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The menstrual cycle is a loop involving the interplay of different organs and hormones, with the capacity to impact numerous physiological processes, including body temperature and heart rate, which in turn display menstrual rhythms. The advent of wearable devices that can continuously track physiological data opens the possibility of using these prolonged time series of skin temperature data to noninvasively detect the temperature variations that occur in ovulatory menstrual cycles. Here, we show that the menstrual skin temperature variation is better represented by a model of oscillation, the cosinor, than by a biphasic square wave model. We describe how applying a cosinor model to a menstrual cycle of distal skin temperature data can be used to assess whether the data oscillate or not, and in cases of oscillation, rhythm metrics for the cycle, including mesor, amplitude, and acrophase, can be obtained. We apply the method to wearable temperature data collected at a minute resolution each day from 120 female individuals over a menstrual cycle to illustrate how the method can be used to derive and present menstrual cycle characteristics, which can be used in other analyses examining indicators of female health. The cosinor method, frequently used in circadian rhythms studies, can be employed in research to facilitate the assessment of menstrual cycle effects on physiological parameters, and in clinical settings to use the characteristics of the menstrual cycles as health markers or to facilitate menstrual chronotherapy.

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundations Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling In-the-Wild. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Purpose: To examine the feasibility and utility of the Fitbit Charge HR to estimate physical activity among ambulatory children and youth with disabilities. Method: Participants (4–17 y old) with disabilities were recruited and asked to wear a Fitbit for 28 days. Feasibility was assessed as the number of participants who adhered to the 28-day protocol. Heat maps were generated to visually examine variability in step count by age, gender, and disability group. Between-group differences for wear time and step counts by age, gender, and disability type were assessed by independent sample t tests for gender and disability group, and a 1-way analysis of variance for age group. Results: Participants (N = 157; median age = 10 y; 71% boys; 71% nonphysical disabilities) averaged 21 valid days of wear time. Wear time was higher in girls than boys (mean difference = 18.0; 95% confidence interval [CI], 6.8 to 29.1), and in preadolescents (mean difference = 27.6; 95% CI, 15.5 to 39.7) and adolescents (mean difference = −21.2; 95% CI, −33.6 to −8.7) than children. More daily steps were taken by boys than girls (mean difference = −1040; 95% CI, −1465 to −615) and individuals with a nonphysical disability than a physical disability (mean difference = −1120; 95% CI, −1474 to −765). The heat maps showed peaks in physical activity on weekdays before school, at recess, lunchtime, and after school. Conclusion: The Fitbit is a feasible tool for monitoring physical activity among ambulatory children and youth with disabilities and may be useful for population-level surveillance and intervention. [ABSTRACT FROM AUTHOR]

Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants wearable device data and participants responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

BackgroundSocial distancing from the COVID-19 pandemic may have decreased engagement in cardiac rehabilitation (CR) and may have had possible consequences on post-CR exercise maintenance. The increased use of technology as an adaptation may benefit post-CR participants via wearables and social media. Thus, we sought to explore the possible relationships of both the pandemic and technology on post-CR exercise maintenance.ObjectiveThis study aimed to (1) understand CR participation during the COVID-19 pandemic, (2) identify perceived barriers and facilitators to physical activity after CR completion, and (3) assess willingness to use technology and social media to support physical activity needs among older adults with cardiovascular disease.MethodsWe recruited participants aged 55 years and older in 3 different CR programs offered at both public and private hospitals in Northern California. We conducted individual interviews on CR experiences, physical activity, and potential for using technology. We used thematic analysis to synthesize the data.ResultsIn total, 22 participants (n=9, 41% female participants; mean age 73, SD 8 years) completed in-depth interviews. Themes from participants' feedback included the following: (1) anxiety and frustration about the wait for CR caused by COVID-19 conditions, (2) positive and safe participant experience once in CR during the pandemic, (3) greater attention needed to patients after completion of CR, (4) notable demand for technology during the pandemic and after completion of CR, and (5) social media networking during the CR program considered valuable if training is provided.ConclusionsIndividuals who completed CR identified shared concerns about continuing physical activity despite having positive experiences during the CR program. There were significant challenges during the pandemic and heightened concerns for safety and health. The idea of providing support by leveraging digital technology (wearable devices and social media for social support) resonated as a potential solution to help bridge the gap from CR to more independent physical activity. More attention is needed to help individuals experience a tailored and safe transition to home to maintain physical activity among those who complete CR.

The ability to deliver drugs with precise dosages at specific time points can significantly improve disease treatment while reducing side effects. Drug encapsulation for gradual delivery has opened the doors for a superior treatment regimen. To expand on this ability, programming bioelectronic devices to deliver small molecules enables ad-hoc personalized therapeutic profiles that are more complex than gradual release. Here, a wearable bioelectronic device with an integrated electrophoretic ion pump that affords on-demand drug delivery with precise dose control is introduced. Delivery of fluoxetine to wounds in mice result in a 27.2% decrease in the macrophage ratio (M1/M2) and a 39.9% increase in re-epithelialization, indicating a shorter inflammatory phase and faster overall healing. Programmable drug delivery using wearable bioelectronics in wounds introduces a broadly applicable strategy for the long-term delivery of a prescribed treatment regimen with minimal external intervention.

