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We present a machine learning driven system to monitor joint flexion angles during dynamic motion, using a wearable loop-based sensor. Our approach uses wearable loops to collect transmission coefficient data and an Artificial Neural Network (ANN) with fine-tuned parameters to increase accuracy of the measured angles. We train and validate the ANN for sagittal plane flexion of a leg phantom emulating slow motion, walking, brisk walking, and jogging. We fabricate the loops on conductive threads and evaluate the effect of fabric drift via measurements in the absence and presence of fabric. In the absence of fabric, our model produced a root mean square error (RMSE) of 5.90°, 6.11°, 5.90°, and 5.44° during slow motion, walking, brisk walking, and jogging. The presence of fabric degraded the RMSE to 8.97°, 7.21°, 9.41°, and 7.79°, respectively. Without the proposed ANN method, errors exceeded 35.07° for all scenarios. Proof-of-concept results on three human subjects further validate this performance. Our approach empowers feasibility of wearable loop sensors for motion capture in dynamic, real-world environments. Increasing speed of motion and the presence of fabric degrade sensor performance due to added noise. Nevertheless, the proposed framework is generalizable and can be expanded upon in the future to improve upon the reported angular resolution. [ABSTRACT FROM AUTHOR]

The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users' trust. As technology continues to advance, the refinement of IoT devices' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals' health outcomes and the role of wearables in promoting a healthier lifestyle. [ABSTRACT FROM AUTHOR]

Wearable devices have attracted significant attention in epilepsy research in recent years for their potential to enhance patient care through improved seizure monitoring and forecasting. This narrative review presents a detailed overview of the current clinical state of the art while addressing how devices that assess autonomic nervous system (ANS) function reflect seizures and central nervous system (CNS) state changes. This includes a description of the interactions between the CNS and the ANS, including physiological and epilepsy‐related changes affecting their dynamics. We first discuss technical aspects of measuring autonomic biosignals and considerations for using ANS sensors in clinical practice. We then review recent seizure detection and seizure forecasting studies, highlighting their performance and capability for seizure detection and forecasting using devices measuring ANS biomarkers. Finally, we address the field's challenges and provide an outlook for future developments. [ABSTRACT FROM AUTHOR]

Summary Self‐report studies show that sleep and positive and negative affect are closely and bidirectionally linked. However, studies assessing sleep objectively yield more inconsistent results. This study assessed the reciprocal, daily relationship between sleep as measured with electroencephalography (EEG) and affect (measured in the evening) in a natural setting. We assessed sleep both on the macrolevel (i.e., rapid eye movement [REM] sleep and slow‐wave sleep [SWS] duration) and on the microlevel (i.e., REM sleep fragmentation). In this study, 33 participants (i.e., healthy college students, mean [standard deviation] age 21.55 [3.73] years, 67% female) were followed for 2 weeks. Each participant wore an EEG headband for 15 nights and had polysomnography during 3 of the 15 nights providing 72 analysable nights of polysomnography and 271 analysable nights with the EEG headband. Every evening participants reported their momentary negative and positive affect. We examined the relationship between pre‐sleep affect and the sleep variables, as well as the reverse relationship, with sleep variables predicting evening affect the next day. We detected that higher negative affect in the evening was related to more fragmented REM sleep. However, this result was only found with polysomnography and not with the EEG headband. No significant associations were found between affect and time spent in REM sleep and SWS. Overall, no support was found for the reciprocal association between negative and positive affect and EEG measured sleep. Only limited support was found for an association in one direction (i.e., evening negative affect was associated with more REM sleep fragmentation at night). [ABSTRACT FROM AUTHOR]

Background. Knee and hip arthroplasty are two of the most frequently performed procedures in orthopaedic surgery. They are associated with positive patient-reported outcomes and significant improvements in quality of life for patients. Despite this, there may be room for further progress by quantifying functional improvements with gait analysis. Our study therefore aims to characterise the disease-specific gait pattern of participants with knee and hip osteoarthritis undergoing total joint replacement using a single chest-based wearable sensor. Methods. Twenty-nine participants awaiting total hip replacement and 28 participants awaiting total knee replacement underwent three-dimensional motion analysis with inertial wearable sensors. These gait metrics were then compared with 28 healthy controls of similar ages. Differences in gait metrics were evaluated using a T-test. The participants were recruited through a single centre to participate in this cross-sectional observational study. Participants with osteoarthritis severity sufficient to warrant surgical intervention were considered for inclusion in our study. The participants were instructed to walk 15–120 m in a hospital environment while fitted with a chest-based wearable sensor. Results. In total, three domains were evaluated, including spatiotemporal, variability and asymmetry parameters. There were marked variations in the gait asymmetry parameters and step length variation in both the hip and knee osteoarthritis patients compared with the healthy controls. The magnitude of gait deterioration in terms of step length asymmetry was greater on average in the hip osteoarthritis group than the knee group. The hip osteoarthritis (+180%, p < 0.001) and knee osteoarthritis (+129%, p = 0.001) groups demonstrated marked differences in step length asymmetry. Discussion. A single chest-based sensor was found to be capable of detecting pathological gait signatures in osteoarthritis patients when compared with age-matched controls. Future studies should compare pre- and postoperative changes to disease-specific gait impairments to validate the use of wearable sensors as a clinical adjunct. [ABSTRACT FROM AUTHOR]

Work-related diseases and disorders remain a significant global health concern, necessitating multifaceted measures for mitigation. One potential measure is work technique training utilizing augmented feedback through wearable motion capture systems. However, there exists a research gap regarding its current effectiveness in both real work environments and controlled settings, as well as its ability to reduce postural exposure and retention effects over short, medium, and long durations. A rapid review was conducted, utilizing two databases and three previous literature reviews to identify relevant studies published within the last twenty years, including recent literature up to the end of 2023. Sixteen studies met the inclusion criteria, of which 14 were of high or moderate quality. These studies were summarized descriptively, and the strength of evidence was assessed. Among the included studies, six were rated as high quality, while eight were considered moderate quality. Notably, the reporting of participation rates, blinding of assessors, and a-priori power calculations were infrequently performed. Four studies were conducted in real work environments, while ten were conducted in controlled settings. Vibration feedback was the most common feedback type utilized (n = 9), followed by auditory (n = 7) and visual feedback (n = 1). All studies employed corrective feedback initiated by the system. In controlled environments, evidence regarding the effectiveness of augmented feedback from wearable motion capture systems to reduce postural exposure ranged from strong evidence to no evidence, depending on the time elapsed after feedback administration. Conversely, for studies conducted in real work environments, the evidence ranged from very limited evidence to no evidence. Future reach needs are identified and discussed. [ABSTRACT FROM AUTHOR]

