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Manufacturing workers face prolonged strenuous physical activities, impacting both financial aspects and their health due to work-related fatigue. Continuously monitoring physical fatigue and providing meaningful feedback is crucial to mitigating human and monetary losses in manufacturing workplaces. This study introduces a novel application of multimodal wearable sensors and machine learning techniques to quantify physical fatigue and tackle the challenges of real-time monitoring on the factory floor. Unlike past studies that view fatigue as a dichotomous variable, our central formulation revolves around the ability to predict multilevel fatigue, providing a more nuanced understanding of the subjects physical state. Our multimodal sensing framework is designed for continuous monitoring of vital signs, including heart rate, heart rate variability, skin temperature, and more, as well as locomotive signs by employing inertial motion units strategically placed at six locations on the upper body. This comprehensive sensor placement allows us to capture detailed data from both the torso and arms, surpassing the capabilities of single-point data collection methods. We developed an innovative asymmetric loss function for our machine learning model, which enhances prediction accuracy for numerical fatigue levels and supports real-time inference. We collected data on 43 subjects following an authentic manufacturing protocol and logged their self-reported fatigue. Based on the analysis, we provide insights into our multilevel fatigue monitoring system and discuss results from an in-the-wild evaluation of actual operators on the factory floor. This study demonstrates our systems practical applicability and contributes a valuable open-access database for future research.

Introduction: Greater side-to-side asymmetry can indicate impaired skill, reduced power production, and an increased risk of injury. Bilateral differences highlight the presence of asymmetries that should be assessed to understand their impact on both injury risk and performance enhancement. Objective: This study aimed to assessment muscle activation and bilateral asymmetry in major trunk and shoulder muscles during a two-armed kettlebell swing exercise. Methods: Twenty-seven participants (age: 24.2 ± 2.6 years; body mass: 82.9 ± 7.7 kg; height: 176.9 ± 7.0 cm) were included in the study. Electromyographic (EMG) data were collected bilaterally from twelve muscles (six muscles per side: anterior deltoid [AD], posterior deltoid [PD], erector spinae longissimus [ESL], erector spinae iliocostalis [ESI], external oblique [EO], and rectus abdominis [RA]). Results: Results indicated that asymmetry indices for the AD, ESL, ESI, and RA muscles during the upward propulsion phase fell within the determined threshold of 15%. However, the asymmetry indices for the PD and EO muscles exceeded this threshold by 3.36% and 2.62%, respectively. The findings suggest that trunk muscle asymmetries during the kettlebell swing are generally less pronounced than those of the shoulder muscles, particularly during the float phase. Conclusion: These results provide valuable insights into bilateral muscle asymmetry during a two-armed kettlebell swing, which can inform the development of targeted training programs. The methods and findings of this study may further contribute to understanding the effects of muscle balance, symmetry, and injury mechanisms in dynamic movements. [ABSTRACT FROM AUTHOR]

The evolving landscape of modern manufacturing is affected by a confluence of social challenges arising from demographic shifts and erratic market demands. The emergence of Industry 5.0 revolutionized practices, with a keen focus on harnessing the potential of the Internet of Things to digitize the workforce embracing a human-centric approach. In this research, an original digital architecture integrates sensors into manufacturing environments to evaluate the well-being of assembly operators. The core objective is to capture human-process interactions, empowering production managers to optimize assembly environments from the ergonomic perspective. Three key enabling technologies are employed: radio frequency identification smart gloves, motion capture cameras, and superficial electromyography sensors. This physical layer detects and analyzes assembly tasks, operator movements, and muscular activities. Computational algorithms mine these data streams to assess the Ergonomic Assembly Worksheet, automatically. In detail, the investigated sections of this ergonomic index are basic postures, action forces, and manual material handling. Furthermore, a supplementary set of Key Risk Indicators supports production managers in evaluating the physical resilience of the assembly systems. These operator-specific metrics provide strategic information to trigger workstation redesign, task rebalancing, and other corrective actions for enhancing the safety of whichever human-centric assembly process. Finally, an experimental campaign in a controlled industrial environment tests the architecture's effectiveness and potential to reduce the risks and enhance workforce health. [ABSTRACT FROM AUTHOR]

Textile-based triboelectric nanogenerators (TENGs) hold considerable promise as sustainable power sources for wearable electronics, yet their seamless integration into everyday clothing remains a challenge. This paper introduces a scalable woven TENG structure that utilizes core−shell yarns composed of commercially available wool and polyester with an inner copper electrode. The device achieves notable performance with an output voltage of 18.5 V, a current of 3.7 µA, and a power density of 51 mW/m2. The novel multilayer design combines hybrid single-electrode and contact-separation approaches, significantly enhancing triboelectric performance and the device's durability. The multilayer functionality amplifies energy conversion efficiency by increasing the contact area and optimizing charge accumulation through multiple interacting layers. This advanced textile TENG is comfortable, flexible, and aesthetically pleasing while being compatible with large-scale textile manufacturing processes. It demonstrates excellent washability, durability under diverse weather conditions, and resilience to repeated mechanical loading. Highly responsive to a broad range of forces, from gentle taps to strong impacts, this TENG is versatile for body motion monitoring and wearable applications. It represents a significant advancement in creating practical, efficient, and durable energy-harvesting textiles, with promising applications in garment production for precise motion detection, such as for professional athletes and individuals requiring specialized monitoring. [ABSTRACT FROM AUTHOR]

Wearable sensing devices can reliably track players' mobility, revolutionizing sports training. However, current sensing electronics face challenges due to their complex structures, battery dependence, and unreliable sensing signals. Here, a tennis training system is demonstrated using machine learning based on elastic self‐powered sensing yarns. By employing a simple and effective strategy, piezoelectric nanofibers and triboelectric materials are integrated into a single yarn, enabling the simultaneous translation of both triboelectric and piezoelectric signals. Additionally, these yarns exhibit outstanding processability, allowing them to be machine‐knitted into self‐powered sensing fabrics. Due to their great sensitivity, these sensing yarns and fabrics may detect human movement with great precision. Machine learning algorithms can classify and interpret these signals to recognize various human motions. The developed tennis training system aims to maximize its benefits and provide comprehensive training for both players and coaches. This work enhances the applicability of self‐powered sensing systems in smart sports monitoring and training, advancing the field of intelligent sports training. [ABSTRACT FROM AUTHOR]

With the increasing awareness of health, using wearable sensors to monitor individual activities and accurately estimate energy expenditure has become a current research focus. However, existing research encounters challenges including low estimation accuracy, a deficiency of frequency domain features, and difficulty in integrating time domain and frequency domain features. To address these issues, we propose an innovative framework called the Dual-Stream Fusion Network (DSFN). This framework combines the Time Domain Encoding (TDE) module, the Frequency Domain Hierarchical-Split Encoding (FDHSE) module, and a Two-Stage Feature Fusion (TSF) module. Specifically, the temporal stream of the framework employs the TDE module to capture deep temporal features that reflect the complex dynamic variations in time-series data. The frequency domain stream introduces the FDHSE module, which extracts frequency domain features using a multi-level, multi-scale approach, ensuring a comprehensive and diverse representation of frequency information. Through this dual-stream architecture, our model effectively learns both time and frequency domain features, addressing the limitations of frequency domain features observed in prior studies. Additionally, we propose the TSF module to fully integrate time and frequency domain features, effectively overcoming the challenge of fusing these two types of features. We conducted experiments on two public datasets, namely the GOTOV dataset (elderly people) and the JSI dataset (young people). Experimental results demonstrate that our method achieves excellent performance across different age groups. Compared to the baseline models, the proposed DSFN significantly improves the accuracy of human energy expenditure estimation. [ABSTRACT FROM AUTHOR]