In healthcare, blood pH and glucose levels are critical indicators, especially for chronic conditions like diabetes. Although taking blood samples is accurate, it is invasive and unaffordable for many. Wearable sensors offer non‐invasive and continuous detection methods, yet face major challenges, such as high cost, inaccuracies, and complex interpretation. Colorimetric wearable sensors integrated with machine learning (ML) are introduced for accurately detecting pH values and glucose concentrations in sweat. These battery‐free and cost‐effective biosensors, made of cotton textiles, are designed to work seamlessly with smartphones for data collection and automated analysis. A new pH indicator is synthesized with enhanced sensitivity and two types of glucose sensors are developed by depositing enzymatic solutions onto cotton substrates. The sensors' performance is assessed using standard solutions with known pH levels ranging from 4 to 10 and glucose concentrations between 0.03 to 1 mm. The photos captured from these sensors are then analyzed by image processing and three different ML algorithms, achieving an accuracy of 90% in pH and glucose detection. These findings provide effective synthesis methods for textile‐based sweat sensors and demonstrate the significance of employing different ML algorithms for their colorimetric analysis, thus eliminating the need for human intervention in the process. [ABSTRACT FROM AUTHOR]

Background: The aim of the research was to evaluate outdoor aerobic sport activities (OASA) in the physical education (PE) of university students using wearables and their potential to personalize the learning process and enhance motivation. Methods: In total, 368 university students participated. The OASA structure and the key points of application in PE were described. Descriptive statistics of the training units (n = 3,680) were processed. The students recorded their training data in the Strava app (10 sessions per semester), and the data were shared in the online sport community created on the Strava platform. Motivation was evaluated using a questionnaire. The focus was both on intrinsic motivation and extrinsic "ICT" motivation, based on Strava app features and tools. Results: The most preferred outdoor aerobic sport activities were running (58%), cycling (13%), and walking (16%). The results provided insight into motivation and performance analysis. Students' motivation to participate in OASA was mainly in health concerns, such as staying in shape (94%), staying healthy (90%), and psychological concerns, such as having fun (88%), improving state of mind (88%), or relieving stress (83%). In achievement concerns, the motivation was a personal challenge (72%), while competing with others was ranked lowest (32%). The Strava app was a motivating tool for students to record, monitor, and analyze their individual activities and feel "connectedness" to the online sport community. 70% of students were motivated by the non-competitive character of PE, which gave them a personalized opportunity to train without being compared to others. Discussion: The OASA management, with the use of blended learning methods and the Strava app, uses a motivational approach to create, support, and maintain students' healthy habits of physical activity through PE lessons. The need for students to be motivated to exercise can be confirmed in the analysis of the statistical descriptive parameters of running, cycling, and walking. There was a tendency for students to complete only the minimum required distance/time (not more). On the other hand, students enjoyed the training, and 99% of students confirmed that they would enroll again. That fact underlined the importance of motivating students with an effective learning strategy and giving support and guidance. [ABSTRACT FROM AUTHOR]

While the analysis of gait and balance can be an important indicator of age- or disease-related changes, it remains unclear if repeated performance of gait and balance tests in healthy adults leads to habituation effects, if short-term gait and balance training can improve gait and balance performance, and whether the placement of wearable sensors influences the measurement accuracy. Healthy adults were assessed before and after performing weekly gait and balance tests over three weeks by using a force plate, motion capturing system and smartphone. The intervention group (n = 25) additionally received a home-based gait and balance training plan. Another sample of healthy adults (n = 32) was assessed once to analyze the impact of sensor placement (lower back vs. lower abdomen) on gait and balance analysis. Both the control and intervention group exhibited improvements in gait/stance. However, the trends over time were similar for both groups, suggesting that targeted training and repeated task performance equally contributed to the improvement of the measured variables. Since no significant differences were found in sensor placement, we suggest that a smartphone used as a wearable sensor could be worn both on the lower abdomen and the lower back in gait and balance analyses. [ABSTRACT FROM AUTHOR]

Most balance assessment studies using inertial measurement units (IMUs) in smartphones use a body strap and assume the alignment of the smartphone with the anatomical axes. To replace the need for a body strap, we have used an anatomical alignment method that employs a calibration maneuver and Principal Component Analysis (PCA) so that the smartphone can be held by the user in a comfortable position. The objectives of this study were to determine if correlations existed between angular velocity scores derived from a handheld smartphone with PCA functional alignment vs. a smartphone placed in a strap with assumed alignment, and to analyze acceleration score differences across balance poses of increasing difficulty. The handheld and body strap smartphones exhibited moderately to strongly correlated angular velocity scores in the calibration maneuver (r = 0.487–0.983, p < 0.001). Additionally, the handheld smartphone with PCA functional calibration successfully detected significant variance between pose type scores for anteroposterior, mediolateral, and superoinferior acceleration data (p < 0.001). [ABSTRACT FROM AUTHOR]

