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History is full of technological breakthroughs that arguably were first discovered because fundamental research was conducted without commercial influence. Fundamental research also plays an important role in producing the next generation of researchers. However, this article argues that research in general, and particularly biosensor research, is suffering from diminishing impact because of a lack of entrepreneurial mindset when defining research objectives. The story of the author's own biosensor research pathway is presented, and it provides an exemplary case study of this larger problem. An entrepreneurial mindset enables a stronger foundation even at the stage of forming the research hypothesis: the need for the research is justified; the research is not duplicative and is positioned to create new knowledge; the research product will be more readily translatable by industry. Most graduates in sensor research will work in industry, and therefore those students exposed to an entrepreneurial mindset will also start their careers with a more complete set of the skills that industry is looking for. [ABSTRACT FROM AUTHOR]

Advances in wearable technologies now allow modern smartwatches to collect body composition estimates through bioelectrical impedance techniques embedded within their design. However, this technique is susceptible to increased measurement error when postural changes alter body fluid distribution. The purpose of this study was to evaluate the effects of postural orientation on body composition and total body water (TBW) estimates produced by smartwatch bioelectrical impedance analysis (SWBIA) and determine its agreement with criterion measures. For this cross-sectional evaluation, 117 (age: 21.4±3.0 y; BMI: 25.3±5.7 kg/m2) participants (F:69, M:48) completed SWBIA measurements while in the seated, standing, and supine positions, then underwent criterion dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance spectroscopy (BIS) assessments. In the combined sample and females, body fat percent, fat mass, and fat-free mass using SWBIA were significantly different between the supine and standing positions (all p

Breathing is one of the most vital functions for being mentally and emotionally healthy. A growing number of studies confirm that breathing, although unconscious, can be under voluntary control. However, it requires systematic practice to acquire relevant experience and skillfulness to consciously utilize breathing as a tool for self-regulation. After the COVID-19 pandemic, a global discussion has begun about the potential role of emerging technologies in breath-control interventions. Emerging technologies refer to a wide range of advanced technologies that have already entered the race for mental health training. Artificial intelligence, immersive technologies, biofeedback, non-invasive neurofeedback, and other wearable devices provide new, but yet underexplored, opportunities in breathing training. Thus, the current systematic review examines the synergy between emerging technologies and breathing techniques for improving mental and emotional health through the lens of skills development. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is utilized to respond to the objectives and research questions. The potential benefits, possible risks, ethical concerns, future directions, and implications are also discussed. The results indicated that digitally assisted breathing can improve various aspects of mental health (i.e., attentional control, emotional regulation, mental flexibility, stress management, and self-regulation). A significant finding of this review indicated that the blending of different technologies may maximize training outcomes. Thus, future research should focus on the proper design and evaluation of different digital designs in breathing training to improve health in different populations. This study aspires to provide positive feedback in the discussion about the role of digital technologies in assisting mental and emotional health-promoting interventions among populations with different needs (i.e., employees, students, and people with disabilities). [ABSTRACT FROM AUTHOR]

Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors. [ABSTRACT FROM AUTHOR]

New technologies can help older persons age in place and support their caregivers. However, they need to be accepted by the end-users to do so. Technology acceptance models, such as TAM and UTAUT and their extensions, use factors like performance expectancy and effort expectancy to explain acceptance. Furthermore, they are based on quantitative methods. Our qualitative study investigates factors fostering and hindering acceptance among older persons and their caregivers for a variety of assistive technologies, including wearables, ambient sensors at home with and without cameras and social companion robots. The goal of this paper is twofold: On the one hand, it investigates the factors of technology acceptance models in a qualitative setting. On the other hand, it informs these models with aspects currently overlooked by them. The results reveal that performance expectancy and effort expectancy are relevant for acceptance. We also find that reliability, anxiety around technology and different social aspects have an influence on acceptance of assistive technology in aged care for all end-user groups. Our findings can be used to update current technology acceptance models and provide in-depth knowledge about the currently used factors. [ABSTRACT FROM AUTHOR]