Background: Regular moderate–vigorous intensity exercise is recommended for adults as it can improve longevity and reduce health risks associated with a sedentary lifestyle. However, there are barriers to achieving intense exercise that may be addressed using virtual reality (VR) as a tool to promote exercise intensity and adherence, particularly through visual feedback and competition. The purpose of this work is to compare visual feedback and competition within fully immersive VR to enhance exercise intensity and user experience of exercise for young adults; and to describe and compare visual attention during each of the conditions. Methods: Young adults (21–34 years old) bicycled in three 5 min VR conditions (visual feedback, self-competition, and competition against others). Exercise intensity (cycling cadence and % of maximum heart rate) and visual attention (derived from a wearable eye tracking sensor) were measured continuously. User experience was measured by an intrinsic motivation questionnaire, perceived effort, and participant preference. A repeated-measures ANOVA with paired t-test post hoc tests was conducted to detect differences between conditions. Results: Participants exercised at a higher intensity and had higher intrinsic motivation in the two competitive conditions compared to visual feedback. Further, participants preferred the competitive conditions and only reached a vigorous exercise intensity during self-competition. Visual exploration was higher in visual feedback compared to self-competition. Conclusions: For young adults bicycling in VR, competition promoted higher exercise intensity and motivation compared to visual feedback. [ABSTRACT FROM AUTHOR]

Over the past two decades, deep eutectic solvents (DESs) have captured significant attention as an emergent class of solvents that have unique properties and applications in differing fields of chemistry. One area where DES systems find utility is the design of polymeric gels, often referred to as "eutectogels," which can be prepared either using a DES to replace a traditional solvent, or where monomers form part of the DES themselves. Due to the extensive network of intramolecular interactions (e.g., hydrogen bonding) and ionic species that exist in DES systems, polymeric eutectogels often possess appealing material properties—high adhesive strength, tuneable viscosity, rapid polymerization kinetics, good conductivity, as well as high strength and flexibility. In addition, non‐covalent crosslinking approaches are possible due to the inherent interactions that exist in these materials. This review considers several key applications of polymeric eutectogels, including organic electronics, wearable sensor technologies, 3D printing resins, adhesives, and a range of various biomedical applications. The design, synthesis, and properties of these eutectogels are discussed, in addition to the advantages of this synthetic approach in comparison to traditional gel design. Perspectives on the future directions of this field are also highlighted. [ABSTRACT FROM AUTHOR]

Athletes' performance evaluation is a critical step for the assessment of the skills and the quality of training of the athletes. This is even more important in team sports such as football, in which different roles, and hence different skills, are required. In light of this, the presented work aims at forecasting above team-average performance of football players by using supervised machine learning algorithms. Such algorithms were trained and tested on four biometric parameters as input and seven performance indicators as labels. The algorithms have been optimized using three optimization techniques: the gridsearch, and two versions of the whale-optimization algorithm, the standard one and another, proposed by us, in which Euclidean distance is used. The analyses were conducted by dividing the players by their role: the strikers, the midfielders and the defenders, to take into account the different skills required for each task. The obtained results show that the Random Forest, trained with specific combinations of biometric parameters is capable of predicting the selected performance indicators with an accuracy higher than 90%, for all the considered players' roles and that the proposed optimization technique outperforms the existing ones. In light of these results, the proposed method could be used profitably by football teams' staff to monitor their players, offering the possibility to take tactical decisions, design customized training sessions and orient market choices. [ABSTRACT FROM AUTHOR]

Motion artifacts can be a significant noise source in biomagnetic measurements when magnetic sensors are not separated from the signal source. In ambient environments, motion artifacts can be up to ten times stronger than the desired signals, varying with environmental conditions. This study evaluates the variability of these artifacts and the effectiveness of a gradiometer in reducing them in such settings. To achieve these objectives, we first measured the single channel output in varying magnetic field conditions to observe the effect of homogeneous and gradient background fields. Our analysis revealed that the variability in motion artifact within an ambient environment is primarily influenced by the gradient magnetic field rather than the homogeneous one. Subsequently, we configured a gradiometer in parallel and vertical alignment with the direction of vibration (X-axis). Our findings indicated that in a gradient background magnetic field ranging from 1 nT/mm to 10 nT/mm, the single-channel sensor output exhibited a change of 164.97 pT per mm unit increase, while the gradiometer output showed a change of only 0.75 pT/mm within the same range. Upon repositioning the gradiometer vertically (Y direction), perpendicular to the direction of vibration, the singlechannel output slope increased to 196.85 pT, whereas the gradiometer output only increased by 1.06 pT/mm for the same range. Our findings highlight the influence of ambient environments on motion artifacts and demonstrate the potential of gradiometers to mitigate these effects. In the future, we plan to record biomagnetic signals both inside and outside the shielded room to compare the efficacy of different gradiometer designs under varying environmental conditions. [ABSTRACT FROM AUTHOR]

Background: Onasemnogene abeparvovec gene replacement therapy (GT) has changed the prognosis of patients with spinal muscular atrophy (SMA) with variable outcome regarding motor development in symptomatic patients. This pilot study evaluates acceptability, validity and clinical relevance of Inertial Measurement Units (IMU) to monitor spontaneous movement recovery in early onset SMA patients after GT. Methods: Clinical assessments including CHOPINTEND score (the gold standard motor score for infants with SMA) and IMU measurements were performed before (M0) and repeatedly after GT. Inertial data was recorded during a 25-min spontaneous movement task, the child lying on the back, without (10 min) and with a playset (15 min) wearing IMUs. Two commonly used parameters, norm acceleration 95th centile (||A||_95) and counts per minute (||A||_CPM) were computed for each wrist, elbow and foot sensors. Results: 23 SMA-patients were included (mean age at diagnosis 8 months [min 2, max 20], 19 SMA type 1, three type 2 and one presymptomatic) and 104 IMU-measurements were performed, all well accepted by families and 84/104 with a good child participation (evaluated with Brazelton scale). ||A||_95 and ||A||_CPM showed high internal consistency (without versus with a playset) with interclass correlation coefficient for the wrist sensors of 0.88 and 0.85 respectively and for the foot sensors of 0.93 and 0.91 respectively. ||A||_95 and ||A||_CPM were strongly correlated with CHOPINTEND (r for wrist sensors 0.74 and 0.67 respectively and for foot sensors 0.61 and 0.68 respectively, p-values < 0.001). ||A||_95 for the foot, the wrist, the elbow sensors and ||A||_CPM for the foot, the wrist, the elbow sensors increased significantly between baseline and the 12 months follow-up visit (respective p-values: 0.004, < 0.001, < 0.001, 0.006, < 0.001, < 0.001). Conclusion: IMUs were well accepted, consistent, concurrently valid, responsive and associated with unaided sitting acquisition especially for the elbow sensors. This study is the first reporting a large set of inertial sensor derived data after GT in SMA patients and paves the way for IMU-based follow-up of SMA patients after treatment. [ABSTRACT FROM AUTHOR]