Vibrotactile feedback can be built into clothing such as vests. This means that often vibrotactile information is presented to the back. It is known that the back has a relatively low spatial acuity. Spatial acuity varies across different limbs and sometimes with different locations on a limb. These known anisotropies suggest that there might be systematic variations in vibrotactile spatial acuity for different areas of the back and also for different orientations (i.e. horizontal vs. vertical). Here we systematically measured spatial acuity in four areas of the back for both horizontal and vertical orientations. The results show no significant differences in spatial acuity for the back areas that were tested. Spatial acuity was, however, higher in the horizontal direction than in the vertical direction by roughly a factor of two. This means that when designing vibrotactile displays for the back the tactor density can be lower in the vertical direction than in the horizontal direction and density should be constant for different areas of the back. [ABSTRACT FROM AUTHOR]

The experience sampling method (ESM) allows for a high degree of ecological validity compared to laboratory research, at the cost of greater effort for participants. It would therefore benefit from implementations that reduce participant effort. In the present paper, we introduce a screenless wrist-worn one-button wearable as an unobtrusive measurement method that can be employed in ESM designs. We developed an open-source Android application to make this commercially available wearable easily configurable and usable. Over the course of six pilot studies, we explored the technical viability (e.g., battery life, reliability of inputs) of this wearable. We compared data quality between wearables and smartphones in a within-subjects design, exploring both the input options of using the number of button presses as a Likert scale, as well as using the angle of the device as a Physical Analogue Scale. Assessments of Extraversion made with either of these methods were highly correlated to comparable assessments made with comparable methods on a smartphone (i.e., Likert scale or a Visual Analogue Scale, respectively). Furthermore, in a preregistered ESM field experiment (N = 134, 4 weeks), we compared compliance to real-life event triggers between wearable devices and smartphones. We found higher numbers of logged events in the wearable group, indicating better adherence to the event-contingent scheduling. Overall, despite the device's minimal capabilities and resulting limitations, one-button wearables can be beneficial for use in ESM designs. [ABSTRACT FROM AUTHOR]

Introduction In today's fast-paced world, the quest for better health has led to the rise of wearable devices. These innovative tools, from smartwatches to activity trackers, offer real-time insights into our physical activity, stress levels, and sleep patterns. By seamlessly integrating into our daily lives, wearables empower individuals to take control of their health and well-being like never before. Aim of the study The present review aims to provide the readers with a broader knowledge of the impact of wearables on health. Materials and methods This article provides a foundation for understanding the significance and potential of wearable devices in promoting health and wellness. Comprehensive literature searches were performed across the main electronic databases of PubMed and Google Scholar for studies published in the English language about use and validation of wearables. Results Wearable devices are effective tools in promoting physical activity, managing stress, and monitoring sleep. With advanced sensors and algorithms, these devices enable users to track their health progress in real-time and provide personalized strategies for improvement. By combining fitness tracking, communication, and stress management capabilities, these devices possess the capacity to empower individuals in maintaining a healthy lifestyle and have become a potential option at the point where technology and personal health meet. As a result, wearable devices have tremendous potential to transform healthcare and enhance quality of life. [ABSTRACT FROM AUTHOR]

Several emerging technologies afford a specific type of interference with privacy which has not yet been conceptualised: inferring information about mental properties of persons on the basis of physiological or biometric signals, in short: mind probing. This paper conceptualises it, analyses several forms and proposes concrete norms that ban and criminalise it. Facial emotion recognition, neuroimaging, or wearable consumer devices such as smartwatches process biometric data to draw inferences about minds; the prevalence and sophistication of such mind probes will soon increase through AI data analysis. This paper explores how human rights law, the EU General Data Protection Regulation and the EU AI Act address mind probes and shows why the current level of protection appears insufficient. This motivates novel norms banning and criminalising non-consensual biometric-based forms of mind reading, ensuring that at least some parts of the mind remain in principle free from non-consensual mind probing. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are a global burden that requires attention. For the detection and diagnosis of CVDs, the 12-lead ECG is a key tool. With technological advancements, ECG devices are becoming smaller and available for home use. Most of these devices contain a limited number of leads and are aimed to detect atrial fibrillation (AF). To investigate whether a four-electrode arrangement could provide enough information to diagnose other CVDs, further research is necessary. At the University Medical Center Utrecht in a multidisciplinary team, we developed the miniECG, a four-electrode ECG handheld system for scientific research in clinical environments (TRL6). This paper describes the process followed during the development of the miniECG. From assembling a multidisciplinary team, which includes engineers, cardiologists, and clinical physicians to the contribution of team members in the design input, design, and testing for safety and functionality of the device. Finally, we detail how the development process was composed by iterative design steps based on user input and intended use evolution. The miniECG is a device compliant for scientific research with patients within Dutch Medical Centers. We believe that hospital-based development led to a streamlined process, which could be applied for the design and development of other technologies used for scientific research in clinical environments. [ABSTRACT FROM AUTHOR]