The adoption of the Internet of Things (IoT) in the mining industry can dramatically enhance the safety of workers while simultaneously decreasing monitoring costs. By implementing an IoT solution consisting of a number of interconnected smart devices and sensors, mining industries can improve response times during emergencies and also reduce the number of accidents, resulting in an overall improvement of the social image of mines. Thus, in this paper, a robust end-to-end IoT system for supporting workers in harsh environments such as in mining industries is presented. The full IoT solution includes both edge devices worn by the workers in the field and a remote cloud IoT platform, which is responsible for storing and efficiently sharing the gathered data in accordance with regulations, ethics, and GDPR rules. Extended experiments conducted to validate the IoT components both in the laboratory and in the field proved the effectiveness of the proposed solution in monitoring the real-time status of workers in mines. [ABSTRACT FROM AUTHOR]

Insertable biosensor systems are medical diagnostic devices with two primary components: an implantable biosensor within the body and a wearable monitor that can remotely interrogate the biosensor from outside the body. Because the biosensor does not require a physical connection to the electronic monitor, insertable biosensor systems promise improved patient comfort, reduced inflammation and infection risk, and extended operational lifetimes relative to established percutaneous biosensor systems. However, the lack of physical connection also presents technical challenges that have necessitated new innovations in developing sensing chemistries, transduction methods, and communication modalities. In this review, we discuss the key developments that have made insertables a promising option for longitudinal biometric monitoring and highlight the essential needs and existing development challenges to realizing the next generation of insertables for extended-use diagnostic and prognostic devices. [ABSTRACT FROM AUTHOR]

Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive analysis of the assessment of CW using wearable sensors in HMI. Methods: this narrative review explores several techniques and procedures for collecting physiological data through wearable sensors with the possibility to integrate these multiple physiological signals, providing a multimodal monitoring of the individuals'CW. Finally, it focuses on the impact of artificial intelligence methods in the physiological signals data analysis to provide models of the CW to be exploited in HMI. Results: the review provided a comprehensive evaluation of the wearables, physiological signals, and methods of data analysis for CW evaluation in HMI. Conclusion: the literature highlighted the feasibility of employing wearable sensors to collect physiological signals for an ecological CW monitoring in HMI scenarios. However, challenges remain in standardizing these measures across different populations and contexts. [ABSTRACT FROM AUTHOR]

Background: Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, a reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. Methods: In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on ten subjects while performing 5-min sitting and walking tasks. Results: Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. Conclusion: We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity. [ABSTRACT FROM AUTHOR]

The growth of wearable technology has enabled the collection of even more personalized information on individuals. New health-related devices marketed to consumers collect health information that might not fall under the traditional category of Protected Health Information, and thus, HIPAA protections do not fully apply. Meaning, commercial wearable health devices do not fall under FDA oversight, and data not paired with a doctor–patient relationship do not fall under HIPAA privacy protection; thus, much of the gathered health-related metrics are left without regulation and open to be sold to data brokers. As such, these data can be leveraged by health insurance, law enforcement, and employers, to name a few. This manuscript explores the loopholes in current regulations and suggests a framework that categorizes wearable data and addresses challenges in data transfer. Furthermore, taking a user perspective, the suggested framework offers solutions that aim to guide users and policymakers in navigating privacy issues in wearable technology. [ABSTRACT FROM AUTHOR]

The nonlinear variability present during steady-state gait may provide a signature of health and showcase one's walking adaptability. Although treadmills can capture vast amounts of walking data required for estimating variability within a small space, gait patterns may be misrepresented compared to an overground setting. Smartphones may provide a low-cost and user-friendly estimate of gait patterns among a variety of walking settings. However, no study has investigated differences in gait patterns derived from a smartphone between treadmill walking (TW) and overground walking (OW). This study implemented a smartphone accelerometer to compare differences in temporal gait variability and gait dynamics between TW and OW. Sixteen healthy adults (8F; 24.7 ± 3.8 years) visited the laboratory on three separate days and completed three 8-minute OW and three TW trials, at their preferred speed, during each visit. The inter-stride interval was calculated as the time difference between right heel contact events located within the vertical accelerometery signals recorded from a smartphone while placed in participants front right pant pocket during walking trials. The inter-stride interval series was used to calculate stride time standard deviation (SD) and coefficient of variation (COV), statistical persistence (fractal scaling index), and statistical regularity (sample entropy). Two-way analysis of variance compared walking condition and laboratory visits for each measure. Compared to TW, OW displayed significantly (p < 0.01) greater stride time SD (0.014 s, 0.020 s), COV (1.26 %, 1.82 %), fractal scaling index (0.70, 0.79) and sample entropy (1.43, 1.63). No differences were found between visits for all measures. Smartphone-based assessment of gait provides the ability to distinguish between OW and TW conditions, similar to previously established methodologies. Furthermore, smartphones may be a low-cost and user-friendly tool to estimate gait patterns outside the laboratory to improve ecological validity, with implications for free-living monitoring of gait among various populations. • Smartphones can enable gait monitoring outside the lab and for clinical purposes. • Treadmills reduce gait variability and dynamics compared to overground setting. • Context of walking must be considered within the interpretation of the results. [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]

Gait asymmetries are a common problem in clinical populations, such as those with a history of stroke or Parkinson's disease. The use of a split-belt treadmill is one way to enhance gait symmetry but relies on specialty (and typically expensive) equipment. Alternatively, visual cues have been shown as a method to alter gait mechanics, but their utility in altering gait symmetry has been relatively understudied. Before deploying this method to clinical populations, a proof-of-concept study is needed to explore using visual cues to alter gait symmetry in healthy adults. Therefore, the purpose of this study was to examine the extent to which healthy adults could synchronize to an asymmetric visual cue with a small or large gait asymmetry using wearable sensors to measure gait asymmetries. Seventy-two healthy adults (ages: 23.89 ± 6.08 years) walked on the treadmill for two conditions: with and without the visual cue. Each walking condition lasted 10 min at the participant's preferred walking speed. Inertial sensors were used to measure gait asymmetries. Some participants did not respond to the visual cue, and groups were separated into responders and non-responders. Participants in the small and large asymmetry-responder groups exhibited statistically significant increased asymmetries in single limb support % (p < 0.01) and step duration (s) (p < 0.05, p < 0.01, respectively). Only the large asymmetry-responder group showed statistically significant (p < 0.01) increased asymmetries in stride length. Overall, asymmetrical walking visual cues can alter gait asymmetries, and inertial sensors were sensitive enough to detect small changes in gait asymmetries. [ABSTRACT FROM AUTHOR]