Pretreatment prognostication, on-treatment monitoring, and early detection of physiological symptoms are considerable challenges in cancer. We describe the feasibility of high-resolution wearable data (steps per day, walking speed) to longitudinally profile physiological trajectories extracted from Apple Health data in three patients with lung cancer from diagnosis through cancer treatment after obtaining informed consent. We used descriptive statistics to describe our approach of building longitudinal physiological profiles. The wearable data monitoring period ranged from 58 to 135 weeks, with between 34,319 and 103,535 distinct digital physiological measures collected during this period--the equivalent to 41 measures per day/patient. Longitudinal profiling revealed that wearable data accurately captured physiological changes linked with clinical events such as surgery and hospitalizations as well as initiation (and cessation) of systemic cancer treatment in all three patients. These findings suggest that wearable devices could play a critical role in the management of lung cancer, although larger studies are needed to confirm these preliminary observations and validate their generalizability. Wearable devices hold significant promise for the development of personalized "digital biomarkers," which may enhance risk stratification and management in oncology. [ABSTRACT FROM AUTHOR]

In this work, a flexible textile-based capacitive respiratory sensor, based on a capacitive sensor structure, that does not require direct skin contact is designed, optimised, and evaluated using both computational modelling and empirical measurements. In the computational study, the geometry of the sensor was examined. This analysis involved observing the capacitance and frequency variations using a cylindrical model that mimicked the human body. Four designs were selected which were then manufactured by screen printing multiple functional layers on top of a polyester/cotton fabric. The printed sensors were characterised to detect the performance against phantoms and impacts from artefacts, normally present whilst wearing the device. A sensor that has an electrode ratio of 1:3:1 (sensor, reflector, and ground) was shown to be the most sensitive design, as it exhibits the highest sensitivity of 6.2% frequency change when exposed to phantoms. To ensure the replicability of the sensors, several batches of identical sensors were developed and tested using the same physical parameters, which resulted in the same percentage frequency change. The sensor was further tested on volunteers, showing that the sensor measures respiration with 98.68% accuracy compared to manual breath counting. [ABSTRACT FROM AUTHOR]

In the swiftly progressing landscape of wearable electronics and the Internet of Things (IoTs), there is a burgeoning demand for devices that are lightweight, cost‐effective, and self‐powered. In this study, a self‐powered bidirectional knee joint motion monitoring system is introduced, leveraging a dual ratchet sensing (DRS) system fabricated using 3D printing technology. This approach offers substantial economic and portability benefits. The DRS system is engineered to harness the negative work generated from knee joint movements to power commercial electronic devices, obviating the need for additional metabolic energy from the human body. By synergizing the DRS with virtual reality technology, it becomes feasible to monitor knee joint movements in real‐time with remarkable accuracy, presenting a novel avenue for the integration of digital twin technology. Through the amalgamation of convolutional neural network machine learning algorithms with Bayesian optimization techniques, the DRS system can discern up to 97% of knee joint movements, paving the way for innovative applications in smart rehabilitation and healthcare. [ABSTRACT FROM AUTHOR]

Falls are a significant health concern leading to increased morbidity and healthcare costs, especially for the elderly. Early and accurate detection of fall events is critical for timely intervention and preventing severe complications. This study presents a novel approach to triaxial accelerometer signals by employing data-adaptive Gaussian average filtering (DAGAF) decomposition in conjunction with machine learning techniques for fall detection. The triaxial accelerometer signals from the FallAllD dataset were decomposed into intrinsic mode functions (IMFs) and a residual component, from which feature vectors were extracted to train support vector machine (SVM) and k-nearest neighbor (kNN) classifiers. Experimental results demonstrate that the combination of the first and the third IMFs with the residual component yields the highest classification accuracy of 96.34%, with SVM outperforming kNN across all performance metrics. This approach significantly improves fall detection accuracy compared to using raw accelerometer signals, highlighting its potential in enhancing wearable fall detection systems. The proposed DAGAF decomposition method not only enhances feature extraction but also provides a promising advancement in the field, suggesting its potential to increase the reliability and accuracy of fall detection in practical applications. [ABSTRACT FROM AUTHOR]

In this paper, the concept of symmetry is used to design the efficient inference of a fall-detection algorithm for elderly people on embedded processors—that is, there is a symmetric relation between the model's structure and the memory footprint on the embedded processor. Artificial intelligence (AI) and, more particularly, Long Short-Term Memory (LSTM) neural networks are commonly used in the detection of falls in the elderly population based on acceleration measures. Nevertheless, embedded systems that may be utilized on wearable or wireless sensor networks have a hurdle due to the customarily massive dimensions of those networks. Because of this, the algorithms' most popular implementation relies on edge or cloud computing, which raises privacy concerns and presents challenges since a lot of data need to be sent via a communication channel. The current work proposes a memory occupancy model for LSTM-type networks to pave the way to more efficient embedded implementations. Also, it offers a sensitivity analysis of the network hyper-parameters through a grid search procedure to refine the LSTM topology network under scrutiny. Lastly, it proposes a new methodology that acts over the quantization granularity for the embedded AI implementation on wearable devices. The extensive simulation results demonstrate the effectiveness and feasibility of the proposed methodology. For the embedded implementation of the LSTM for the fall-detection problem on a wearable platform, one can see that an STM8L low-power processor could support a 40-hidden-cell LSTM network with an accuracy of 96.52 % . [ABSTRACT FROM AUTHOR]

Fall detection in daily activities hinges on both feature selection and algorithm choice. This study delves into their intricate interplay using the Sisfall dataset, testing 10 machine learning algorithms on 26 features encompassing diverse falls and age groups. Individual feature analysis yields key insights. RFC with the autocorrelation feature outperformed the other classifiers, achieving 97.94% accuracy and 97.51% sensitivity (surpassing F3‐SVM at 96.18% and F17‐LightGBM at 95.79%). The F3‐SVM exhibited exceptional specificity (98.72%) for distinguishing daily activities. Time‐series features employed by SVM achieved a peak accuracy of 98.60% on unseen data, exceeding motion, basic statistical, and frequency domain features. Feature combinations further excel: the Quintuple approach, fusing top‐performing features, reaches 98.69% accuracy, 98.28% sensitivity, and 99.08% specificity with the ETC, demonstrating notable sensitivity owing to its adaptability. This study underscores the crucial interplay of features and algorithms, with the Quintuple‐ETC approach emerging as the most effective. Rigorous hyperparameter tuning strengthens its performance in real‐world fall‐detection applications. Furthermore, the study investigates algorithm transferability, training models on young participants' data and applying them to the elderly—a significant challenge in machine learning. This highlights the importance of understanding the data transfer between age groups in healthcare, aging management, and medical diagnostics. [ABSTRACT FROM AUTHOR]