The rising incidence of type 2 diabetes underscores the need for technological innovations aimed at enhancing diabetes management by aiding individuals in monitoring their dietary intake. This has resulted in the development of technologies capable of tracking the timing and content of an individual's meals. However, the ability to use non-invasive wearables to estimate or classify the carbohydrate content of the food an individual has just consumed remains a relatively unexplored area. This study investigates carbohydrate content classification using postprandial heart rate responses from non-invasive wearables. We designed and developed timeStampr, an iOS application for collecting timestamps essential for data labeling and establishing ground truth. We then conducted a pilot study in controlled, yet naturalistic settings. Data were collected from 23 participants using an Empatica E4 device worn on the upper arm, while each participant consumed either low-carbohydrate or carbohydrate-rich foods. Due to sensor irregularities with dark skin tones and non-compliance with the study's health criteria, we excluded data from three participants. Finally, we configured and trained a Light Gradient Boosting Machine (LGBM) model for carbohydrate content classification. Our classifiers demonstrated robust performance, with the carbohydrate content classification model consistently achieving at least 84% in accuracy, precision, recall, and AUCROC within a 60 s window. The results of this study demonstrate the potential of postprandial heart rate responses from non-invasive wearables in carbohydrate content classification. [ABSTRACT FROM AUTHOR]

High-density electromyography (HD-EMG) can provide a natural interface to enhance human–computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3 ± 0.3 % accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8 ± 0.8 % . When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3 ± 0.9 % on the sequential set and 88.5 ± 0.9 % on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R 2 of 0.884 ± 0.003 in the sequential set and 0.750 ± 0.008 in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of 1.8 ± 0. 04 ∘ and 3.4 ± 0. 07 ∘ for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human–robot collaboration. [ABSTRACT FROM AUTHOR]

Anastomotic leakage (AL) is the leaking of non‐sterile gastrointestinal contents into a patient's abdominal cavity. AL is one of the most dreaded complications following gastrointestinal surgery, with mortality rates reaching up to 27%. The current diagnostic methods for anastomotic leaks are limited in sensitivity and specificity. Since the timing of detection directly impacts patient outcomes, developing new, fast, and simple methods for early leak detection is crucial. Here, a naked eye‐readable, electronic‐free macromolecular network drain fluid sensor is introduced for continuous monitoring and early detection of AL at the patient's bedside. The sensor array comprises three different macromolecular network sensing elements, each tailored for selectivity toward the three major digestive enzymes found in the drainage fluid during a developing AL. Upon digestion of the macromolecular network structure by the respective digestive enzymes, the sensor produces an optical shift discernible to the naked eye. The diagnostic efficacy and clinical applicability of these sensors are demonstrated using clinical samples from 32 patients, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 1.0. This work has the potential to significantly contribute to improved patient outcomes through continuous monitoring and early, low‐cost, and reliable AL detection. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of integrating wearable activity trackers (WATs) into cardiovascular disease care. Despite evidence supporting the use of trackers for monitoring and promoting physical activity, implementation challenges persist in clinical settings. The paper emphasizes the lack of systematic, evidence-based implementation approaches for integrating trackers. It underscores the urgent need for stakeholder collaboration between clinicians, patients, implementation scientists, researchers, health and technology partners, and the use of proven implementation science methodologies. This is crucial for bridging the gap and ensuring effective translation of WATs into cardiovascular care, meeting the increasing demand from patients and clinicians. [ABSTRACT FROM AUTHOR]

Poor pain alleviation remains a problem following orthopedic surgery, leading to prolonged recovery time, increased morbidity, and prolonged opioid use after hospitalization. Wearable device data, collected during postsurgical recovery, may help ameliorate poor pain alleviation because a patient's physiological state during the recovery process may be inferred from sensor data. In this study, we collected smart ring data from 37 inpatients following orthopedic surgery and developed machine learning models to predict if a patient had postsurgical poor pain alleviation. Machine learning models based on the smart ring data were able to predict if a patient had poor pain alleviation during their hospital stay with an accuracy of 70.0%, an F1-score of 0.769, and an area under the receiver operating characteristics curve of 0.762 on an independent test dataset. These values were similar to performance metrics from existing models that rely on static, preoperative patient factors. Our results provide preliminary evidence that wearable device data may help control pain after orthopedic surgery by incorporating real-time, objective estimates of a patient's pain during recovery. [ABSTRACT FROM AUTHOR]