Fitness trackers are increasingly used by their wearers to monitor and optimise their exercise, health, and well-being. What is usually not considered is the role our interoceptive sense, the sense for inner-bodily processes, plays in the interpretation of body data and the formation of habits. By including neuroscientific research, perspectives from pragmatist philosophy, and science and technology studies into the analysis of self-tracking devices, this article will give a perspective on the scope and the effects of the involvement of the interoceptive sense in self-tracking practices. By offering a critical analysis of the contextualisation of experience through self-tracking devices, this article will argue that interoceptive awareness provides the context to make sense of body data. I propose here that paying greater attention to how interoception is involved in the mediation of bodily processes and activities through tracking devices can give novel insights into self-tracking as an embodied and affective practice. [ABSTRACT FROM AUTHOR]

To establish the concurrent validity, acceptability, and sensor optimization of a consumer-grade, wearable, multi-sensor system to capture quantity and quality metrics of mobility and upper limb movements in stroke survivors. Single-session, cross-sectional. Clinical research laboratory. Thirty chronic stroke survivors (age 57 (10) years; 33% female) with mild to severe motor impairments participated. Not Applicable. Participants donned 5 sensors and performed standardized assessments of mobility and upper limb (UL) movement. True/false, positive/negative time in active movement for the UL were calculated and compared to criterion-standards using an accuracy rate. Bland-Altman plots and linear regression models were used to establish concurrent validity of UL movement counts, step counts, and stance time symmetry of MiGo against established criterion-standard measures. Acceptability and sensor optimization were assessed through an end-user survey and decision matrix. Mobility metrics showed excellent association with criterion-standards for step counts (video: r =0.988, P <.001, IMU: r =0.921, P <.001) and stance-time symmetry (r =0.722, P <.001). In the UL, movement counts showed excellent to good agreement (paretic: r =0.849, P <.001, nonparetic: r =0.672, P <.001). Accuracy of active movement time was 85.2% (paretic) and 88.0% (nonparetic) UL. Most participants (63.3%) had difficulty donning/doffing the sensors. Acceptability was high (4.2/5). The sensors demonstrated excellent concurrent validity for mobility metrics and UL movements of stroke survivors. Acceptability of the system was high, but alternative wristbands should be considered. [ABSTRACT FROM AUTHOR]

In this article, we explore multi-material additive manufacturing (MMAM) for conductive trace printing using molten metal microdroplets on polymer substrates to enhance digital signal transmission. Investigating microdroplet spread informs design rules for adjacent trace printing. We studied the effects of print distance on trace morphology and resolution, noting that printing distance showed almost no change in the printed trace pitch. Crosstalk interference between adjacent signal traces was analyzed across frequencies and validated both experimentally and through simulation; no crosstalk was visible for printed traces at input frequencies below 600 kHz. Moreover, we demonstrate printed trace reliability against thermal shock, whereby no discontinuation in conductive traces was observed. Our findings establish design guidelines for MMAM electronics, advancing digital signal transmission capabilities. [ABSTRACT FROM AUTHOR]

The study focuses on developing a noninvasive, flexible polyvinyl alcohol (PVA)‐nickel (Ni) nanocomposite thin film as a passive skin‐integrated patch sensor for wearable temperature monitoring, marking a significant advancement in the field of wearable sensors. This flexible PVA‐Ni nanocomposite thin film serves as a temperature‐sensitive material, offering several advantages over commercially available active‐type sensors. Field emission scanning electron microscopy (FE‐SEM) images show that Ni nanoparticles (NPs) are broadly dispersed throughout the PVA matrix, indicating the effectiveness of the conductive patch in detecting temperature variations. A PVA‐Ni nanocomposite patch with a thickness of 0.08 mm demonstrated superior flexibility, breathability, and lower tearing strength compared to a pure PVA patch. The film also exhibited excellent repeatability in bending tests, maintaining performance after 120 bending and unloading cycles, suggesting its durability for long‐term use as a wearable sensor. Furthermore, the fabricated sensors function as thermistors, with conductivity increasing linearly with temperature. The performance of these temperature sensors was compared, revealing a highest temperature coefficient of resistance (TCR) and thermal index of −1.08%/°C and 1271 K, respectively. The sensors showed high temperature sensitivity between room temperature and 50°C, outperforming typical commercial platinum temperature sensors. The stability and response time of the PVA‐Ni nanocomposite film were evaluated by adhering the patch to a human wrist and capturing thermal images using a FLIR thermal imaging camera. The observed maximum temperature difference of approximately 1.9–2.1°C highlights the patch's sensitivity in detecting temperature changes. Additionally, the antimicrobial properties of the conductive film were tested to assess its biocompatibility, confirming its potential for applications in energy storage, thermal management, and early breast cancer detection. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has increased the pressure on organizations to ensure health and safety in the workplace. An increasing number of organizations are considering wearables and physiolytics devices as part of their safe return to work programs so as to comply with governments' accountability rules. As with other technologies with ambivalent use (i.e., simultaneously beneficial and harmful), the introduction of these devices in work settings is met with skepticism. In this context, nudging strategies as a way of using design, information, and other ways to manipulate behaviors (system 1 nudge) and choices (system 2 nudge) has gained traction and is often applied alongside the introduction of ambivalent technologies with the aim to "nudge" their use. While the feasibility of different nudge strategies is often studied from only a managerial perspective, where employees' volitional autonomy and dignity is often treated as secondary, we explore which nudges are acceptable from the perspectives of ordinary workers. Using Q-methodology as a more evolutionary and participatory way to design nudges, we describe five basic strategies that are (to varying degrees) acceptable to them: (a) positive reinforcement and fun, (b) controlling the organizational environment, (c) self-responsibility, (d) collective responsibility, and (e) adapting the individual environment. Our findings show that there is a wide range of viewpoints on what is being considered an acceptable nudge and stress the importance of a transparent, equal dialogue between those who design nudges and potential nudgees. [ABSTRACT FROM AUTHOR]