Background: Remote monitoring systems have the potential to measure symptoms and treatment effects in people with Parkinson's disease (PwP) in the home environment. However, information about user experience and long‐term compliance of such systems in a large group of PwP with relatively severe PD symptoms is lacking. Objective: The aim was to gain insight into user experience and long‐term compliance of a smartwatch (to be worn 24/7) and an online dashboard to report falls and receive feedback of data. Methods: We analyzed the data of the "Bringing Parkinson Care Back Home" study, a 1‐year observational cohort study in 200 PwP with a fall history. User experience, compliance, and reasons for noncompliance were described. Multiple Cox regression models were used to identify determinants of 1‐year compliance. Results: We included 200 PwP (mean age: 69 years, 37% women), of whom 116 (58%) completed the 1‐year study. The main reasons for dropping out of the study were technical problems (61 of 118 reasons). Median wear time of the smartwatch was 17.5 h/day. The online dashboard was used by 77% of participants to report falls. Smartphone possession, shorter disease duration, more severe motor symptoms, and less‐severe freezing and balance problems, but not age and gender, were associated with a higher likelihood of 1‐year compliance. Conclusions: The 1‐year compliance with this specific smartwatch was moderate, and the user experience was generally good, except battery life and data transfer. Future studies can build on these findings by incorporating a smartwatch that is less prone to technical issues. [ABSTRACT FROM AUTHOR]

Wearable gait analysis systems using inertial sensors offer the potential for easy-to-use gait assessment in lab and free-living environments. This can enable objective long-term monitoring and decision making for individuals with gait disabilities. This study explores a novel approach that applies a hidden Markov model-based similarity measure (HMM-SM) to assess changes in gait patterns based on the gyroscope and accelerometer signals from just one or two inertial sensors. Eleven able-bodied individuals were equipped with a system which perturbed gait patterns by manipulating stance-time symmetry. Inertial sensor data were collected from various locations on the lower body to train hidden Markov models. The HMM-SM was evaluated to determine whether it corresponded to changes in gait as individuals deviated from their baseline, and whether it could provide a reliable measure of gait similarity. The HMM-SM showed consistent changes in accordance with stance-time symmetry in the following sensor configurations: pelvis, combined upper leg signals, and combined lower leg signals. Additionally, the HMM-SM demonstrated good reliability for the combined upper leg signals (ICC = 0.803) and lower leg signals (ICC = 0.795). These findings provide preliminary evidence that the HMM-SM could be useful in assessing changes in overall gait patterns. This could enable the development of compact, wearable systems for unsupervised gait assessment, without the requirement to pre-identify and measure a set of gait parameters. [ABSTRACT FROM AUTHOR]

Load monitoring has been identified as a valuable tool for optimizing training planning and minimizing injury risk. This study's aim was divided into two main objectives: (1) to describe the physical demands during official competition through IMU (inertial movement unit) metrics and (2) to investigate the relationship between basketball statistics and these physical demands. Twelve female highly trained basketballers (26.5 ± 5.3 years, 180 ± 7.1 cm, and 73.6 ± 10.3 kg) were monitored during four official games. Our results indicate that games with more frequent possession changes, particularly those driven by steals and turnovers, exhibit higher physical demands. Additionally, longer game durations were associated with longer recovery time while maintaining similar active time and physical load. Players who assume prominent shooting roles face greater conditional demands, such as increased jumps and impacts, even with equal playing time. These findings suggest that IMUs provide valuable insights into high-intensity actions and patterns, indicating a direct association between physical load and player performance in professional female basketball. This study also highlights the potential for professionals to better manage workload and understand player demands using these insights, even in the absence of in-game sensor data. Our research underscores the importance of contextual analysis in sports performance studies, encouraging future investigations into game phases and their specific physical demands. [ABSTRACT FROM AUTHOR]

In recent years, Romania's stomatology private practice sector has seen substantial growth, with many dentists fully committing to building and expanding their own practices, often funded by their personal income. This study aimed to explore how various postures affect the muscle groups of dentists (380), particularly focusing on identifying positions that may jeopardize their musculoskeletal health. A group of dentists effectively participated in this study (10), adhering to their regular work routines while wearing wearable sensors on their backs to monitor posture and activity. The data gathered from these sensors were analyzed using the RULA (rapid upper-limb assessment) and REBA (rapid entire-body assessment) tools. The findings indicated that the head and shoulder movements during dental procedures involved considerable and repetitive angular shifts, which could strain the neck and back muscles and heighten the risk of musculoskeletal problems. Additionally, the standing postures adopted by the dentists were associated with an increased risk of postural issues and greater overall fatigue. Extended periods of trunk and head tilting were also identified as contributing factors to posture-related challenges. [ABSTRACT FROM AUTHOR]

Active life monitoring via chemosensitive sensors could hold promise for enhancing athlete monitoring, training optimization, and performance in athletes. The present work investigates a resistive flex sensor (RFS) in the guise of a chemical sensor. Its carbon 'texture' has shown to be sensitive to CO2, O2, and RH changes; moreover, different bending conditions can modulate its sensitivity and selectivity for these gases and vapors. A three-step feasibility study is presented including: design and fabrication of the electronic read-out and control; calibration of the sensors to CO2, O2 and RH; and a morphological study of the material when interacting with the gas and vapor molecules. The 0.1 mm−1 curvature performs best among the tested configurations. It shows a linear response curve for each gas, the ranges of concentrations are adequate, and the sensitivity is good for all gases. The curvature can be modulated during data acquisition to tailor the sensitivity and selectivity for a specific gas. In particular, good results have been obtained with a curvature of 0.1 mm−1. For O2 in the range of 20–70%, the sensor has a sensitivity of 0.7 mV/%. For CO2 in the range of 4–80%, the sensitivity is 3.7 mV/%, and for RH the sensitivity is 33 mV/%. Additionally, a working principle, based on observation via scanning electron microscopy, has been proposed to explain the chemical sensing potential of this sensor. Bending seems to enlarge the cracks present in the RFS coverage; this change accounts for the altered selectivity depending on the sensor's curvature. Further studies are needed to confirm result's reliability and the correctness of the interpretation. [ABSTRACT FROM AUTHOR]

The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person's psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications. [ABSTRACT FROM AUTHOR]

Workplace exposure is an important source of ill health. The use of wearable sensors and sensing technologies may help improve and maintain worker health, safety, and wellbeing. Input from workers should inform the integration of these sensors into workplaces. We developed an online survey to understand the acceptability of wearable sensor technologies for occupational health and safety (OSH) management. The survey was disseminated to members of OSH-related organizations, mainly in the United Kingdom and the Netherlands. There were 158 respondents, with over half (n = 91, 58%) reporting current use of wearable sensors, including physical hazards (n = 57, 36%), air quality (n = 53, 34%), and location tracking (n = 36, 23%), although this prevalence likely also captures traditional monitoring equipment. There were no clear distinctions in wearable sensor use between the reported demographic and occupational characteristics, with the exception that hygienists were more likely than non-hygienists (e.g. safety professionals) to use wearable sensors (66% versus 34%). Overall, there was an interest in how sensors can help OSH professionals understand patterns of exposure and improve exposure management practices. Some wariness was expressed primarily around environmental and physical constraints, the quality of the data, and privacy concerns. This survey identified a need to better identify occupational situations that would benefit from wearable sensors and to evaluate existing devices that could be used for occupational hygiene. Further, this work underscores the importance of clearly defining "sensor" according to the occupational setting and context. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment. Non-invasive technologies, such as wearable sensors, coupled with machine learning algorithms, assess quantitatively and objectively the motor performances, with possible benefits either for in-clinic and at-home settings. We conducted a systematic review of the literature on machine learning algorithms embedded in smart devices in Parkinson's disease diagnosis. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between December, 2007 and July, 2023, using a search string combining "Parkinson's disease" AND ("healthy" or "control") AND "diagnosis", within the Groups and Outcome domains. Additional search terms included "Algorithm", "Technology" and "Performance". Results: From 89 identified studies, 47 met the inclusion criteria based on the search string and four additional studies were included based on the Authors' expertise. Gait emerged as the most common parameter analysed by machine learning models, with Support Vector Machines as the prevalent algorithm. The results suggest promising accuracy with complex algorithms like Random Forest, Support Vector Machines, and K-Nearest Neighbours. Discussion: Despite the promise shown by machine learning algorithms, real-world applications may still face limitations. This review suggests that integrating machine learning with wearable sensors has the potential to improve Parkinson's disease diagnosis. These tools could provide clinicians with objective data, potentially aiding in earlier detection. [ABSTRACT FROM AUTHOR]