With a constantly growing percentage of the population having access to high‐quality healthcare facilities, preventable pathogenic illnesses have been nearly eradicated in the developed parts of the world, which has led to a significant rise in the average human life expectancy over the last few decades. In such a highly developed world, age‐related illnesses will lead to an immense burden on healthcare providers. Remote health monitoring enabled by wearable sensors will play a significant role in the growth and evolution of Health 3.0 by providing intimate and valuable information to healthcare providers regarding the progression of disease in patients with critical life‐altering conditions. Especially, in the case of people suffering from neurodegenerative disorders, inexpensive and user‐friendly wearable sensors can enable physiotherapists monitor real‐time physiological parameters to design patient‐specific treatment plans. This review provides a comprehensive overview of the recent advances and emerging trends at the convergence of biomimicry and nanomaterial sensors, with a specific focus on wearable skin‐inspired mechanical sensors for applications in IoT‐enabled human physiological parameters monitoring. Skin‐inspired wearable mechanical sensors with relevance to the most common types of sensing mechanisms including piezoresistive, piezocapacitive, and triboelectric sensing are discussed along with their current challenges and possible future opportunities. [ABSTRACT FROM AUTHOR]

History is full of technological breakthroughs that arguably were first discovered because fundamental research was conducted without commercial influence. Fundamental research also plays an important role in producing the next generation of researchers. However, this article argues that research in general, and particularly biosensor research, is suffering from diminishing impact because of a lack of entrepreneurial mindset when defining research objectives. The story of the author’s own biosensor research pathway is presented, and it provides an exemplary case study of this larger problem. An entrepreneurial mindset enables a stronger foundation even at the stage of forming the research hypothesis: the need for the research is justified; the research is not duplicative and is positioned to create new knowledge; the research product will be more readily translatable by industry. Most graduates in sensor research will work in industry, and therefore those students exposed to an entrepreneurial mindset will also start their careers with a more complete set of the skills that industry is looking for.

Remote patient monitoring is a critical component of digital medicine, and the COVID-19 pandemic has further highlighted its importance. Wearable sensors aimed at noninvasive extraction and transmission of high-fidelity physiological data provide an avenue toward at-home diagnostics and therapeutics; however, the infrastructure requirements for such devices limit their use to areas with well-established connectivity. This accentuates the socioeconomic and geopolitical gap in digital health technology and points toward a need to provide access in areas that have limited resources. Low-power wide area network (LPWAN) protocols, such as LoRa, may provide an avenue toward connectivity in these settings; however, there has been limited work on realizing wearable devices with this functionality because of power and electromagnetic constraints. In this work, we introduce wearables with electromagnetic, electronic, and mechanical features provided by a biosymbiotic platform to realize high-fidelity biosignals transmission of 15 miles without the need for satellite infrastructure. The platform implements wireless power transfer for interaction-free recharging, enabling long-term and uninterrupted use over weeks without the need for the user to interact with the devices. This work presents demonstration of a continuously wearable device with this long-range capability that has the potential to serve resource-constrained and remote areas, providing equitable access to digital health. [ABSTRACT FROM AUTHOR]

COVID-19 has had a devastating impact worldwide, including in care homes where there have been substantial numbers of cases among a very vulnerable population. A key mechanism for managing exposure to the virus and targeting interventions is contact tracing. Unfortunately, environments such as care homes that were most catastrophically impacted by COVID-19 are also those least amenable to traditional contact tracing. A promising alternative to recall and smartphone-based contact tracing approaches is the use of discrete wearable devices that exploit Bluetooth Low Energy (BLE) and Long-Range Wide Area Network (LoRaWAN) technologies. However, the real-world performance of these devices in the context of contact tracing is uncertain. A series of experiments were conducted to evaluate the performance of a wearables system that is based on BLE and LoRaWAN technologies. In each experiment, the number of successful contacts was recorded and the physical distance between two contacts was compared to a calculated distance using the Received Signal Strength Indication (RSSI) to determine the precision, error rate, and duration of proximity. The overall average system contact detection success rate was measured as 75.5%; when wearables were used as per the manufacturer's guidelines the contact detection success rate increased to 81.5%, but when obstructed by everyday objects such as clothing or inside a bag the contact detection success rate was only 64.2%. The calculated distance using RSSI was close to the physical distance in the absence of obstacles. However, in the presence of typical obstacles found in care home settings, the reliability of detection decreased, and the calculated distance usually appeared far from the actual contact point. The results suggest that under real-world conditions there may be a large proportion of contacts that are underestimated or undetected. [ABSTRACT FROM AUTHOR]

There is a need for real-time and predictive data on alcohol use both broadly and specific to HIV. However, substance use and HIV data often suffer from lag times in reporting as they are typically measured from surveys, clinical case visits and other methods requiring extensive time for collection and analysis. Social big data might help to address this problem and be used to provide near real-time assessments of people's alcohol use and/or alcohol. This manuscript describes three types of social data sources (i.e., social media data, internet search data, and wearable device data) that might be used in surveillance of alcohol and HIV, and then discusses the implications and potential of implementing them as additional tools for public health surveillance.

Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors.

We created and carried out a cross-sectional anonymous structured questionnaire on what motivates users of mobile health applications and wearables to share their collected health related data. The questionnaire was distributed online in English, French, and Norwegian. In addition, a flyer with information of where to locate the online questionnaire was distributed during a Swiss health conference. We used snowball sampling and encouraged participants to forward the questionnaires to friends, family, and others. Data were collected between October 2018 and March 2020. 58.1 % (n = 473) responded to the English survey, 34.3 % (n = 279) responded to the French survey, and 7.6 % (n = 62) responded to the Norwegian survey.The questionnaire contained 38 questions divided into seven themes: Background and health goals, Wearables and sensors, Mobile applications, Logging of health data, Data sharing- and integration, Social media and entertainment, and Demographics (age, gender, country of origin, chronic disease status, and chronic disease caretaker status). Answer options were single answer, multiple-choice, open-ended, or on a 4-point Likert scale.Questions were defined based on 16 in-person interviews with people without any chronic disorder, people with diabetes, and people with sickle cell disease. All questions were optional. Data were collected from 814 participants. All answers to the open-ended questions have been translated into English.This dataset is especially interesting for researchers interesting in what motivates people with and without chronic disease across countries to use mHealth tools and share their collected health data. Only a subset of variables has been analyzed so far and new research questions on motivation can potentially be answered using this dataset.

Decompressive craniectomy (DC) is a surgical procedure where a portion of the skull is removed to relieve potentially fatal brain swelling. As the swelling can take months to subside, the patient is discharged from an acute care facility to recover prior to cranioplasty (reconstruction surgery). Cranioplasty is associated with complications due to infection, seizure, haematoma and death. The interval between these surgeries is potentially a modifiable risk factor to reduce the rate of complication. We aim to allow clinicians to remotely monitor patients to facilitate an optimal pre-operative review. We have developed a platform technology encompassing a 'smart' device fitted into a skullcap to measure physiological parameters, such as changes in brain swelling, and a clinician portal that allows remote viewing of the patients' physiological data. The use of patient generated data during the transition between craniectomy and cranioplasty has the potential to significantly improve neurorehabilitation outcomes for patients. [ABSTRACT FROM AUTHOR]

BACKGROUND: Persons with severe intellectual disability (ID) may be non-verbal and unable to communicate pain or distress. Consequently, painful conditions may go undetected, and non-verbal persons with ID may not receive adequate care and treatment. OBJECTIVE: This study aims to explore how professional caregivers and parents identify pain and distress in non-verbal persons with ID, and their attitudes towards using wearable sensors to identify pain and distress in daily life situations. METHODS: Exploratory, mixed method study. Caregivers (83) answered an online questionnaire, and professional caregivers (18) and parents (7) were interviewed. RESULTS: Professional caregivers and parents recognise pain and distress from observations and behavioural signs that are often equivocal. They experience that this is inadequate to reliably detect pain and distress in non-verbal persons with ID. Professional caregivers and parents' express frustration and fear that painful conditions may remain untreated. They are positive towards using wearable sensors on condition that sensors do not infringe on user autonomy and privacy. CONCLUSIONS: There is a need for sensors and methods that can objectively identify pain and distress and ensure adequate treatment, that may improve quality of life of non-verbal persons with ID. [ABSTRACT FROM AUTHOR]

Electronic performance and tracking systems are becoming a standard in many sports to automate data collection and gather more profound insights into performance and game dynamics. In large soccer clubs and federations, the problem is that different electronic performance and tracking systems report different kinematic parameters and performance indicators, which should be the same. Furthermore, a drawback in recent validation studies is the subdivision of speed and acceleration zones in validating the systems, as we show that the kinematic parameters are interdependent. We propose a new method to classify multidimensional validation outputs with a clustering approach. Additionally, we offer a data-driven strategy to reduce errors between distinct systems when data from different electronic performance and tracking systems are compared and show the method's effectiveness with data collected in a validation study. This error reduction strategy can be applied to correct errors when no validation data is available. [ABSTRACT FROM AUTHOR]

Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem to neglect the aspects of security and privacy. This problem occurs due to the pervasive nature of sensor-based HARSs. Sensors are devices with low power and computational capabilities, joining a machine learning application that lies in a dynamic and heterogeneous communication environment, and there is no generalized unified approach to evaluate their security/privacy, but rather only individual solutions. In this work, we studied HARSs in particular and tried to extend existing techniques for these systems considering the security/privacy of all participating components. Initially, in this work, we present the architecture of a real-life medical IoT application and the data flow across the participating entities. Then, we briefly review security and privacy issues and present possible vulnerabilities of each system layer. We introduce an architecture over the communication layer that offers mutual authentication, solving many security and privacy issues, particularly the man-in-the-middle attack (MitM). Relying on the proposed solutions, we manage to prevent unauthorized access to critical information by providing a trustworthy application. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common neurodegenerative disorder, with increasing numbers of affected patients. Many patients lack adequate care due to insufficient specialist neurologists/geriatricians, and older patients experience difficulties traveling far distances to reach their treating physicians. A new option for these obstacles would be telemedicine and wearables. During the last decade, the development of wearable sensors has allowed for the continuous monitoring of bradykinesia and dyskinesia. Meanwhile, other systems can also detect tremors, freezing of gait, and gait problems. The most recently developed systems cover both sides of the body and include smartphone apps where the patients have to register their medication intake and well-being. In turn, the physicians receive advice on changing the patient's medication and recommendations for additional supportive therapies such as physiotherapy. The use of smartphone apps may also be adapted to detect PD symptoms such as bradykinesia, tremor, voice abnormalities, or changes in facial expression. Such tools can be used for the general population to detect PD early or for known PD patients to detect deterioration. It is noteworthy that most PD patients can use these digital tools. In modern times, wearable sensors and telemedicine open a new window of opportunity for patients with PD that are easy to use and accessible to most of the population. [ABSTRACT FROM AUTHOR]

Recently, human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems. Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs, physical human action recognition (HAR) through body sensors gives useful information about the lifestyle and functionality of an individual. This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based Behavior Recognition (ITSOMLBR) technique using body sensor data. The presented ITSOML-BR technique collects data from different body sensors namely electrocardiography (ECG), accelerometer, and magnetometer. In addition, the ITSOML-BR technique extract features like variance, mean, skewness, and standard deviation. Moreover, the presented ITSOML-BR technique executes a micro neural network (MNN) which can be employed for long term healthcare monitoring and classification. Furthermore, the parameters related to the MNN model are optimally selected via the ITSO algorithm. The experimental result analysis of the ITSOML-BR technique is tested on the MHEALTH dataset. The comprehensive comparison study reported a higher result for the ITSOMLBR approach over other existing approaches with maximum accuracy of 99.60%. [ABSTRACT FROM AUTHOR]

The increasing ubiquity of health sensing technology holds promise to enable patients and health care providers to make more informed decisions based on continuously-captured data. The use of sensor-captured patient-generated data (sPGD) has been gaining greater prominence in the assessment of physical health, but we have little understanding of the role that sPGD can play in mental health. To better understand the use of sPGD in mental health, we interviewed care providers in an intensive treatment program (ITP) for veterans with post-traumatic stress disorder. In this program, patients were given Fitbits for their own voluntary use. Providers identified a number of potential benefits from patients' Fitbit use, such as patient empowerment and opportunities to reinforce therapeutic progress through collaborative data review and interpretation. However, despite the promise of sensor data as offering an "objective" view into patients' health behavior and symptoms, the relationships between sPGD and therapeutic progress are often ambiguous. Given substantial subjectivity involved in interpreting data from commercial wearables in the context of mental health treatment, providers emphasized potential risks to their patients and were uncertain how to adjust their practice to effectively guide collaborative use of the FitBit and its sPGD. We discuss the implications of these findings for designing systems to leverage sPGD in mental health care.