Social isolation and loneliness greatly contribute to negative health consequences in older adults. Technological solutions can be an asset in promoting social connections and healthy behaviours. This paper presents an innovative structure for an Internet of Things (IoT) platform specifically tailored for older persons. The framework utilises a supervised learning algorithm to classify users into four identified profiles to facilitate the adoption and engagement of technology. The platform incorporates wearables, such as socks and smart bands, to track physical activity, and a messaging module to encourage social interaction. The platform processes the acquired data to quantify steps and deliver tailored interventions remotely to the older adults through the AGAPE Assistant, the mHealth solution of the platform. Furthermore, the AGAPE Assistant has a user interface design for older adults, with a focus on their specific needs. Additionally, improving digital literacy among older adults is crucial for maximizing the long-term compliance and benefits of such technological solutions. On the other hand, AGAPE Monitor is a web application used by formal caregivers to configure the tailored interventions. The platform's usability was assessed using different usability scale questionnaires, which revealed a mild level of user satisfaction and acceptance. The proposed framework is currently being deployed on more than 112 older adults across three countries: Italy, Romania, and Portugal. The proposed framework provides a holistic solution to encourage active ageing by adopting technology, implementing hybrid interventions, and promoting social interactions. [ABSTRACT FROM AUTHOR]

Affective computing has been widely used to detect and recognize emotional states. The main goal of this study was to detect emotional states using machine learning algorithms automatically. The experimental procedure involved eliciting emotional states using film clips in an immersive and non-immersive virtual reality setup. The participants’ physiological signals were recorded and analyzed to train machine learning models to recognize users’ emotional states. Furthermore, two subjective ratings emotional scales were provided to rate each emotional film clip. Results showed no significant differences between presenting the stimuli in the two degrees of immersion. Regarding emotion classification, it emerged that for both physiological signals and subjective ratings, user-dependent models have a better performance when compared to user-independent models. We obtained an average accuracy of 69.29 ± 11.41% and 71.00 ± 7.95% for the subjective ratings and physiological signals, respectively. On the other hand, using user-independent models, the accuracy we obtained was 54.0 ± 17.2% and 24.9 ± 4.0%, respectively. We interpreted these data as the result of high inter-subject variability among participants, suggesting the need for user-dependent classification models. In future works, we intend to develop new classification algorithms and transfer them to real-time implementation. This will make it possible to adapt to a virtual reality environment in real-time, according to the user’s emotional state. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: The aim of this study was to examine the dose–response associations of device-measured physical activity types and postures (sitting and standing time) with cardiometabolic health. Methods: We conducted an individual participant harmonised meta-analysis of 12,095 adults (mean ± SD age 54.5±9.6 years; female participants 54.8%) from six cohorts with thigh-worn accelerometry data from the Prospective Physical Activity, Sitting and Sleep (ProPASS) Consortium. Associations of daily walking, stair climbing, running, standing and sitting time with a composite cardiometabolic health score (based on standardised z scores) and individual cardiometabolic markers (BMI, waist circumference, triglycerides, HDL-cholesterol, HbA1c and total cholesterol) were examined cross-sectionally using generalised linear modelling and cubic splines. Results: We observed more favourable composite cardiometabolic health (i.e. z score <0) with approximately 64 min/day walking (z score [95% CI] −0.14 [−0.25, −0.02]) and 5 min/day stair climbing (−0.14 [−0.24, −0.03]). We observed an equivalent magnitude of association at 2.6 h/day standing. Any amount of running was associated with better composite cardiometabolic health. We did not observe an upper limit to the magnitude of the dose–response associations for any activity type or standing. There was an inverse dose–response association between sitting time and composite cardiometabolic health that became markedly less favourable when daily durations exceeded 12.1 h/day. Associations for sitting time were no longer significant after excluding participants with prevalent CVD or medication use. The dose–response pattern was generally consistent between activity and posture types and individual cardiometabolic health markers. Conclusions/interpretation: In this first activity type-specific analysis of device-based physical activity, ~64 min/day of walking and ~5.0 min/day of stair climbing were associated with a favourable cardiometabolic risk profile. The deleterious associations of sitting time were fully attenuated after exclusion of participants with prevalent CVD and medication use. Our findings on cardiometabolic health and durations of different activities of daily living and posture may guide future interventions involving lifestyle modification. [ABSTRACT FROM AUTHOR]

Background: Physical behaviors such physical activity, sedentary behavior, and sleep are associated with mortality, but there is a lack of epidemiological data and knowledge using device-measured physical behaviors. Purpose: To assess the feasibility of baseline data collection using the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS) protocols in the specific context of Saudi Arabia. ProPASS is a recently developed global platform for collaborative research that aims to harmonize retrospective and prospective data on device-measured behaviors and health. Using ProPASS methods for collecting data to perform such studies in Saudi Arabia will provide standardized data from underrepresented countries. Method: This study explored the feasibility of baseline data collection in Saudi Arabia between November and December 2022 with a target recruitment of 50 participants aged ≥ 30 years. Established ProPASS methods were used to measure anthropometrics, measure blood pressure, collect blood samples, carry out physical function test, and measure health status and context of physical behaviors using questionnaires. The ActivPal™ device was used to assess physical behaviors and the participants were asked to attend two sessions at (LHRC). The feasibility of the current study was assessed by evaluating recruitment capability, acceptability, suitability of study procedures, and resources and abilities to manage and implement the study. Exit interviews were conducted with all participants. Result: A total of 75 participants expressed an interest in the study, out of whom 54 initially agreed to participate. Ultimately, 48 participants were recruited in the study (recruitment rate: 64%). The study completion rate was 87.5% of the recruited participants; 95% participants were satisfied with their participation in the study and 90% reported no negative feelings related to participating in the study. One participant reported experiencing moderate skin irritation related to placement of the accelerometer. Additionally, 96% of participants expressed their willingness to participate in the study again. Conclusion: Based on successful methodology, data collection results, and participants' acceptability, the ProPASS protocols are feasible to administer in Saudi Arabia. These findings are promising for establishing a prospective cohort in Saudi Arabia. [ABSTRACT FROM AUTHOR]

The introduction of IoT data in court without the consideration of its impact on the privilege against self-incrimination leads to the restriction of this fundamental right of the accused, who sometimes has no real choice to decide for or against the use of their IoT data due to various economic, social and health reasons. This article presents the point of friction between digital evidence and the privilege against self-incrimination and its evolution over the years in Germany and the United States, highlighting the increasing introduction of IoT evidence in court. It then identifies the various reasons why IoT are adopted by users. Finally, this article emphasizes that technical solutions cannot fully contribute to the resolution of the conflict between the privilege against self-incrimination and the use of IoT data as evidence in court. A real solution to the problem can only be achieved by the legislature and corresponding adjustments to existing rules of criminal procedure. [ABSTRACT FROM AUTHOR]