Skin‐like optoelectronic devices have progressed in leaps and bounds, permitting considerable applications such as remote medical diagnosis, optical communication, biometric authentication, and 3D vision imaging. However, achieving extendable imaging in such frontier detectors poses a formidable challenge for stretchable electronic materials and remains largely unexplored. Herein, an ultrasensitive intrinsically stretchable organic photodiode is configured by the spatially modularizable‐assembled elastic photoactive film for faint near‐infrared light detection and extendable imaging. The performance metrics of the stretchable organic photodiodes rival that of commercial photodetectors, including high specific detectivity (D*noise) over 1012 Jones at 850 nm, ultralow noise current of 4.1 × 10−15 A Hz−1/2 and high stretchability up to 100%. Furthermore, it is the first time to simultaneously achieve the extendable near‐infrared cryptographic imaging and stable photoplethysmography signal detection based on stretchable photodiodes. These results signify a significant advancement toward enabling practical scenarios for stretchable organic optoelectronics. [ABSTRACT FROM AUTHOR]

Helmets and shoulder pads are required equipment intended to protect American football athletes by attenuating collision forces during participation. Surprisingly, research differentiating kinematics from head impacts initiated by helmets from those initiated by shoulder pads among adolescent athletes has not been completed. The current study's purpose was to determine the effects of equipment on head impact kinematics. Sixty-nine male American football athletes from three high schools wore helmets equipped with Head Impact Telemetry (HIT) System instrumentation to quantify peak linear (g) and rotational (rad/s2) accelerations. Data were extracted for video-confirmed impacts during two competitions. Separate multivariable linear regressions using ordinary least squares were conducted to determine if equipment type (helmet vs. shoulder pad) was associated with log-transformed linear and rotational accelerations. In total, 1150 video-confirmed impacts involved helmet (N = 960) or shoulder pad (N = 190) initiated contact. Linear (p = 0.809) and rotational (p = 0.351) acceleration were not associated with equipment type. Head impact kinematics were similar between impacts initiated by either helmets or shoulder pads and suggests an opponent's shoulder pads and helmet can deliver comparable forces to the struck player. Equipment manufacturers may need to consider the unintended role shoulder pads may contribute to head injury risk. [ABSTRACT FROM AUTHOR]

The use of mobile phones while driving is restricted to hands-free mode. But even in the hands-free mode, the use of mobile phones while driving causes cognitive distraction due to the diverted attention of the driver. By employing innovative machine-learning approaches to drivers' physiological signals, namely electroencephalogram (EEG), heart rate (HR), and blood pressure (BP), the impact of talking on hands-free mobile phones in real time has been investigated in this study. The cognitive impact was measured using EEG, HR, and BP data. The authors developed an intelligent model that classified the cognitive performance of drivers using physiological signals that were measured while drivers were driving and reverse bay parking in real time and talking on hands-free mobile phones, considering all driver ages as a complete cohort. Participants completed two numerical tasks varying in difficulty while driving and reverse bay parking. The results show that when participants did the hard tasks, their theta and lower alpha EEG frequency bands increased and exceeded those when they did the easy tasks. The results also show that the BP and HR under phone condition were higher than the BP and HR under no-phone condition. Participants' cognitive performance was classified using a feedforward neural network, and 97% accuracy was achieved. According to qualitative results, participants experienced significant cognitive impacts during the task completion. [ABSTRACT FROM AUTHOR]

Healthcare devices play an important role in the diagnosis, treatment, and monitoring of patients. MXene, as a new member of the two-dimensional materials family, has characteristic conductivity, hydrophilicity, biocompatibility, and antibacterial ability, which makes it suitable for fabricating healthcare devices. By combining MXene with self-healing polymers, durable and self-healing healthcare devices that are resistant to mechanical damage during dynamic work can be achieved. Thanks to the dual biocompatibility of MXene and polymers, the self-healing MXene/polymer composites have the functions of sensing and self-healing in vivo and in vitro, serving as a basis for modern healthcare devices. Herein, we summarize the recent progress of using MXene/polymer composites to fabricate skin-friendly sensors with self-healing capability: universal strategies for fabricating self-healing MXene sensors and their fundamental performance are discussed, and biomedical healthcare applications are demonstrated. This review aims to provide a reference for MXene-based self-healing healthcare electronics and facilitate further efforts in the innovation of modern biomedical devices. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) deals with the automatic recognition of physical activities and plays a crucial role in healthcare and sports where wearable sensors and intelligent computational techniques are used. We propose a HAR algorithm that uses the smartphones accelerometer data for human activity recognition. In particular, we present a recurrent convolutional neural network-based HAR algorithm that combines a Convolutional Neural Network (CNN) to extract temporal features from the sensor data, a Fuzzy C-Means (FCM) clustering algorithm to cluster the features extracted by the CNN, and a Long Short-Term Memory (LSTM) network to learn the temporal dependencies between the features. We evaluate the proposed methodology on two distinct datasets: the MotionSense dataset and the WISDM dataset. We evaluate the proposed CNN-FCM-LSTM model on the publicly available MotionSense dataset to classify ten activity types: 1) walking upstairs, 2) walking downstairs, 3) jogging, 4) sitting, 5) standing, 6) level ground walking, 7) jumping jacks, 8) brushing teeth, 9) writing, and 10) eating. Next, we evaluate the model's performance on the WISDM dataset to assess its ability to generalize to unseen data. On the MotionSense test dataset, CNN-FCM-LSTM achieves a classification accuracy of 99.69%, a sensitivity of 99.62%, a specificity of 99.63%, and a false positive rate per hour (FPR/h) of 0.37%. Meanwhile, it achieves a classification accuracy of 97.27% on the WISDM dataset. The CNN-FCM-LSTM model's capability to classify a diverse range of activities within a single architecture is noteworthy. The results suggest that the proposed CNN-FCM-LSTM model using smartphone inputs is more accurate, reliable, and robust in detecting and classifying activities than the state-of-the-art models. It should be noted that activity recognition technology has the potential to aid in studying the underpinnings of physical activity, designing more effective training regimens, and simulating the rigors of competition in sports. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increases caregivers' burdens. The use of the quantitative gait data of people with PD and deep learning (DL) approaches based on gait are emerging as increasingly promising methods to support and aid clinical decision making, with the aim of providing a quantitative and objective diagnosis, as well as an additional tool for disease monitoring. This will allow for the early detection of the disease, assessment of progression, and implementation of therapeutic interventions. In this paper, the authors provide a systematic review of emerging DL techniques recently proposed for the analysis of PD by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed, and Web of Science databases were searched across an interval of six years (between 2018, when the first article was published, and 2023). A total of 25 articles were included in this review, which reports studies on the movement analysis of PD patients using both wearable and non-wearable sensors. Additionally, these studies employed DL networks for classification, diagnosis, and monitoring purposes. The authors demonstrate that there is a wide employment in the field of PD of convolutional neural networks for analyzing signals from wearable sensors and pose estimation networks for motion analysis from videos. In addition, the authors discuss current difficulties and highlight future solutions for PD monitoring and disease progression. [ABSTRACT FROM AUTHOR]