The monitoring of physiological parameters using wearable (bio-) sensors of military personnel is a progressing process within the military environment. It sets high demands on such devices, in order to support healthcare and performance of the personnel. To get an ovierview of the current status of the use, the evaluation and the implementation in the military, in May 2021, the Multinational Medical Coordination Centre / European Medical Command has organized an expert workshop about ‘Biosensors Supporting Healthcare in Missions’. Three thematic clusters were addressed: ‘Human Performance and Readiness’; ‘Health and Medical Management Applications’ and ‘Ethical and Legal Aspects of the Use of Biosensors’.

Background: Pitch count recommendations are used to reduce injury risk in youth baseball pitchers and are based chiefly on expert opinion, with limited scientific support. Furthermore, they only account for pitches thrown against a hitter and do not include the total number of throws on the day a player pitched. Currently, counts are recorded manually. Purpose: To provide a method using a wearable sensor to quantify total throws per game that is compliant with Little League Baseball rules and regulations. Study Design: Descriptive laboratory study. Methods: Eleven male baseball players (age, 10-11 years) from an 11U (players 11 years and younger) competitive travel team were evaluated over a single summer season. An inertial sensor was placed above the midhumerus of the throwing arm and was worn during baseball games across the season. A throw identification algorithm capturing all throws and reporting linear acceleration and peak linear acceleration was used to quantify throwing intensity. Pitching charts were collected and used to verify actual pitches thrown against a hitter in a game versus all other throws identified. Results: A total of 2748 pitches and 13,429 throws were captured. On the day a player pitched, he averaged 36 ± 18 pitches (23%) and 158 ± 106 total throws (pitches in game as well as all warm-up pitches and other throws during game). In comparison, on a day a player did not pitch, he averaged 119 ± 102 throws. Across all pitchers, 32% of all throws were low intensity, 54% were medium intensity, and 15% were high intensity. The player with one of the highest percentages of high-intensity throws did not pitch as their primary position, while the 2 players who pitched most often had the lowest percentages. Conclusion: Total throw count can be successfully quantified using a single inertial sensor. Total throws tended to be higher on days a player pitched compared with regular game days without pitching. Clinical Relevance: This study provides a fast, feasible, and reliable method to obtain pitch and throw counts so that more rigorous research on contributing factors to arm injury in the youth athlete can be achieved. [ABSTRACT FROM AUTHOR]

This study characterized the absolute pressure measurement error and reliability of a new fully integrated (Kinetyx, SI) plantar-pressure measurement system (PPMS) versus an industry-standard PPMS (F-Scan, Tekscan) during an established benchtop testing protocol as well as via a research-grade, instrumented treadmill (Bertec) during a running protocol. Benchtop testing results showed that both SI and F-Scan had strong positive linearity (Pearson's correlation coefficient, PCC = 0.86–0.97, PCC = 0.87–0.92; RMSE = 15.96 ± 9.49) and mean root mean squared error RMSE (9.17 ± 2.02) compared to the F-Scan on a progressive loading step test. The SI and F-Scan had comparable results for linearity and hysteresis on a sinusoidal loading test (PCC = 0.92–0.99; 5.04 ± 1.41; PCC = 0.94–0.99; 6.15 ± 1.39, respectively). SI had less mean RMSE (6.19 ± 1.38) than the F-Scan (8.66 ±2.31) on the sinusoidal test and less absolute error (4.08 ± 3.26) than the F-Scan (16.38 ± 12.43) on a static test. Both the SI and F-Scan had near-perfect between-day reliability interclass correlation coefficient, ICC = 0.97–1.00) to the F-Scan (ICC = 0.96–1.00). During running, the SI pressure output had a near-perfect linearity and low RMSE compared to the force measurement from the Bertec treadmill. However, the SI pressure output had a mean hysteresis of 7.67% with a 28.47% maximum hysteresis, which may have implications for the accurate quantification of kinetic gait measures during running. [ABSTRACT FROM AUTHOR]

Background: Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea. Objective: The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard. Methods: We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device's efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant's data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods. Results: The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of -0.25 and 0.33. The RR bias was 0.018, and the LoAs were -1.89 and 1.89. Conclusions: Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals. [ABSTRACT FROM AUTHOR]