Tai Chi is a Chinese martial art that provides an adaptive and accessible exercise for older adults with varying functional capacity. While Tai Chi is widely recommended for its physical benefits, wider adoption in at-home practice presents challenges for practitioners, as limited feedback may hamper learning. This study examined the feasibility of using a wearable sensor, combined with machine learning (ML) approaches, to automatically and objectively classify Tai Chi expertise. We hypothesized that the combination of wrist acceleration profiles with ML approaches would be able to accurately classify practitioners' Tai Chi expertise levels. Twelve older active Tai Chi practitioners were recruited for this study. The self-reported lifetime practice hours were used to identify subjects in low, medium, or highly experienced groups. Using 15 acceleration-derived features from a wearable sensor during a self-guided Tai Chi movement and 8 ML architectures, we found multiclass classification performance to range from 0.73 to 0.97 in accuracy and F1-score. Based on feature importance analysis, the top three features were found to each result in a 16–19% performance drop in accuracy. These findings suggest that wrist-wearable-based ML models may accurately classify practice-related changes in movement patterns, which may be helpful in quantifying progress in at-home exercises. [ABSTRACT FROM AUTHOR]

Porous conductive polymer structures, in particular Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) structures, are gaining in importance due to their versatile fields of application as sensors, hydrogels, or supercapacitors, to name just a few. Moreover, (porous) conducting polymers have become of interest for wearable and smart textile applications due to their biocompatibility, which enables applications with direct skin contact. Therefore, there is a huge need to investigate distinct, straightforward, and textile-compatible production methods for the fabrication of porous PEDOT:PSS structures. Here, we present novel and uncomplicated approaches to producing diverse porous PEDOT:PSS structures and characterize them thoroughly in terms of porosity, electrical resistance, and their overall appearance. Production methods comprise the incorporation of micro cellulose, the usage of a blowing agent, creating a sponge-like structure, and spraying onto a porous base substrate. This results in the fabrication of various porous structures, ranging from thin and slightly porous to thick and highly porous. Depending on the application, these structures can be modified and integrated into electronic components or wearables to serve as porous electrodes, sensors, or other functional devices. [ABSTRACT FROM AUTHOR]

Spatial computing – that is, a form of human–computer interaction that retains or manipulates referents of real object and spaces – is an increasingly intense focus for Meta. In 2018, Meta launched 'Reality Labs' (RL), a research and development division to oversee the company's production of spatial computing technologies. Drawing on a media historiographical approach from platform studies, this article charts the development of the company's spatial computing ambitions through RL from 2018 to 2022. In so doing, we find that Meta attempts to consolidate complementors through acquisitions, capture policymakers and academics, convene third-party businesses and developers, and expand its ecosystem through enhancing platform programmability. We argue that RL's efforts to grow the platform from within, and through drawing in third-parties, signals an ambition to grow their spatial computing offerings such that they take on a central, infrastructural role in society. [ABSTRACT FROM AUTHOR]

Self-tracking enables people to quantify and measure lifestyle and fitness activities and experiences. Our study focuses on the role of self-tracking in young people's relationship with their body and their lived, 'fleshy' experiences in the social world. We draw on 23 in-depth interviews with young people using a life story approach. Our findings show that self-tracking affords young people to engage in different types of 'body work', to care for and transform their body that is in constant flux by treating it as either a 'private' or 'shared' project. We contribute to ongoing debates about the role of self-tracking in young people's lives by offering a holistic approach that considers the individual and social circumstances that render self-tracking an ongoing, iterative, cumulative and embodied process of discovery, learning and lived and 'fleshy' experience. [ABSTRACT FROM AUTHOR]

Background: Patients undergoing major oncological abdominal surgery are prone to postoperative complications, making early recognition crucial. Clinical deterioration is often preceded by changes in vital signs, which are typically measured thrice a day by a nurse. However, intermittent measurements may delay recognizing clinical deterioration. Continuous vital parameter monitoring may lead to earlier recognition and management of complications and reduce nursing workload. Objective: To compare vital parameter measurements between ward nurses and a wireless continuous monitoring system (Sensium® wireless patch) and assess whether this patch can detect clinical deterioration earlier in patients with complications in the first postoperative week. Methods: Vital parameters (heart rate, respiratory rate, and temperature) were collected in patients undergoing an oncological resection of the liver, colorectal, or pancreas. Sensium® patch measurements were compared to nurses' measurements to assess the percentages of discordant measurements. In patients with complications in the first postoperative week, time discrepancies between nurses and Sensium® patch measurements were identified in cases of clinical deterioration (respiratory rate ≥15/min, heart rate ≥100/min, and temperature ≥38°C). Results: Among 227 patients, 22% of the patients experienced complications. Nurse and Sensium® measurements were discrepant in 586/2272 measurements (26%). In 506/586 discrepancies (86%), this was due to the respiratory rate (difference ≥4/min). Compared to nurses, the Sensium® patch detected an elevated respiratory rate 14 h earlier and heart rate 2 h earlier within complications in the first postoperative week. For temperature, no difference was observed. Conclusion: Continuous monitoring with the Sensium® wireless patch holds promise for earlier recognition of complications in patients who underwent major oncological abdominal surgery. [ABSTRACT FROM AUTHOR]