Wearable sensor-based human activity recognition (HAR) methods hold considerable promise for upper-level control in exoskeleton systems. However, such methods tend to overlook the critical role of data quality and still encounter challenges in cross-subject adaptation. To address this, we propose an active learning framework that integrates the relation network architecture with data sampling techniques. Initially, target data are used to fine tune two auxiliary classifiers of the pre-trained model, thereby establishing subject-specific classification boundaries. Subsequently, we assess the significance of the target data based on classifier discrepancy and partition the data into sample and template sets. Finally, the sampled data and a category clustering algorithm are employed to tune model parameters and optimize template data distribution, respectively. This approach facilitates the adaptation of the model to the target subject, enhancing both accuracy and generalizability. To evaluate the effectiveness of the proposed adaptation framework, we conducted evaluation experiments on a public dataset and a self-constructed electromyography (EMG) dataset. Experimental results demonstrate that our method outperforms the compared methods across all three statistical metrics. Furthermore, ablation experiments highlight the necessity of data screening. Our work underscores the practical feasibility of implementing user-independent HAR methods in exoskeleton control systems. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human–computer interaction. This systematic review of the 19 most relevant studies examines the potential of IoT in health and lifestyle management by conducting detailed analyses and quality assessments of each study. Findings indicate that IoT-based systems effectively monitor various health parameters using biosensors, facilitate real-time feedback, and support personalized health recommendations. Key limitations include small sample sizes, insufficient security measures, practical issues with wearable sensors, and reliance on internet connectivity in areas with poor network infrastructure. The reviewed studies demonstrated innovative applications of IoT, focusing on M2M interactions, edge devices, multimodality health monitoring, intelligent decision-making, and automated health management systems. These insights offer valuable recommendations for optimizing IoT technologies in health and wellness management. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of using Hospital Fit as part of usual care physiotherapy on the physical activity (PA) behavior of hospitalized patients compared to patients who received physiotherapy before implementation of Hospital Fit. In addition, a process evaluation is conducted. A prospective, multi-center, mixed-methods stepped wedge cluster randomized trial was performed at the cardiology and medical oncology departments of two Dutch university medical centers. Patients were included in the non-intervention or intervention phase. During the non-intervention phase, patients received usual care physiotherapy. During the intervention phase, Hospital Fit was additionally used. Mean time spent walking, standing, lying/sitting per day and the number of postural transitions from lying/sitting to standing/walking positions were measured using an accelerometer and analyzed using linear mixed models. A process evaluation was performed using questionnaires and semi-structured interviews with patients and focus-group interviews with healthcare professionals. A total of 77 patients were included, and data from 63 patients were used for data analysis. During the intervention phase, the average time spent walking per day was 20 min (95% confidence interval: −2 to 41 min) higher than during the non-intervention phase (p = 0.075). No significant differences were found for mean time spent standing per day, mean time spent lying/sitting per day, or the number of postural transitions per day either. During the intervention phase, 87% of patients used Hospital Fit at least once, with a median daily use of 2.5 to 4.0 times. Patients and healthcare professionals believed that Hospital Fit improved patients' PA behavior and recovery. Insufficient digital skills and technical issues were described as challenges. Although patients and healthcare professionals described Hospital Fit as an added value, no statistically significant effects were found. [ABSTRACT FROM AUTHOR]

This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing six distinct activities using five wearable inertial measurement units placed at various anatomical locations. The signals collected from the sensors were first processed to eliminate noise and then input into an LSTM neural network for recognizing features in sequential time-dependent data. Results indicated that the chest-mounted sensor provided the highest F1-score of 0.939, representing superior performance over other placements and combinations of them. Moreover, the magnetometer surpassed the accelerometer and gyroscope, highlighting its superior ability to capture crucial orientation and motion data related to the investigated activities. However, multimodal fusion of accelerometer, gyroscope, and magnetometer data showed the benefit of integrating data from different sensor types to improve classification accuracy. The study emphasizes the effectiveness of strategic sensor placement and fusion in optimizing human activity recognition, thus minimizing data requirements and computational expenses, and resulting in a cost-optimal system configuration. Overall, this research contributes to the development of more intelligent, safe, cost-effective adaptive synergistic systems that can be integrated into a variety of applications. [ABSTRACT FROM AUTHOR]

Epidermal electronics has garnered significant research attention due to its promising applications in wearable human‐machine interfaces and intelligent healthcare sensing. However, their widespread use faces challenges due to complex manufacturing processes, high material costs, inadaptability to different skin surfaces, and inadequate reusability. Herein, inspired by the biological reshapability and environmental adaptability of amoeba, an ultra‐deformable (≈2600% strain), bioadhesive (adhesive strength ≈3 kPa), strong self‐healing (fastest recovery time ≈1s, maximum wound distance ≈5 mm), and electromechanical‐durable wearable electronic slime (E‐slime) is proposed, which can instantaneously form on‐skin electronics in situ to detect body motion and physiological signals. E‐slime demonstrates desired sensing performance with high sensitivity (gauge factor 2.95), wide sensing range (up to 400% strain), and low detection limit (≈1% strain), which can seamlessly adhere to the skin and can be easily reused multiple times (≈100 cycles usage). E‐slime also enables on‐the‐fly deployment of motion monitoring tasks at various body locations, showcasing its versatility and reliability for body motion recognition and personal health monitoring. This study holds potential for next‐generation green electronics, motion sensing devices, and wearable human‐machine interfaces, ultimately helping to ensure healthy lives and promote well‐being. [ABSTRACT FROM AUTHOR]

Introduction: Digital health technologies (DHTs) have the potential to alleviate challenges experienced in clinical trials through more objective, naturalistic, and frequent assessments of functioning. However, implementation of DHTs come with their own challenges, including acceptability and ease of use for study participants. In addition to acceptability, it is also important to understand device proficiency in the general population and within patient populations who may be asked to use DHTs for extended periods of time. We thus aimed to provide an overview of participant feedback on acceptability of DHTs, including body-worn sensors used in the clinic and a mobile application used at-home, used throughout the duration of the Wearable Assessments in the Clinic and at Home in Parkinson's Disease (WATCH-PD) study, an observational, longitudinal study looking at disease progression in early Parkinson's Disease (PD). Methods: 82 participants with PD and 50 control participants were enrolled at 17 sites throughout the United States and followed for 12 months. We assessed participants' general device proficiency at baseline, using the Mobile Device Proficiency Questionnaire (MDPQ). The mean MDPQ score at Baseline did not significantly differ between PD patients and healthy controls (20.6 [2.91] vs 21.5 [2.94], p = .10). Results: Questionnaire results demonstrated that participants had generally positive views on the comfort and use of the digital technologies throughout the duration of the study, regardless of group. Discussion: This is the first study to evaluate patient feedback and impressions of using technology in a longitudinal observational study in early Parkinson's Disease. Results demonstrate device proficiency and acceptability of various DHTs in people with Parkinson's does not differ from that of neurologically healthy older adults, and, overall, participants had a favorable view of the DHTs deployed in the WATCH-PD study. [ABSTRACT FROM AUTHOR]