Background: With anxiety a growing issue and barriers to accessing support services, there is a need for innovative solutions to provide early intervention. In-the-moment interventions support individuals to recognize early signs of distress and use coping mechanisms to prevent or manage this distress. There is potential for wearable sensors linked to an individual's mobile phone to provide in-the-moment support tailored to individual needs and physiological responses. Objective: The aim of this scoping review is to examine the role of sensors in detecting the physiological signs of anxiety to initiate and direct interventions for its management. Methods: Relevant studies were identified through searches conducted in Embase, MEDLINE, APA PsycINFO, ProQuest, and Scopus. Studies were identified if they were conducted with people with stress or anxiety or at risk of anxiety and included a wearable sensor providing real-time data for in-the-moment management of anxiety. Results: Of the 1087 studies identified, 11 studies were included in the review, including 5 randomized controlled trials and 6 pilot or pretesting studies. The results showed that most studies successfully demonstrated improvements in their target variables. This included overall anxiety and stress levels, and the implementation of in-the-moment stress and anxiety management techniques such as diaphragmatic breathing. There was wide variation in the types of sensors used, physiological measures, and sensor-linked interventions. Conclusions: This review indicates that sensors are potentially a useful tool in detecting anxiety and facilitating the implementation of a known control mechanism to reduce anxiety and improve mood, but further work is needed to understand the acceptability and effectiveness of this type of intervention. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them. [ABSTRACT FROM AUTHOR]

With the demand for miniaturized devices, the development of 3-D-printing (3-DP) techniques to create sensors has emerged in the recent decade. 3-DP is not the only technique used to construct 3-D sensors (3-DS) and 3-D structures created from composites but also used for this purpose. This review reveals the most used technologies for the creation of electrochemical sensors, where 3-D electrodes (3-DE) are involved: 3-DP, plating, and composite formation. Fabrication and applications reported in the years 2020 and 2021 are summarized, and clairvoyance for most perspective directions is given. [ABSTRACT FROM AUTHOR]

Breath rate is an important vital sign for monitoring the well-being and detecting underlying illnesses related to the respiratory system. The spirometer is widely used for evaluating the health of the respiratory system by measuring lung capacity and respiration rate (RR). Although this test yields accurate results, however, it is significantly inconvenient and invasive, as the test subject must breathe through a tube inserted in the mouth. Therefore, this device is not suitable for continuous monitoring of respiration. In this article, a novel contactless wearable breath rate measurement system is presented. The proposed sensor is based on a spiral resonator (SR) tag printed on a thin flexible textile substrate. The sensor exploits the body expansion and contraction during respiration to provide an estimate of the breathing frequency. This movement is measured by a microstrip probing loop that acts as the antenna of the reader. The reader operates at one frequency; the resonant frequency of the tag, and the sensing principle relies on the near-field coupling between the probe and the resonating tag at this frequency. The proposed sensor is characterized with its simple and lightweight design, can be easily integrated in garments, and its capability of providing continuous monitoring of the breathing rate. The proposed sensor has been tested through experimental measurements, and the results matched measurements from a reference ground-truth breath rate sensor based on a nasal cannula equipped with a thermistor. The solution proposed in this work provides a novel contactless technique for RR estimation. [ABSTRACT FROM AUTHOR]

Recently, artificial intelligence (AI) including machine learning (ML) and deep learning (DL) models has been commonly employed for the automated disease diagnosis process. AI in biological and biomedical imaging is an emerging area and will be a future trend in the field. At the same time, biomedical images can be used for the classification of Rheumatoid arthritis (RA) diseases. RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects. The disease frequently becomes progressive and decreases physical function, causing articular damage, suffering, and fatigue. After a time, RA causes harm to the cartilage of the joints and bones, weakens the tendons and joints, and finally causes joint destruction. Sensors (thermal infrared camera sensor, accelerometers and wearable sensors) are more commonly employed to collect data for RA. This study develops an Automated Rheumatoid Arthritis Classification using an Arithmetic Optimization Algorithm with Deep Learning (ARAC-AOADL) model. The goal of the presented ARAC-AOADL technique lies in the classification of health disorders depending upon RA and orthopaedics. Primarily, the presented ARAC-AOADL technique pre-processes the input images by median filtering (MF) technique. Then, the ARAC-AOADL technique uses AOA with an enhanced capsule network (ECN) model to produce feature vectors. For RA classification, the ARAC-AOADL technique uses a multi-kernel extreme learning machine (MKELM) model. The experimental result analysis of the ARAC-AOADL technique on a benchmark dataset reported a maximum accuracy of 98.57%. Therefore, the ARAC-AOADL technique can be employed for accurate and timely RA classification. [ABSTRACT FROM AUTHOR]