Students’ stress levels may affect their well-being, attentiveness and learning outcomes in primary education classrooms. Positive behavioural interventions and support actions conducted by teachers may alleviate students’ stress levels, especially when addressing special educational needs. In this multimodal learning analytics study, students in a classroom were all given a smartband for their wrist during regular curriculum activities. Data comprised the semester of a single subject as a part of a research project conducted in Sweden. Biobehavioural stress-related arousal of students’ autonomic nervous system was visualised and analysed through distinguished behavioural modes. Additional data include naturalistic observational notes and two short teacher interviews. Research methodology and strategies for innovative implementation were presented and discussed alongside contextual details. For example, stress-level visualisations can aid actionable adjustments of behavioural intervention intensity and provide students’ attentiveness overview for teachers that sequence curricular activities during planning. Findings show an interdisciplinary basis for cost-effective real-time dynamic solutions that involve visual dashboards with advantages to understanding student learning, both at a school-wide system level and for the classroom, if viewed optimistically. However, research on the topic is still in its infancy, notably with ethical risks as a growing pain. [ABSTRACT FROM AUTHOR]

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios. [ABSTRACT FROM AUTHOR]

In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children. [ABSTRACT FROM AUTHOR]

Hematopoietic stem cell transplantation and cell therapies like CAR-T are costly, complex therapeutic procedures. Outpatient models, including at-home transplantation, have been developed, resulting in similar survival results, reduced costs, and increased patient satisfaction. The complexity and safety of the process can be addressed with various emerging technologies (artificial intelligence, wearable sensors, point-of-care analytical devices, drones, virtual assistants) that allow continuous patient monitoring and improved decisionmaking processes. Patients, caregivers, and staff can also benefit from improved training with simulation or virtual reality. However, many technical, operational, and above all, ethical concerns need to be addressed. Finally, outpatient or athome hematopoietic transplantation or CAR-T therapy creates a different, integrated operative system that must be planned, designed, and carefully adapted to the patient's characteristics and distance from the hospital. Patients, clinicians, and their clinical environments can benefit from technically improved at-home transplantation. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to model below and above anaerobic threshold exercise-induced heart rate (HR) drift, so that the corrected HR could better represent V̇O2 kinetics during and after the exercise itself. Methods: Fifteen healthy subjects (age: 28 ± 5 years; V̇O2Max: 50 ± 8 mL/kg/min; 5 females) underwent a maximal and a 30-min submaximal (80% of the anaerobic threshold) running exercises. A five-stage computational (i.e., delay block, new training impulse-calculation block, Sigmoid correction block, increase block, and decrease block) model was built to account for instantaneous HR, fitness, and age and to onset, increase, and decrease according to the exercise intensity and duration. Results: The area under the curve (AUC) of the hysteresis function, which described the differences in the maximal and submaximal exercise-induced V̇O2 and HR kinetics, was significantly reduced for both maximal (26%) and submaximal (77%) exercises and consequent recoveries. Discussion: In conclusion, this model allowed HR drift instantaneous correction, which could be exploited in the future for more accurate V̇O2 estimations. [ABSTRACT FROM AUTHOR]

The use of wearable devices in sport is steadily increasing. This is particularly true for the applications using the concept of the real-time biomechanical feedback. The paper investigates the problem of providing such feedback to the user via tactile actuators in aquatic environments, which poses an additional challenge. A waterproof wearable device with six tactile actuators, control circuit, microcontroller and wireless connectivity is developed. Two studies are carried out with it. The exploratory study provides a basic understanding of the human perception of tactile interfaces in an aquatic environment. The device is used by 34 participants in eight separate trials determined by three independent variables i. e. a setup, environment and activity. Participants wear the devices around their waist and on their head, in and out of the water, while moving and at rest. A usability study involving 51 participants tests the use of the device during an intense sport tests exercise in an aquatic environment to establish whether users can respond to commands from the device. The first study uses 20 different tactile symbols, each symbol using a unique combination of one or more of the device actuators. The second study uses six best performing symbols from the first study. The paper ends by presenting the results of the exploratory and usability study and discusses their findings. [ABSTRACT FROM AUTHOR]

Introduction: The choice of materials for covering plantar orthoses or wearable insoles is often based on their hardness, breathability, and moisture absorption capacity, although more due to professional preference than clear scientific criteria. An analysis of the thermal response to the use of these materials would provide information about their behavior; hence, the objective of this study was to assess the temperature of three lining materials with different characteristics. Materials and Methods: The temperature of three materials for covering plantar orthoses was analyzed in a sample of 36 subjects (15 men and 21 women, aged 24.6 ± 8.2 years, mass 67.1 ± 13.6 kg, and height 1.7 ± 0.09 m). Temperature was measured before and after 3 h of use in clinical activities, using a polyethylene foam copolymer (PE), ethylene vinyl acetate (EVA), and PE-EVA copolymer foam insole with the use of a FLIR E60BX thermal camera. Results: In the PE copolymer (material 1), temperature increases between 1.07 and 1.85 °C were found after activity, with these differences being statistically significant in all regions of interest (p < 0.001), except for the first toe (0.36 °C, p = 0.170). In the EVA foam (material 2) and the expansive foam of the PE-EVA copolymer (material 3), the temperatures were also significantly higher in all analyzed areas (p < 0.001), ranging between 1.49 and 2.73 °C for EVA and 0.58 and 2.16 °C for PE-EVA. The PE copolymer experienced lower overall overheating, and the area of the fifth metatarsal head underwent the greatest temperature increase, regardless of the material analyzed. Conclusions: PE foam lining materials, with lower density or an open-cell structure, would be preferred for controlling temperature rise in the lining/footbed interface and providing better thermal comfort for users. The area of the first toe was found to be the least overheated, while the fifth metatarsal head increased the most in temperature. This should be considered in the design of new wearables to avoid excessive temperatures due to the lining materials. [ABSTRACT FROM AUTHOR]