Textile‐based electromechanical sensors are increasingly used as wearable sensors for various applications, such as health monitoring and human‐machine interfaces. These sensors are becoming increasingly popular as they offer a comfortable and conformable sensing platform and possess properties that can be tuned by selecting different fiber materials, yarn‐spinning techniques, or fabric fabrication methods. Although it is still in its early stages, recent attempts have been made to introduce auxeticity to textile sensors to enhance their sensitivity. Having a negative Poisson's ratio, i.e., undergoing expansion laterally when subjected to tensile forces and contraction laterally under compressive forces, makes them distinct from conventional sensors with positive Poisson's ratio. This unique feature has demonstrated great potential in enhancing the performance of electromechanical sensors. This review presents an overview of electromechanical sensors based on auxetic textiles (textiles made from auxetic materials and/or non‐auxetic materials but with auxetic structures), specifically focusing on how the unique auxetic deformation impacts sensing performance. Sensors based on different working mechanisms, including piezoelectric, triboelectric, piezoresistive, and piezocapacitive, are covered. It is envisioned that incorporating auxeticity and electromechanical sensing capabilities into textiles will significantly advance wearable technology, leading to new sensors for health monitoring, fitness tracking, and smart clothing. [ABSTRACT FROM AUTHOR]

Background: With treatment trials on the horizon, this study aimed to identify candidate digital‐motor gait outcomes for autosomal recessive spastic ataxia of Charlevoix‐Saguenay (ARSACS), capturable by wearable sensors with multicenter validity, and ideally also ecological validity during free walking outside laboratory settings. Methods: Cross‐sectional multicenter study (four centers), with gait assessments in 36 subjects (18 ARSACS patients; 18 controls) using three body‐worn sensors (Opal, APDM) in laboratory settings and free walking in public spaces. Sensor gait measures were analyzed for discriminative validity from controls, and for convergent (ie, clinical and patient relevance) validity by correlations with SPRSmobility (primary outcome) and Scale for the Assessment and Rating of Ataxia (SARA), Spastic Paraplegia Rating Scale (SPRS), and activities of daily living subscore of the Friedreich Ataxia Rating Scale (FARS‐ADL) (exploratory outcomes). Results: Of 30 hypothesis‐based digital gait measures, 14 measures discriminated ARSACS patients from controls with large effect sizes (|Cliff's δ| > 0.8) in laboratory settings, with strongest discrimination by measures of spatiotemporal variability Lateral Step Deviation (δ = 0.98), SPcmp (δ = 0.94), and Swing CV (δ = 0.93). Large correlations with the SPRSmobility were observed for Swing CV (Spearman's ρ = 0.84), Speed (ρ = −0.63), and Harmonic Ratio V (ρ = −0.62). During supervised free walking in a public space, 11/30 gait measures discriminated ARSACS from controls with large effect sizes. Large correlations with SPRSmobility were here observed for Swing CV (ρ = 0.78) and Speed (ρ = −0.69), without reductions in effect sizes compared with laboratory settings. Conclusions: We identified a promising set of digital‐motor candidate gait outcomes for ARSACS, applicable in multicenter settings, correlating with patient‐relevant health aspects, and with high validity also outside laboratory settings, thus simulating real‐life walking with higher ecological validity. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Sleep quality (SQ) is a crucial aspect of overall health. Poor sleep quality may cause cognitive impairment, mood disturbances, and an increased risk of chronic diseases. Therefore, assessing sleep quality helps identify individuals at risk and develop effective interventions. SQ has been demonstrated to affect heart rate variability (HRV) and skin temperature even during wakefulness. In this perspective, using wearables and contactless technologies to continuously monitor HR and skin temperature is highly suited for assessing objective SQ. However, studies modeling the relationship linking HRV and skin temperature metrics evaluated during wakefulness to predict SQ are lacking. This study aims to develop machine learning models based on HRV and skin temperature that estimate SQ as assessed by the Pittsburgh Sleep Quality Index (PSQI). HRV was measured with a wearable sensor, and facial skin temperature was measured by infrared thermal imaging. Classification models based on unimodal and multimodal HRV and skin temperature were developed. A Support Vector Machine applied to multimodal HRV and skin temperature delivered the best classification accuracy, 83.4%. This study can pave the way for the employment of wearable and contactless technologies to monitor SQ for ergonomic applications. The proposed method significantly advances the field by achieving a higher classification accuracy than existing state-of-the-art methods. Our multimodal approach leverages the synergistic effects of HRV and skin temperature metrics, thus providing a more comprehensive assessment of SQ. Quantitative performance indicators, such as the 83.4% classification accuracy, underscore the robustness and potential of our method in accurately predicting sleep quality using non-intrusive measurements taken during wakefulness. [ABSTRACT FROM AUTHOR]

Bradykinesia is a term describing several manifestations of movement disruption caused by Parkinson's disease (PD), including movement slowing, amplitude reduction, and gradual decrease of speed and amplitude over multiple repetitions of the same movement. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves bradykinesia in patients with PD. We examined the effect of DBS on specific components of bradykinesia when applied at two locations within the STN, using signal processing techniques to identify the time course of amplitude and frequency of repeated hand pronation-supination movements performed by participants with and without PD. Stimulation at either location increased movement amplitude, increased frequency, and decreased variability, though not to the range observed in the control group. Amplitude and frequency showed decrement within trials, which was similar in PD and control groups and did not change with DBS. Decrement across trials, by contrast, differed between PD and control groups, and was reduced by stimulation. We conclude that DBS improves specific aspects of movement that are disrupted by PD, whereas it does not affect short-term decrement that could reflect muscular fatigue. NEW & NOTEWORTHY: In this study, we examined different components of bradykinesia in patients with Parkinson's disease (PD). We identified different components through signal processing techniques and their response to deep brain stimulation (DBS). We found that some components of bradykinesia respond to stimulation, whereas others do not. This knowledge advances our understanding of brain mechanisms that control movement speed and amplitude. [ABSTRACT FROM AUTHOR]

Inertial measurement units (IMU) are increasingly utilized to capture biomechanical measures such as joint kinematics outside traditional biomechanics laboratories. These wearable sensors have been proven to help clinicians and engineers monitor rehabilitation progress, improve prosthesis development, and record human performance in a variety of settings. The Valor IMU aims to offer a portable motion capture alternative to provide reliable and accurate joint kinematics when compared to industry gold standard optical motion capture cameras. However, IMUs can have disturbances in their measurements caused by magnetic fields, drift, and inappropriate calibration routines. Therefore, the purpose of this investigation is to validate the joint angles captured by the Valor IMU in comparison to an optical motion capture system across a variety of movements. Our findings showed mean absolute differences between Valor IMU and Vicon motion capture across all subjects' joint angles. The tasks ranged from 1.81 degrees to 17.46 degrees, the root mean squared errors ranged from 1.89 degrees to 16.62 degrees, and interclass correlation coefficient agreements ranged from 0.57 to 0.99. The results in the current paper further promote the usage of the IMU system outside traditional biomechanical laboratories. Future examinations of this IMU should include smaller, modular IMUs with non-slip Velcro bands and further validation regarding transverse plane joint kinematics such as joint internal/external rotations. [ABSTRACT FROM AUTHOR]