Concerning occupational safety, the aim of ergonomics as a scientific discipline is to study and adjust working conditions, worker equipment, and work processes from a psychological, physiological, and anatomical aspect instead of adapting the worker to the needs of the job. This paper will discuss and analyze the potential of the garment-embedded body posture tracking sensor and its usage as standard working equipment, which is meant to help correct improper and high-risk upper body positions during prolonged and static work activities. The analysis evaluation cross-reference is based on the Rapid Upper Limb Assessment ergonomics risk assessment tool. Signals generated by the wearable are meant to help the wearer and observer promptly-continuously detect and correct bad posture. The results show a positive progression of workers' body posture to reduce the ergonomic risks this research covers. It can be concluded that wearable technology and sensors would significantly contribute to the observer as the evaluation tool and the wearer to spot the risk factors promptly and self-correct them independently. This feature would help workers learn and improve the correct habits of correcting ergonomically incorrect body postures when performing work tasks. [ABSTRACT FROM AUTHOR]

Internet is a dynamic growth that is always evolving into several new types of technology and programming, making it impossible for anyone to escape. The innovation named Internet of Things expands the capacity of humanity and PCs to control billions of available substances like actuators, sensors, and different administrations. The fate of the Internet will comprise different associated gadgets that will additionally expand the lines of the world with actual elements and virtual parts [1]. This profoundly connected worldwide organizational building known as the IoT(Internet of Things) will enhance everybody's day- to-day life, increment business efficiency, and further develop government proficiency. Be that as it may, this new Internet- based reality brings with it a variety of new security and protection challenges[2]. The primary goal of this paper is to give an outline of the IoT, models, fundamental developments, and their purposes in our normal daily practice. Likewise, it portrays a six-layered design of IoT and brings up the connected key difficulties. [ABSTRACT FROM AUTHOR]

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS. [ABSTRACT FROM AUTHOR]

There is widespread and growing use of inertial measurement technology for human motion analysis in biomechanics and clinical research. Due to advancements in sensor miniaturization, inertial measurement units can be used to obtain a description of human body and joint kinematics both inside and outside the laboratory. While algorithms for data processing continue to improve, a lack of standard reporting guidelines compromises the interpretation and reproducibility of results, which hinders advances in research and development of measurement and intervention tools. To address this need, the International Society of Biomechanics approved our proposal to develop recommendations on the use of inertial measurement units for joint kinematics analysis. A collaborative effort that incorporated feedback from the biomechanics community has produced recommendations in five categories: sensor characteristics and calibration, experimental protocol, definition of a kinematic model and subject-specific calibration, analysis of joint kinematics, and quality assessment. We have avoided an overly prescriptive set of recommendations for algorithms and protocols, and instead offer reporting guidelines to facilitate reproducibility and comparability across studies. In addition to a conceptual framework and reporting guidelines, we provide a checklist to guide the design and review of research using inertial measurement units for joint kinematics. [ABSTRACT FROM AUTHOR]

Wearable technologies are small electronic and mobile devices with wireless communication capabilities that can be worn on the body as a part of devices, accessories or clothes. Sensors incorporated within wearable devices enable the collection of a broad spectrum of data that can be processed and analysed by artificial intelligence (AI) systems. In this narrative review, we performed a literature search of the MEDLINE, Embase and Scopus databases. We included any original studies that used sensors to collect data for a sporting event and subsequently used an AI-based system to process the data with diagnostic, treatment or monitoring intents. The included studies show the use of AI in various sports including basketball, baseball and motor racing to improve athletic performance. We classified the studies according to the stage of an event, including pre-event training to guide performance and predict the possibility of injuries; during events to optimise performance and inform strategies; and in diagnosing injuries after an event. Based on the included studies, AI techniques to process data from sensors can detect patterns in physiological variables as well as positional and kinematic data to inform how athletes can improve their performance. Although AI has promising applications in sports medicine, there are several challenges that can hinder their adoption. We have also identified avenues for future work that can provide solutions to overcome these challenges. [ABSTRACT FROM AUTHOR]

The article provide a brief overview of the critical principles of inertial measurement unit (IMU) operation for performing kinematic assessments. It reports resources and recommendations helpful in using IMUs to support wearable inertial sensors for objective kinematic assessment in the workplace to an audience that may be less familiar with the technology.