Introduction: Diabetic foot ulcers (DFU) are a devastating complication of diabetes. There are numerous challenges with preventing diabetic foot complications and barriers to achieving the care processes suggested in established foot care guidelines. Multi-faceted digital health solutions, which combine multimodal sensing, patient-facing biofeedback, and remote patient monitoring (RPM), show promise in improving our ability to understand, prevent, and manage DFUs. Methods: Patients with a history of diabetic plantar foot ulcers were enrolled in a prospective cohort study and equipped with custom sensory insoles to track plantar pressure, plantar temperature, step count, and adherence data. Sensory insole data enabled patient-facing biofeedback to cue active plantar offloading in response to sustained high plantar pressures, and RPM assessments in response to data trends of concern in plantar pressure, plantar temperature, or sensory insole adherence. Three non-consecutive case participants that ultimately presented with pre-ulcerative lesions (a callus and/or erythematous area on the plantar surface of the foot) during the study were selected for this case series. Results: Across three illustrative patients, continuous plantar pressure monitoring demonstrated promise for empowering both the patient and provider with information for data-driven management of pressure offloading treatments. Conclusion: Multi-faceted digital health solutions can naturally enable and reinforce the integrative foot care guidelines. Multi-modal sensing across multiple physiologic domains supports the monitoring of foot health at various stages along the DFU pathogenesis pathway. Furthermore, digital health solutions equipped with remote patient monitoring unlock new opportunities for personalizing treatments, providing periodic self-care reinforcement, and encouraging patient engagement—key tools for improving patient adherence to their diabetic foot care plan. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor's efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes. [ABSTRACT FROM AUTHOR]

Clinical characteristics of COVID-19 patients have been mostly described in hospitalised patients, yet most are managed in an outpatient setting. The COVID-19 pandemic transformed healthcare delivery models and accelerated the implementation and adoption of telemedicine solutions. We employed a modular remote monitoring system with multi-modal data collection, aggregation, and analytics features to monitor mild COVID-19 patients and report their characteristics and symptoms. At enrolment, the patients were equipped with wearables, which were associated with their accounts, provided the respective in-system consents, and, in parallel, reported the demographics and patient characteristics. The patients monitored their vitals and symptoms daily during a 14-day monitoring period. Vital signs were entered either manually or automatically through wearables. We enrolled 162 patients from February to May 2022. The median age was 51 (42–60) years; 44% were male, 22% had at least one comorbidity, and 73.5% were fully vaccinated. The vitals of the patients were within normal range throughout the monitoring period. Thirteen patients were asymptomatic, while the rest had at least one symptom for a median of 11 (7–16) days. Fatigue was the most common symptom, followed by fever and cough. Loss of taste and smell was the longest-lasting symptom. Age positively correlated with the duration of fatigue, anorexia, and low-grade fever. Comorbidities, the number of administered doses, the days since the last dose, and the days since the positive test did not seem to affect the number of sick days or symptomatology. The i-COVID platform allowed us to provide remote monitoring and reporting of COVID-19 outpatients. We were able to report their clinical characteristics while simultaneously helping reduce the spread of the virus through hospitals by minimising hospital visits. The monitoring platform also offered advanced knowledge extraction and analytic capabilities to detect health condition deterioration and automatically trigger personalised support workflows. [ABSTRACT FROM AUTHOR]

Telerehabilitation systems have the potential to enable therapists to monitor and assist stroke patients in achieving high-intensity upper extremity exercise in the home environment. We adopted an iterative user-centred approach, including multiple data sources and meetings with end-users and stakeholders to define the user requirements for home-based upper extremity rehabilitation using wearable motion sensors for subacute stroke patients. We performed a requirement analysis consisting of the following steps: 1) context & groundwork; 2) eliciting requirements; 3) modelling & analysis; 4) agreeing requirements. During these steps, a pragmatic literature search, interviews and focus groups with stroke patients, physiotherapists and occupational therapists were performed. The results were systematically analysed and prioritised into "must-haves", "should-haves", and "could-haves". We formulated 33 functional requirements: eighteen must-have requirements related to blended care (2), exercise principles (7), exercise delivery (3), exercise evaluation (4), and usability (2); ten should-haves; and five could-haves. Six movement components, including twelve exercises and five combination exercises, are required. For each exercise, appropriate exercise measures were defined. This study provides an overview of functional requirements, required exercises, and required exercise measures for home-based upper extremity rehabilitation using wearable motion sensors for stroke patients, which can be used to develop home-based upper extremity rehabilitation interventions. Moreover, the comprehensive and systematic requirement analysis used in this study can be applied by other researchers and developers when extracting requirements for designing a system or intervention in a medical context. This study provides an extensive overview of user requirements for home-based upper extremity rehabilitation using wearable motion sensors in stroke patients. These requirements can be used as a basis for developing home-based UE telerehabilitation interventions. Including these requirements may facilitate the clinical implementation of such telerehabilitation systems. The comprehensive and systematic approach used in this sudy can be applied by other researchers and developers when extracting requirements for designing a system or intervention in a medical context. [ABSTRACT FROM AUTHOR]

• A treatment's effect on physical function is a fundamental part of its value for patients. • Traditional heart failure symptoms and functional clinical outcome assessments have important limitations that actigraphy might help to overcome. • This work offers comprehensive recommendations for the successful implementation of actigraphy in clinical trials in HF and highlights the need for further research and standardization. Estimation of the effects that drugs or other interventions have on patients' symptoms and functions is crucial in heart failure trials. Traditional symptoms and functions clinical outcome assessments have important limitations. Actigraphy may help to overcome these limitations due to its objective nature and the potential for continuous recording of data. However, actigraphy is not currently accepted as clinically relevant by key stakeholders. In this state-of-the-art study, the key aspects to consider when implementing actigraphy in heart failure trials are discussed. They include which actigraphy-derived measures should be considered, how to build endpoints using them, how to measure and analyze them, and how to handle the patients' and sites' logistics of integrating devices into trials. A comprehensive recommendation based on the current evidence is provided. Actigraphy is technically feasible in clinical trials involving heart failure, but successful implementation and use to demonstrate clinically important differences in physical functioning with drug or other interventions require careful consideration of many design choices. Recommendations about how to implement actigraphy successfully in clinical trials of heart failure. [Display omitted] [ABSTRACT FROM AUTHOR]

In recent years, the adoption of wearable gadgets such as Fitbit has revolutionized the way individuals track and monitor their personal activity data. These devices provide valuable in-sights into an individual's physical activity levels, sleep patterns, and overall health metrics. Integrating this data into healthcare informatics systems can offer significant benefits in terms of personalized healthcare delivery and improved patient outcomes. This paper explores the synergistic integration of Fitbit-generated personal activity data using the openEHR Reference Model in healthcare informatics as a practical case study in patientgenerated health data (PGHD) integration based on health informatics standards as a framework for the representation and exchange of Electronic Health Records (EHRs). The synergistic integration of Fitbit-generated personal activity data through openEHR and FHIR standards models also covers the way for advanced analytics and population health management. By linking and analyzing data from various sources, including sensors and wearable devices, healthcare organizations can identify trends, patterns, and insights that can guide population health strategies, preventive care initiatives, and personalized treatment plans, in addition to aiding physicians in follow-up care. [ABSTRACT FROM AUTHOR]

