcomes. We conducted a systematic review with best evidence synthesis to examine the association between WD metrics and clinical outcomes in patients with cancer. METHODS: MEDLINE and Embase were searched from inception until June 2022. Risk of bias assessment and best evidence synthesis were performed and, If possible, meta-analysis was conducted. RESULTS: A total of 34 studies was included. We found moderate-to-strong evidence for associations between circadian rest-activity metrics and OS. Disrupted I

BACKGROUND: Most young adults and adolescents in the United Arab Emirates (UAE) do not meet the established internationally recommended physical activity levels per day. The Arab Teen Lifestyle Study (ATLS) physical activity questionnaire has been recommended for measuring self-reported physical activity of Arab adolescents and young adults (aged 14 years to mid-twenties). The first version of the ATLS has been validated with accelerometers and pedometers (r ≤ 0.30). The revised version of the questionnaire (ATLS-2, 2021) needs further validation. The aim of this study was to validate the self-reported subjective sedentary and physical activity time of the ATLS-2 (revised version) physical activity questionnaire with that of Fibion accelerometer-measured data. METHODS: In this cross-sectional study, 131 healthy adolescents and young adults (aged 20.47 ± 2.16 [mean ± SD] years (range 14-25 years), body mass index 23.09 ± 4.45 (kg/m2) completed the ATLS-2 and wore the Fibion accelerometer for a maximum of 7 days. Participants (n = 131; 81% non-UAE Arabs (n = 106), 13% Asians (n = 17) and 6% Emiratis (n = 8)) with valid ATLS-2 data without missing scores and Fibion data of minimum 10 h/day for at least 3 weekdays and 1 weekend day were analyzed. Concurrent validity between the two methods was assessed by the Spearman rho correlation and Bland-Altman plots. RESULTS: The questionnaire underestimated sedentary and physical activity time compared to the accelerometer data. Only negligible to weak correlations (r ≤ 0.12; p > 0.05) were found for sitting, walking, cycling, moderate intensity activity, high intensity activity and total activity time. In addition, a proportional/systematic bias was evident in the plots for all but two (walking and moderate intensity activity time) of the outcome measures of interest. CONCLUSIONS: Overall, self-reported ATLS-2 sedentary and physical activity time had low correlation and agreement with objective Fibion accelerometer measurements in adolescents and young adults in the UAE. Therefore, sedentary and physical activity assessment for these groups should not be limited to self-reported measures.

In manufacturing, ergonomic and productivity benefits may result from combining the sensing and dexterity of the human workers with the strength of collaborative robots (cobots). Anyway, manual tasks requiring repetitive motion or working with robots can cause musculoskeletal disorders especially of the hands. In such a context, this paper proposes a novel solution that includes both a full body inertial system and a dedicated solution for hand tracking. This allows implementing the standard methods for assessing ergonomic indexes and to introduce new advanced task analyses based on gesture evaluation. The benefits reached by the integrated system for body and hand tracking can be exploited in the virtual prototyping of collaborative workstation in the manufacturing domain. Different software packages have been considered to model and simulate the collaboration between the operator and the cobot. In particular, Unity and ROS are used to develop the virtual scene and control the cobot behavior and the HTC Vive Head Mounted Display (HMD) to interact with the virtual environment. Physical ergonomics is evaluated by using both a full body suit (XSENS) and sensorized gloves (MANUS). The solution has been applied to the simulation of a virtual production line where the operator collaborates with a six-axis cobot (NIRYO ONE). Test tasks require the operator to move and manage different objects and tools by simulating different types of grasping. Real-time feedback about ergonomics, in terms of posture evaluation and alerts in case of critical condition, is shown to the operator as well as the automatic generation of reports for the post-process evaluation of the entire process. The results show how the use of these tools can be effective in the ergonomic evaluation of a collaborative robotic workstation which requires manual tasks.

Eighty one percent of adolescents aged 11–17 years are inadequately physically active worldwide. Physical activity (PA) recommendations for high school children have not been studied previously in schools in the Qatar region. The objectives of the study were: (i) to assess the level of compliance of the recommended PA and to assess if there are any gender differences; and (ii) to analyze the recommended step count compliance during school and non-school days. An observational cross-sectional study was conducted. Twenty-nine children (12 boys and 17 girls) aged 13–17 years (15.24 ± 1.46) took part in this study. Participants wore Fitbit Charge 5 wrist bands for three weeks to collect various digital biomarkers including moderate-to-vigorous physical activity (MVPA) and step counts (tracking during out-of-school time and school time). Based on this study, high school children in the two Qatar region schools did not meet the MVPA and steps/day recommendation by the established agencies: 38% of the total study group met the recommended 60 min/day of activity (50% boys, 29% girls). Gender differences were also observed in PA levels and steps per day: for non-school days, 17% met the recommended 10,000 steps/day (25% boys, 12% girls). There was a pattern of greater PA performance and steps during the weekdays as opposed to the weekend, but these values showed no robust evidence in favor of H1 or statistical significance for step counts. However, the evidence was robust in favor of H1 (difference between weekend and weekday) due to a statistically significant difference for meeting the 60 min/day activity. While further studies are required to establish if this is a general trend in Qatari schools, this pilot study does highlight the need to design more effective programs and messaging strategies to improve PA levels in the high school population.