Over the last few decades, a growing number of studies have used wearable technologies, such as inertial and pressure sensors, to investigate various domains of music experience, from performance to education. In this paper, we systematically review this body of literature using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method. The initial search yielded a total of 359 records. After removing duplicates and screening for content, 23 records were deemed fully eligible for further analysis. Studies were grouped into four categories based on their main objective, namely performance-oriented systems, measuring physiological parameters, gesture recognition, and sensory mapping. The reviewed literature demonstrated the various ways in which wearable systems impact musical contexts, from the design of multi-sensory instruments to systems monitoring key learning parameters. Limitations also emerged, mostly related to the technology's comfort and usability, and directions for future research in wearables and music are outlined. [ABSTRACT FROM AUTHOR]

While interest in using wearable sensors to measure infant leg movement is increasing, attention should be paid to the characteristics of the sensors. Specifically, offset error in the measurement of gravitational acceleration (g) is common among commercially available sensors. In this brief report, we demonstrate how we measured the offset and other errors in three different off-the-shelf wearable sensors available to professionals and how they affected a threshold-based movement detection algorithm for the quantification of infant leg movement. We describe how to calibrate and correct for these offsets and how conducting this improves the reproducibility of results across sensors. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is among prevalent occupational diseases, causing early retirement and disabilities. This paper looks into occupational-related COPD prevention and intervention in the workplace for Industry 4.0-compliant occupation health and safety management. The economic burden and other severe problems caused by COPD are introduced. Subsequently, seminal research in relevant areas is reviewed. The prospects and challenges are introduced and discussed based on critical management approaches. An initial design of an Industry 4.0-compliant occupational COPD prevention system is presented at the end. [ABSTRACT FROM AUTHOR]

Musculoskeletal Disorders (MSDs) stand as a prominent cause of injuries in modern agriculture. Scientific research has highlighted a causal link between MSDs and awkward working postures. Several methods for the evaluation of working postures, and related risks, have been developed such as the Rapid Upper Limb Assessment (RULA). Nevertheless, these methods are generally applied with manual measurements on pictures or videos. As a consequence, their applicability could be scarce, and their effectiveness could be limited. The use of wearable sensors to collect kinetic data could facilitate the use of these methods for risk assessment. Nevertheless, the existing system may not be usable in the agricultural and vine sectors because of its cost, robustness and versatility to the various anthropometric characteristics of workers. The aim of this study was to develop a technology capable of collecting accurate data about uncomfortable postures and repetitive movements typical of vine workers. Specific objectives of the project were the development of a low-cost, robust, and wearable device, which could measure data about wrist angles and workers' hand positions during possible viticultural operations. Furthermore, the project was meant to test its use to evaluate incongruous postures and repetitive movements of workers' hand positions during pruning operations in vineyard. The developed sensor had 3-axis accelerometers and a gyroscope, and it could monitor the positions of the hand–wrist–forearm musculoskeletal system when moving. When such a sensor was applied to the study of a real case, such as the pruning of a vines, it permitted the evaluation of a simulated sequence of pruning and the quantification of the levels of risk induced by this type of agricultural activity. [ABSTRACT FROM AUTHOR]

Prevalence estimates of Parkinson's disease (PD)—the fastest-growing neurodegenerative disease—are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance. [ABSTRACT FROM AUTHOR]

In many regions globally, including low-resource settings, there is a growing trend towards using mHealth technology, such as wearable sensors, to enhance health behaviors and outcomes. However, adoption of such devices in research conducted in low-resource settings lags behind use in high-resource areas. Moreover, there is a scarcity of research that specifically examines the user experience, readiness for and challenges of integrating wearable sensors into health research and community interventions in low-resource settings specifically. This study summarizes the reactions and experiences of young women (N = 57), ages 18 to 24 years, living in poverty in Kampala, Uganda, who wore Garmin vívoactive 3 smartwatches for five days for a research project. Data collected from the Garmins included participant location, sleep, and heart rate. Through six focus group discussions, we gathered insights about the participants' experiences and perceptions of the wearable devices. Overall, the wearable devices were met with great interest and enthusiasm by participants. The findings were organized across 10 domains to highlight reactions and experiences pertaining to device settings, challenges encountered with the device, reports of discomfort/comfort, satisfaction, changes in daily activities, changes to sleep, speculative device usage, community reactions, community dynamics and curiosity, and general device comfort. The study sheds light on the introduction of new technology in a low-resource setting and also on the complex interplay between technology and culture in Kampala's slums. We also learned some insights into how wearable devices and perceptions may influence behaviors and social dynamics. These practical insights are shared to benefit future research and applications by health practitioners and clinicians to advance and enhance the implementation and effectiveness of wearable devices in similar contexts and populations. These insights and user experiences, if incorporated, may enhance device acceptance and data quality for those conducting research in similar settings or seeking to address population-specific needs and health issues. [ABSTRACT FROM AUTHOR]

This paper presents innovations in green electronic and computing technologies. The importance and the status of the main subjects in green electronic and computing technologies are presented in this paper. In the last semicentennial, the planet suffered from rapid changes in climate. The planet is suffering from increasingly wild storms, hurricanes, typhoons, hard droughts, increases in seawater height, floods, seawater acidification, decreases in groundwater reserves, and increases in global temperatures. These climate changes may be irreversible if companies, organizations, governments, and individuals do not act daily and rapidly to save the planet. Unfortunately, the continuous growth in the number of computing devices, cellular devices, smartphones, and other smart devices over the last fifty years has resulted in a rapid increase in climate change. It is severely crucial to design energy-efficient "green" technologies and devices. Toxic waste from computing and cellular devices is rapidly filling up landfills and increasing air and water pollution. This electronic waste contains hazardous and toxic materials that pollute the environment and affect our health. Green computing and electronic engineering are employed to address this climate disaster. The development of green materials, green energy, waste, and recycling are the major objectives in innovation and research in green computing and electronics technologies. Energy-harvesting technologies can be used to produce and store green energy. Wearable active sensors and metamaterial antennas with circular split ring resonators (CSSRs) containing energy-harvesting units are presented in this paper. The measured bandwidth of the matched sensor is around 65% for VSWR, which is better than 3:1. The sensor gain is 14.1 dB at 2.62 GHz. A wideband 0.4 GHz to 6.4 GHz slot antenna with an RF energy-harvesting unit is presented in this paper. The Skyworks Schottky diode, SMS-7630, was used as the rectifier diode in the harvesting unit. If we transmit 20 dBm of RF power from a transmitting antenna that is located 0.2 m from the harvesting slot antenna at 2.4 GHz, the output voltage at the output port of the harvesting unit will be around 1 V. The power conversion efficiency of the metamaterial antenna dipole with metallic strips is around 75%. Wearable sensors with energy-harvesting units provide efficient, low-cost healthcare services that contribute to a green environment and minimize energy consumption. The measurement process and setups of wearable sensors are presented in this paper. [ABSTRACT FROM AUTHOR]