Sensory processing issues are one of the most common issues observed in autism spectrum disorders (ASD). Technologies that could address the issue serve a more and more important role in interventions for ASD individuals nowadays. In this study, a sensory management recommendation system was developed and tested to help ASD children deal with atypical sensory responses in class. The system employed sensor fusion and machine learning techniques to identify distractions, anxious situations, and the potential causes of these in the surroundings. Another novelty of the system included a sensory management strategy making a module based on fuzzy logic, which generated alerts to inform teachers and caregivers about children's states and risky environmental factors. Sensory management strategies were recommended to help improve children's attention or calm children down. The evaluation results suggested that the use of the system had a positive impact on children's performance and its design was user-friendly. The sensory management recommendation system could work as an intelligent companion for ASD children that helps with their in-class performance by recommending management strategies in relation to the real-time information about the children's environment. [ABSTRACT FROM AUTHOR]

Clothing-based respiratory monitoring can provide real-time non-invasive respiratory monitoring services for subjects, and can be used as a reference for daily health observation. In this study, an optical fiber sensing Section woven into plain fabric is produced by a hand-weaving machine, and the produced sensing fabric is then made into a respiratory monitoring garment. This paper explored the luminescence and coupling principle of notched optical fiber through theoretical analysis and simulation, and demonstrated the feasibility of making it into respiratory sensing fabric. We used this structure to make a fabric and then sew the fabric with the yoke to make a smart garment. In testing, we obtained the static respiratory waveform and the respiratory rate (R-R) during daily common movements, which were also compared with two types of commercial sensors. The results showed that the smart garment based on optical fiber sensing fabric maintains good consistency with the monitoring waveform of commercial sensors and good R-R obtained in static motion (error not more than 2 rpm). Monitoring tests showed that the garment has high monitoring accuracy and motion monitoring stability. Moreover, it was also very comfortable and can be used as daily respiratory monitoring equipment. [ABSTRACT FROM AUTHOR]

The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. However, current algorithms lack flexibility and require large training datasets with tedious manual labelling of data. The current study tests the validity of a novel machine learning algorithm for automated gait partitioning of laboratory-based and sensor-based gait data. The developed artificial intelligence tool was used in patients with a central neurological lesion and severe gait impairments. To build the novel algorithm, 2% and 3% of the entire dataset (567 and 368 steps in total, respectively) were required for assessments with laboratory equipment and inertial measurement units. The mean errors of machine learning-based gait partitions were 0.021 s for the laboratory-based datasets and 0.034 s for the sensor-based datasets. Combining reinforcement learning with a deep neural network allows significant reduction in the size of the training datasets to <5%. The low number of required training data provides end-users with a high degree of flexibility. Non-experts can easily adjust the developed algorithm and modify the training library depending on the measurement system and clinical population. [ABSTRACT FROM AUTHOR]

Using a chest-based inertial wearable sensor, we examined the quantitative gait patterns associated with lumbar disc herniation (LDH), lumbar spinal stenosis (LSS), and chronic mechanical low back pain (CMLBP). 'Pathological gait signatures' were reported as statistically significant group difference (%) from the 'normative' gait values of an age-matched control population. A sample of patients presenting to the Prince of Wales Private Hospital (Sydney, Australia) with primary diagnoses of LDH, LSS, or CMLBP were recruited. Spatial, temporal, asymmetry, and variability metrics were compared with age-matched (±2 years) control participants recruited from the community. Participants were fitted at the sternal angle with an inertial measurement unit, MetaMotionC, and walked unobserved (at a self-selected pace) for 120 m along an obstacle-free, carpeted hospital corridor. LDH, CMLBP, and LSS groups had unique pathological signatures of gait impairment. The LDH group (n = 33) had marked asymmetry in terms of step length, step time, stance, and single-support asymmetry. The LDH group also involved gait variability with increased step length variation. However, distinguishing the CMLBP group (n = 33) was gait variability in terms increased single-support time variation. The gait of participants with LSS (n = 22) was both asymmetric and variable in step length. Wearable sensor–based accelerometry was found to be capable of detecting the gait abnormalities present in patients with LDH, LSS, and CMLBP, when compared to age-matched controls. Objective and quantitative patterns of gait deterioration uniquely varied between these subtypes of lumbar spine disease. With further testing and validation, gait signatures may aid clinical identification of gait-altering pathologies. [ABSTRACT FROM AUTHOR]