Developing real-time biomechanical feedback systems for in-field applications will transfer human motor skills’ learning/training from subjective (experience-based) to objective (science-based). The translation will greatly improve the efficiency of human motor skills’ learning and training. Such a translation is especially indispensable for the hammer-throw training which still relies on coaches’ experience/observation and has not seen a new world record since 1986. Therefore, we developed a wearable wireless sensor system combining with artificial intelligence for real-time biomechanical feedback training in hammer throw. A framework was devised for developing such practical wearable systems. A printed circuit board was designed to miniaturize the size of the wearable device, where an Arduino microcontroller, an XBee wireless communication module, an embedded load cell and two micro inertial measurement units (IMUs) could be inserted/connected onto the board. The load cell was for measuring the wire tension, while the two IMUs were for determining the vertical displacements of the wrists and the hip. After calibration, the device returned a mean relative error of 0.87% for the load cell and the accuracy of 6% for the IMUs. Further, two deep neural network models were built to estimate selected joint angles of upper and lower limbs related to limb coordination based on the IMUs’ measurements. The estimation errors for both models were within an acceptable range, i.e., approximately ±12° and ±4°, respectively, demonstrating strong correlation existed between the limb coordination and the IMUs’ measurements. The results of the current study suggest a remarkable novelty: the difficulty-to-measure human motor skills, especially in those sports involving high speed and complex motor skills, can be tracked by wearable sensors with neglect movement constraints to the athletes. Therefore, the application of artificial intelligence in a wearable system has shown great potential of establishing real-time biomechanical feedback training in various sports. To our best knowledge, this is the first practical research of combing wearables and machine learning to provide biomechanical feedback in hammer throw. Hopefully, more wearable biomechanical feedback systems integrating artificial intelligence would be developed in the future.

Wearable devices have emerged as one of the most rapidly growing branches of the consumer electronics industry in recent years. Having a wide breadth of applications, ranging from leisure and fitness tracking to therapeutics and diagnostics, their development has become a critical driving force in the field of personalised medicine and point-of-care technologies. With the availability of more powerful processing techniques, efficient design approaches, and the miniaturisation of the basic building blocks that conform them, the capabilities of wearable devices have great potential for growth. Energy sources are one of the critical challenges associated with the design of wearable electronics. Renewable sources such as piezoelectric energy harvesters are of great interest, offering a viable alternative that can help tackle the problem of e-waste by enhancing the lifespan of a primary power source or as an independent power source. The piezoelectric active core materials of energy harvesters are the elements that allow for the conversion of mechanical energy to electrical energy. Contrary to the case of using piezoelectric ceramics, polymer based active cores offer superior flexibility, low manufacturing costs, and are non-toxic. However, their piezoelectric properties are comparatively lower than those of ceramics. Micro and nanofabrication methods for the manufacture of polymer based piezoelectric structures are of great interest in the field of energy harvesting because they allow for the tuning of specific morphological properties of these materials, offering the possibility of tailoring the material to the intended application and for the enhancement of the piezoelectric properties of the manufactured structures in some cases, which can bring the piezoelectric performance of polymer based materials closer to that of ceramics Electrospinning is a technique for the fabrication of nano and microfibrous structures based on the principles of electrohydrodynamics. This versatile manufacturing method not only allows for the fabrication of diverse morphologies of a material depending on the working parameters, ambient conditions and reagents, but can also intrinsically enhance the properties of the product. In this thesis, electrospinning will be used for the fabrication of polymer based piezoelectric materials. The work presented in the following chapters will focus firstly on the optimisation of the working parameters and on the composition of the polymer solutions for the fabrication of morphologically stable fibres and consequently will deal with improving the electrical response of these structures when they are used as the active core of a piezoelectric generator. Initial experimental work deals with the optimisation of polymer solutions containing the ferroelectric polymer poly(vinylidene fluoride) (PVDF). Favourable conditions for the fabrication of PVDF nanofibres were identified, and the resulting 2D fibrous mats were used for assembling a first iteration of piezoelectric generators. The findings indicated that the electrospun PVDF product had a favourable electrical response in spite of the morphology of the fibrous product not being ideal. Thus, improving the quality of the electrospun products would certainly allow for the fabrication of better performing generators. The use of chemical additives, solvent systems, and the combination of polymers for electrospinning can heavily influence the quality of the product. This thesis proceeds with the exploration of this premise, using combinations of PVDF with poly(ethylene oxide) (PEO) and lithium chloride (LiCl) for improving the quality of the material. Fibre morphology improved dramatically with the use of these additives, and it was observed that the fabricated fibrous structures could now transition to 3D materials under specific conditions, with variants ranging from a cloud-like structure to thick sponge-like fibrous mats. The conditions required for the production of 3D structures were found to be compatible with poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE), a copolymer known to have intrinsically superior piezoelectric properties than PVDF. The fabricated structures were used for assembling piezoelectric generators, and their electrical properties were shown to be comparable or to outperform similar state-of-the-art devices. Design opportunities were identified while working on the proposed piezoelectric generator architecture and the interfacing methods used for bonding the active core to the electrode materials. The thesis finalises with an exploration of additional methods that can be used to further increase the electrical response of generators with thick sponge-like fibrous PVDF-TrFE/PEO active cores. The findings of this final study revealed that electrode placement and design that conforms to the characteristics of the electrospun fibrous core and the use of electrode materials that can interface with both the surface of the active core and the fibrous network within the core material can improve the electrical output of the generators dramatically. The multidisciplinary work presented in this thesis explored fields ranging from chemistry and materials science to electronics and electrical engineering, laying the ground work upon which new research opportunities for the development of portable renewable energy sources can develop.