The assessment of leg asymmetries is gaining scientific interest due to its potential impact on performance and injury development. Athletes around puberty exhibit increased gait variability due to a non-established running pattern. This study aims to describe the asymmetries in the spatiotemporal running parameters in developmentally aged athletes. Forty athletes under 14 (U14) (22 females and 18 males) were assessed running on a treadmill at constant speeds of 12 and 14 km·h−1 for 3 min. Step length, step frequency, along with contact (CT) and flight time, both in absolute values and as a percentage of step time, were recorded using a RunScribe sensor attached to the laces of each shoe. U14 runners exhibited high bilateral symmetry in the spatiotemporal parameters of running, with mean asymmetry values (1–5.7%) lower than the intra-limb coefficient of variation (1.7–9.6%). Furthermore, bilateral asymmetries did not vary between the two speeds. An individual-based interpretation of asymmetries identified subjects with consistent asymmetries at both speeds, particularly in terms of CT and contact ratio (%, CT/step time). This study confirms the high symmetry of pubertal runners and paves the way for the application of portable running assessment technology to detect asymmetries on an individual basis. [ABSTRACT FROM AUTHOR]

Aim: This review aimed to evaluate the effectiveness and feasibility of cancer prehabilitation programs delivered through technological enablers compared to conventional face-to-face interventions. Methods: A systematic review was conducted, searching PubMed, Embase, and CINAHL for studies published from inception to February 6, 2024. Studies were included if they involved adult cancer patients in primary research, utilized technology for prehabilitation, and assessed functional, psychological, and quality of life outcomes. Results: Sixteen studies were included, encompassing wearables, apps, teleprehabilitation, and virtual reality. All studies reported feasibility, but challenges included technical issues, lack of supervision, and non-compliance. Effectiveness depended on intervention rigor and technology type. Wearables offered objective monitoring but faced compliance issues. Videoconferencing provided supervision and could mitigate compliance concerns. Multimodal programs and intervention-specific outcome measures were recommended. Conclusion: Technology-based prehabilitation programs seem feasible, but effectiveness depends on intervention design and technology employed. Future research should focus on developing robust evidence to guide clinical practice and explore the potential of integrated technological solutions. [ABSTRACT FROM AUTHOR]

In recent years, robotic arm exoskeletons have emerged as promising tools, finding widespread application in the rehabilitation of neurological disorders and as assistive devices for everyday activities, even alleviating the physical strain on labor-intensive tasks. Despite the growing prominence of exoskeletons in everyday life, a notable knowledge gap exists in the availability of open-source platforms for classroom-ready usage in educational settings. To address this deficiency, we introduce an open-source educational exoskeleton platform aimed at Science, Technology, Engineering, and Mathematics (STEM) education. This platform represents an enhancement of the commercial EduExo Pro by AUXIVO, tailored to serve as an educational resource for control engineering and biomedical engineering courses. [ABSTRACT FROM AUTHOR]

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People with severe intellectual disabilities (ID) could have difficulty expressing their stress which may complicate timely responses from caregivers. The present study proposes an automatic stress detection system that can work in real-time. The system uses wearable sensors that record physiological signals in combination with machine learning to detect physiological changes related to stress. Four experiments were conducted to assess if the system could detect stress in people with and without ID. Three experiments were conducted with people without ID (n = 14, n = 18, and n = 48), and one observational study was done with people with ID (n = 12). To analyze if the system could detect stress, the performance of random, general, and personalized models was evaluated. The mixed ANOVA found a significant effect for model type, F(2, 134) = 116.50, p <.001. Additionally, the post-hoc t-tests found that the personalized model for the group with ID performed better than the random model, t(11) = 9.05, p <.001. The findings suggest that the personalized model can detect stress in people with and without ID. A larger-scale study is required to validate the system for people with ID. [ABSTRACT FROM AUTHOR]

The objectives of this study were to design, investigate, and compare different designs of coupling circuits for textronic RFID transponders, particularly focusing on magnetic coupling between an antenna and a chip. The configuration of the inductively coupled antenna module and the microelectronic module housing the chip can be varied in several ways. This article explores various geometries of coupling circuits and assesses the effects of altering their dimensions on mutual inductance, chip voltage, and the transponder's read range. The investigation comprised an analytical description of inductive coupling, calculations of mutual inductance and chip voltage based on simulation models of transponders, and laboratory measurements of the read range for selected configurations. The results obtained from this study demonstrate that various designs of textile transponders are capable of achieving satisfactory read ranges, with some configurations extending beyond 10 m. This significant range provides clothing designers with the flexibility to select transponder designs that best meet their specific aesthetic and functional requirements. [ABSTRACT FROM AUTHOR]

With the progress of modern biotechnology and people's strong attention to the quality of life and health, intellectualization and individuation have become a new trend of biomedicine development. Nanogenerators which can collect energy from the surrounding environment and convert mechanical energy into electricity become an ideal choice for self‐powered medical systems. In this review article, two types of nanogenerators, friction triboelectric nanogenerators (TENG), and piezoelectric nanogenerators (PENG), which are commonly used in self‐powered medical systems, are mainly introduced. The working mechanism and operation mode of PENG and TENG are introduced, and the mechanism and research progress of human‐centered intelligent self‐powered medical technologies based on PENG and TENG are focused on in the fields of drug delivery, wound healing, treatment for cardiovascular diseases and tumor, neuromodulation, and so on. In addition, also the opportunities and challenges facing the future development of wearable or implanted nanogenerators are introduced. In this review, it is hoped that a comprehensive overview and clarity will be provided for readers committed to self‐powered medical devices. [ABSTRACT FROM AUTHOR]

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals' overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit. [ABSTRACT FROM AUTHOR]

This paper introduces a new routing and touring service both for outdoor and indoor places of touristic and cultural interest designed to be used in the wider area of Attica, Greece. This service is the result of the work performed in OPTORER (OPTORER: OPtimal rouTing and explOration of touRistic and cultural arEas of interest within Attica given personalized adaptive preferences, promoted underlying purpose, and interactive experience), project, and it aspires to offer a range of innovative and thematic routes to several specified points of interest in the selected area of Attica, encouraging the combination of indoor and outdoor routes in a single tour. The aim is to optimize the user experience while promoting specific, user-centric features, with safety and social welfare being a priority for every designed tour, resulting in enhancing the touristic experience in the area. Using a common smartphone device, as well as common wearable devices (i.e., smartwatches), the OPTORER service will provide an end-to-end solution by developing the algorithms and end-user applications, together with an orchestration platform responsible for managing, operating, and executing the service that produces and presents to the end user results derived from solving dynamically complex optimization problems. [ABSTRACT FROM AUTHOR]