The increased use of wearables in recent years has fostered a great technological development in this area, although without the appropriate supervision usability may go first than security. In addition to this, the fact that wearables have been requiring more and more personal data from the user makes them attractive devices for an attacker. In this paper we propose a set of tests for evaluating the security and privacy of wearables and we apply them to analyse the security and privacy of a set of commercial wearables that are targeted at minors, who represent a group with especially high requirements in this regard. We define the testing scenario, expose the tools to support the research, and specify the testing process to be followed. Based on the obtained results, although the considered low-end devices are broadly speaking less secure than high-end ones, most of them present security and privacy flaws, which illustrates the necessity of regulation that ensures the fulfilment of appropriate security and privacy requirements. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in bioelectronic devices have stemmed from developments involving nanocomposites. Sepsis, preeclampsia, vascular dementia, and heart disease are serious conditions that will benefit from the continuous monitoring of pulse pressure (PP) and temperature (T) to improve medical insights and care. However, current clinical instruments are bulky, and lacking discreetness, while nanocomposites currently lack measurement accuracy. A thin (<1 mm) electronic skin (e‐skin) is developed utilizing nanocomposite micro‐caviar (MC), diameter (D) ≈290 µm, based on confined silver nanowire (AgNW) networks and food‐grade brown seaweed (BS). MCs are virtually invisible (transmittance >99%) and extremely electromechanically sensitive (gauge factor, G >200). In addition to a considerable temperature coefficient of resistance (TCR) of 4.58 ± 0.95% °C−1 and excellent signal stability, skin‐interfaced devices measured cuff‐less PP and skin T with an accuracy conducive to clinical instruments. [ABSTRACT FROM AUTHOR]

People with intellectual disabilities often show challenging behaviour, which can manifest itself in self-harm or aggression towards others. Real-time monitoring of stress in clients with challenging behaviour can help caregivers to promptly deploy interventions to prevent escalations, ultimately to improve the quality of life of client and caregiver. This study aimed to assess the impact of real-time stress monitoring with HUME, and the subsequent interventions deployed by the care team, on stress levels and quality of life. Real-time stress monitoring was used in 41 clients with intellectual disabilities in a long-term care setting over a period of six months. Stress levels were determined at the start and during the deployment of the stress monitoring system. The quality of life of the client and caregiver was measured with the Outcome Rating Scale at the start and at three months of use. The results showed that the HUME-based interventions resulted in a stress reduction. The perceived quality of life was higher after three months for both the clients and caregivers. Furthermore, interventions to provide proximity were found to be most effective in reducing stress and increasing the client's quality of life. The study demonstrates that real-time stress monitoring with the HUME and the following interventions were effective. There was less stress in clients with an intellectual disability and an increase in the perceived quality of life. Future larger and randomized controlled studies are needed to confirm these findings. Assistive technology such as real-time stress monitoring enables caregivers to timely intervene and contributes to the reduction of challenging behaviour. Real-time stress monitoring contributes to the quality of life of clients and caregivers in healthcare. There is a reduction in the levels of stress of people with an intellectual disability by using stress-monitoring technology. [ABSTRACT FROM AUTHOR]

Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based intensity schema in 84,315 participants (≥ 40 years) with wrist-worn accelerometers. Validity study. Both cadence-based methods (one-level cadence, two-level cadence) calculated intensity-specific time based on cadence-thresholds while the two-level cadence identified stepping behaviors first. We used an overlapping plot, mean absolute error, and Spearman's correlation coefficient to examine agreements between the cadence-based and machine-learning methods. We also evaluated agreements between methods based on practically-important-difference (moderate-to-vigorous-physical activity: ± 20 min/day, moderate-physical activity: ± 15, vigorous-physical activity: ± 2.5, light-physical activity: ± 30). The group-level (median) minutes of moderate-to-vigorous- and moderate-physical activity estimated by one-level cadence were within the range of practically-important-difference compared to the machine-learning method (bias of median: moderate-to-vigorous-physical activity, − 3.5, interquartile range [− 15.8, 12.2]; moderate-physical activity, − 6.0 [− 17.2, 4.1]). The group-level vigorous- and light-physical activity minutes derived by two-level cadence were within practically-important-difference range (vigorous-physical activity: − 0.9 [− 3.1, 0.5]; light-physical activity, − 1.3 [− 28.2, 28.9]). The individual-level differences between the cadence-based and machine learning methods were high across intensities (e.g., moderate-to-vigorous-physical activity: mean absolute error [one-level cadence: 24.2 min/day; two-level cadence: 26.2]), with the proportion of participants within the practically-important-difference ranging from 8.4 % to 61.6 %. One-level cadence showed acceptable group-level estimates of moderate-to-vigorous and moderate-physical activity while two-level cadence showed acceptable group-level estimates of vigorous- and light-physical activity. The cadence-based methods might not be appropriate for individual-level intensity-specific time estimation. [ABSTRACT FROM AUTHOR]