The surge of wearable device technology has enabled out-of-the-lab collection of running gait data for commercial, clinical, and coaching applications. However, low sampling frequencies interfere with measuring peak acceleration magnitudes accurately, and the ability to track relative changes during a prolonged run with lower sampling devices is unknown. The purposes of this study were to compare peak resultant acceleration measured simultaneously at different sampling frequencies and evaluate if different sampling frequencies could track similar relative changes in peak acceleration over a 20-min treadmill run. Seventeen participants ran on a treadmill at a self-selected, “easy” pace for 20-min (mean ± SD = 2.6 ± 0.4 m s−1). Three research-grade, triaxial accelerometers (“HiRes” = 1200 Hz, “MedRes” = 462 Hz, and “LoRes” = 100 Hz) were secured to each of three anatomical locations (tibia, low-back, forehead). Mean peak resultant accelerations from each device during minutes 3 to 18 were compared within each location (linear mixed model, α = 0.050). No significant device by timepoint interaction was observed ( p > 0.999). A significant main effect of sampling frequency at all three locations (HiRes > MedRes > LoRes; p

The huge expanding of the more seasoned population, higher caliber of distant medical services and therapy is presently expected in the developing nations. This consideration is particularly vital for the nations with 75% of the older people experiencing ongoing illnesses. Additionally, long haul observing of patients is a test to numerous families in this period of family units. Wireless Sensor Network (WSN) is turning into a promising innovation for different genuine applications. The high level miniature electro-mechanical frameworks (MEMS) with the sensor innovation persistently screen the ailment of a patient that is known as Wireless Body Area Network (WBAN).It can commonly expand over the entire human body and the hubs are associated through a remote correspondence channel. The compound sensor nodes in WBAN are fit for testing, handling, and imparting at least one imperative signs, for example, pulse, oxygen immersion level, action etc. as well as the ecological factors such as humidity, temperature, light and location, etc. of a patient and information transferred to far away using cloud platform. Enhanced wearable devices and cloud access delivers solution to health care field. In this paper, alongside the indispensable boundaries, the fatigue level and ecological boundaries likewise estimated and are sent through fringe interface to a Microcontroller unit (MCU). MCU is answerable for adjusting and investigating the crude information to remove the oxygen immersion, pulse, and temperature data, and afterward diverting the indispensable boundaries into the LoRa and Bluetooth module. The remote Bluetooth and LoRa module communicates these indispensable boundaries to different terminals, for example, cell phones and laptops. Any IoT stage can be utilized to store and recover the information. In light of the qualities got, the sicknesses are analyzed by the specialists from a good ways and caution is shipped off the patient.The test results show that the framework is compelling and dependable for the assortment, transmission, and show of ECG information continuously for the motivation behind tele monitoring of patients with coronary illness