Background: Eating disorders (ED) are serious psychiatric disorders, taking a life every 52 minutes, with high relapse. There are currently no support or effective intervention therapeutics for individuals with an ED in their everyday life. The aim of this study is to build idiographic machine learning (ML) models to evaluate the performance of physiological recordings to detect individual ED behaviors in naturalistic settings. Methods: From an ongoing study (Final N = 120), we piloted the ability for ML to detect an individual's ED behavioral episodes (e.g. purging) from physiological data in six individuals diagnosed with an ED, all of whom endorsed purging. Participants wore an ambulatory monitor for 30 days and tapped a button to denote ED behavioral episodes. We built idiographic (N = 1) logistic regression classifiers (LRC) ML trained models to identify onset of episodes (~600 windows) v. baseline (~571 windows) physiology (Heart Rate, Electrodermal Activity, and Temperature). Results: Using physiological data, ML LRC accurately classified on average 91% of cases, with 92% specificity and 90% sensitivity. Conclusions: This evidence suggests the ability to build idiographic ML models that detect ED behaviors from physiological indices within everyday life with a high level of accuracy. The novel use of ML with wearable sensors to detect physiological patterns of ED behavior pre-onset can lead to just-in-time clinical interventions to disrupt problematic behaviors and promote ED recovery. [ABSTRACT FROM AUTHOR]

Reduced vagally-mediated heart rate variability (HRV) has been associated with anxiety disorders (AD). The aim of this study was to use a wearable device and remote study design to re-evaluate the association of HRV with ADs, anxiety-related traits, and confounders. 240 individuals (AD = 120, healthy controls = 120) completed an at-home assessment of their short-term resting vagally-mediated HRV using a wristband, monitored over videoconference. Following quality control, analyses were performed investigating differences in HRV between individuals with AD (n = 119) and healthy controls (n = 116), associations of HRV with anxiety-related traits and confounders, and antidepressants effects on HRV in patients, including analyses stratified by ancestry (i.e., European, East Asian, African). Among the confounders investigated, only age had a significant association with HRV. Patients with an AD had significantly lower vagally-mediated HRV than healthy controls in the European subsample, with a trend of significance in the whole sample. HRV was significantly associated with the Hamilton Anxiety Rating Scale (HAM-A) but not with antidepressant use in the European subsample. The study measures occurred in a non-standardized at-home setting, and the three ancestry group sample sizes were unequal. This study demonstrates reduced vagally-mediated HRV among patients with ADs compared to healthy controls. Results also point to low HRV being related to more physical anxiety symptoms (measured via HAM-A), suggesting a possible anxiety subtype. Overall, this study highlights the feasibility of using wearables for patients and encourages exploration of the biological and clinical utility of HRV as a risk factor for ADs. • Prior studies show low resting heart rate variability (HRV) is linked to anxiety. • HRV was measured using wearables with a remote study design. • In individuals of European ancestry with anxiety disorders, resting HRV was reduced. • Lower resting HRV was associated with elevated physical anxiety symptoms. • The biology of HRV may aid in understanding the mechanisms underlying anxiety. [ABSTRACT FROM AUTHOR]

The use of wearable devices in conjunction with smartphone applications is at an all-time high and still growing daily. These devices communicate between themselves as well as with remote servers, and research indicates that all these communication bridges create some security and privacy-related issues. This paper will discuss and analyze the difference between a low-end and a high-end wrist wearable device in terms of their security and privacy measures. It will focus the results on what can be obtained from analyzing the Bluetooth communication and the device's official mobile application. [ABSTRACT FROM AUTHOR]

Background: Sleep is a critical component of recovery, but it can be disrupted following prolonged endurance exercise. The objective of this study was to examine the capacity of male and female professional cyclists to recover between daily race stages while competing in the 2022 Tour de France and the 2022 Tour de France Femmes, respectively. The 17 participating cyclists (8 males from a single team and 9 females from two teams) wore a fitness tracker (WHOOP 4.0) to capture recovery metrics related to night-time sleep and autonomic activity for the entirety of the events and for 7 days of baseline before the events. The primary analyses tested for a main effect of 'stage classification'—i.e., rest, flat, hilly, mountain or time trial for males and flat, hilly or mountain for females—on the various recovery metrics. Results: During baseline, total sleep time was 7.2 ± 0.3 h for male cyclists (mean ± 95% confidence interval) and 7.7 ± 0.3 h for female cyclists, sleep efficiency was 87.0 ± 4.4% for males and 88.8 ± 2.6% for females, resting HR was 41.8 ± 4.5 beats·min−1 for males and 45.8 ± 4.9 beats·min−1 for females, and heart rate variability during sleep was 108.5 ± 17.0 ms for males and 119.8 ± 26.4 ms for females. During their respective events, total sleep time was 7.2 ± 0.1 h for males and 7.5 ± 0.3 h for females, sleep efficiency was 86.4 ± 1.2% for males and 89.6 ± 1.2% for females, resting HR was 44.5 ± 1.2 beats·min−1 for males and 50.2 ± 2.0 beats·min−1 for females, and heart rate variability during sleep was 99.1 ± 4.2 ms for males and 114.3 ± 11.2 ms for females. For male cyclists, there was a main effect of 'stage classification' on recovery, such that heart rate variability during sleep was lowest after mountain stages. For female cyclists, there was a main effect of 'stage classification' on recovery, such that the percentage of light sleep (i.e., lower-quality sleep) was highest after mountain stages. Conclusions: Some aspects of recovery were compromised after the most demanding days of racing, i.e., mountain stages. Overall however, the cyclists obtained a reasonable amount of good-quality sleep while competing in these physiologically demanding endurance events. This study demonstrates that it is now feasible to assess recovery in professional athletes during multiple-day endurance events using validated fitness trackers. Key Points: Some aspects of recovery were compromised in male and female cyclists after mountain stages—which are the most demanding days of racing. Overall though, professional cyclists were able to obtain a reasonable amount of good-quality sleep while competing in the Tour de France (for men) and Tour de France Femmes (for women). This study demonstrates that it is now feasible to use validated fitness trackers to assess the night-time recovery of professional athletes during multiple-day endurance events. [ABSTRACT FROM AUTHOR]