Healthcare Internet of Things (IoT) initiatives that aim to integrate technology and medicine are shaking the sector to its foundations. The revolutionary potential of the proposed strategy is shown here as we investigate the far-reaching consequences of the Healthcare IoT on remote patient monitoring. The beginning sets the stage by underlining the significance of bridging the gap between technology and medicine. Our multi-pronged approach comprises Internet of Things (IoT) remote monitoring, cloud-based analysis, artificial intelligence (AI) integrated diagnostics, real-time alerts, and predictive analytics. Our study's results demonstrate that the proposed approach is superior to the status quo. The area of remote patient monitoring has profited considerably from the employment of traditional approaches, such as the fusion of data from wearable sensors, analysis in the cloud, diagnostics that utilize artificial intelligence, real-time monitoring, predictive modeling, and smart alarm systems. The suggested strategy, however, performs very well across all of the most important measures of assessment. Comparatively, the accuracy rate of the conventional wearable sensor fusion approach was only 76%, whereas our suggested method reached 89%. Our strategy was also more accurate than the standard approach (88% vs. 73%). When compared to the recall rate of 68% produced by conventional methods, our suggested strategy significantly outperformed the competition. It's a great option for hospitals and clinics since it improves diagnostic precision and speed without breaking the bank. [ABSTRACT FROM AUTHOR]

Wearable strain sensors have attracted research interest owing to their potential within digital healthcare, offering smarter tracking, efficient diagnostics, and lower costs. Unlike rigid sensors, fiber‐based ones compete with their flexibility, durability, adaptability to body structures as well as eco‐friendliness to environment. Here, the sustainable fiber‐based wearable strain sensors for digital health are reviewed, and material, fabrication, and practical healthcare aspects are explored. Typical strain sensors predicated on various sensing modalities, be it resistive, capacitive, piezoelectric, or triboelectric, are explained and analyzed according to their strengths and weaknesses toward fabrication and applications. The applications in digital healthcare spanning from body area sensing networks, intelligent health management, and medical rehabilitation to multifunctional healthcare systems are also evaluated. Moreover, to create a more complete digital health network, wired and wireless methods of data collection and examples of machine learning are elaborated in detail. Finally, the prevailing challenges and prospective insights into the advancement of novel fibers, enhancement of sensing precision and wearability, and the establishment of seamlessly integrated systems are critically summarized and offered. This endeavor not only encapsulates the present landscape but also lays the foundation for future breakthroughs in fiber‐based wearable strain sensor technology within the domain of digital health. [ABSTRACT FROM AUTHOR]

Greater loneliness as well as a lack of social connectedness have often been associated with poorer sleep. However, the temporal dynamics and direction of these associations remain unclear. Aim of the current study was to examine bi-directional associations between loneliness/social connectedness and sleep in 48 stress-exposed medical students during their first medical internship, considered a period of heightened stress. We obtained trait-level questionnaire data on loneliness and global sleep completed before and during the internship as well as state-level diary- and wearable-based data on daily changes in social connectedness and sleep collected twice over the period of seven consecutive days, once before and once during the internship. Bi-directional associations among greater loneliness and higher daytime dysfunction on trait-level were identified. In addition, several uni-directional associations between loneliness/social connectedness and sleep were found on trait- and state-level. In sum, findings of this study point at a bi-directional relation among loneliness/social connectedness and sleep, in which variables seem to reciprocally influence each other across longer-term periods as well as on a day-to-day basis. [ABSTRACT FROM AUTHOR]

Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction. Machine learning and artificial intelligence are data-driven techniques integrated with neurodiagnostic monitoring technologies that attempt to accomplish both of those tasks. In this narrative review, we describe both the existing software and hardware approaches for seizure detection and forecasting, as well as the concepts for how to evaluate the performance of new technologies for future application in clinical practice. These technologies include long-term monitoring both with and without electroencephalography (EEG) that report very high sensitivity as well as reduced false positive detections. In addition, we describe the implications of seizure detection and forecasting upon the evaluation of novel treatments for seizures within clinical trials. Based on these existing data, long-term seizure detection and forecasting with machine learning and artificial intelligence could fundamentally change the clinical care of people with seizures, but there are multiple validation steps necessary to rigorously demonstrate their benefits and costs, relative to the current standard. [ABSTRACT FROM AUTHOR]

Ulnar collateral ligament (UCL) tears occur due to the prolonged exposure and overworking of joint stresses, resulting in decreased strength in the flexion and extension of the elbow. Current rehabilitation approaches for UCL tears involve subjective assessments (pain scales) and objective measures such as monitoring joint angles and range of motion. The main goal of this study is to find out if using wearable near-infrared spectroscopy technology can help measure digital biomarkers like muscle oxygen levels and heart rate. These measurements could then be applied to athletes who have been injured. Specifically, measuring muscle oxygen levels will help us understand how well the muscles are using oxygen. This can indicate improvements in how the muscles are healing and growing new blood vessels after reconstructive surgery. Previous research studies demonstrated that there remains an unmet clinical need to measure biomarkers to provide continuous, internal data on muscle physiology during the rehabilitation process. This study's findings can benefit team physicians, sports scientists, athletic trainers, and athletes in the identification of biomarkers to assist in clinical decisions for optimizing training regimens for athletes that perform overarm movements; the research suggests pathways for possible earlier detection, and thus earlier intervention for injury prevention. [ABSTRACT FROM AUTHOR]

Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut's Elitex fiber outperformed Shieldex's fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm. [ABSTRACT FROM AUTHOR]

Smartwatches are one of the most relevant fitness trends of the past two decades, and they collect increasing amounts of health and movement data. The accuracy of these data may be questionable and requires further investigation. Therefore, the aim of the present study is to validate smartwatches for use in triathlon training. Ten different smartwatches were tested for accuracy in measuring heart rates, distances (via global navigation satellite systems, GNSSs), swim stroke rates and the number of swim laps in a 50 m Olympic-size pool. The optical heart rate measurement function of each smartwatch was compared to that of a chest strap. Thirty participants (15 females, 15 males) ran five 3 min intervals on a motorised treadmill to evaluate the accuracy of the heart rate measurements. Moreover, for each smartwatch, running and cycling distance tracking was tested over six runs of 4000 m on a 400 m tartan stadium track, six hilly outdoor runs over 3.4 km, and four repetitions of a 36.8 km road bike course, respectively. Three swimming protocols ranging from 200 m to 400 m were performed in triplicate in a 50 m Olympic-size pool, evaluating the tracked distance and the detected number of strokes. The mean absolute percentage errors (MAPEs) for the average heart rate measurements varied between 3.1% and 8.3%, with the coefficient of determination ranging from 0.22 to 0.79. MAPE results ranged from 0.8% to 12.1% for the 4000 m run on the 400 m track, from 0.2% to 7.5% for the 3.4 km outdoor run, and from 0.0% to 4.2% for the 36.8 km bike ride. For the swimming tests, in contrast, the deviations from the true distance varied greatly, starting at a 0.0% MAPE for the 400 m freestyle and reaching 91.7% for the 200 m medley with style changes every 25 m. In summary, for some of the smartwatches, the measurement results deviated substantially from the true values. Measurements taken while road cycling over longer distances with only a few curves were in relative terms more accurate than those taken during outdoor runs and even more accurate than those taken on the 400 m track. In the swimming exercises, the accuracy of the measured distances was severely deteriorated by the medley changes among the majority of the smartwatches. Altogether, the results of this study should help in assessing the accuracy and thus the suitability of smartwatches for general triathlon training. [ABSTRACT FROM AUTHOR]

Electronic textiles (e-textiles) are a branch of wearable technology based on integrating smart systems into textile materials creating different possibilities, transforming industries, and improving individuals' quality of life. E-textiles hold vast potential, particularly for use in personal protective equipment (PPE) by embedding sensors and smart technologies into garments, thus significantly enhancing safety and performance. Although this branch of research has been active for several decades now, only a few products have made it to the market. Achieving durability, reliability, user acceptance, sustainability, and integration into current manufacturing processes remains challenging. High levels of reliability and user acceptance are critical for technical textiles, such as those used in PPE. While studies address washing reliability and field tests, they often overlook end user preferences regarding smart textiles. This paper presents a narrow fabric-based e-textile system co-developed by engineers, garment and textiles' manufacturers, and firefighters. It highlights material choices and integration methods, and evaluates the system's reliability, sustainability, and user experience, providing comprehensive insights into developing and analyzing e-textile products, particularly in the PPE field. [ABSTRACT FROM AUTHOR]

Growing evidence suggests that respiratory frequency (fR) is a valid marker of effort during high-intensity exercise, including sports of an intermittent nature, like soccer. However, very few attempts have been made so far to monitor fR in soccer with unobtrusive devices. This study assessed the validity of three strain-based commercial wearable devices measuring fR during soccer-specific movements. On two separate visits to the soccer pitch, 15 players performed a 30 min validation protocol wearing either a ComfTech® (CT) vest or a BioharnessTM (BH) 3.0 strap and a Tyme WearTM (TW) vest. fR was extracted from the respiratory waveform of the three commercial devices with custom-made algorithms and compared with that recorded with a reference face mask. The fR time course of the commercial devices generally resembled that of the reference system. The mean absolute percentage error was, on average, 7.03% for CT, 8.65% for TW, and 14.60% for BH for the breath-by-breath comparison and 1.85% for CT, 3.27% for TW, and 7.30% for BH when comparison with the reference system was made in 30 s windows. Despite the challenging measurement scenario, our findings show that some of the currently available wearable sensors are indeed suitable to unobtrusively measure fR in soccer. [ABSTRACT FROM AUTHOR]

Remote Patient Monitoring has enjoyed strong growth to new heights driven by several factors, such as the COVID-19 pandemic or advances in technology, allowing consumers and patients to continuously record health data by themselves. This does not come without its challenges, however. A literature review was completed and highlights usability gaps when using wearables or home use medical devices in a virtual environment. Based on these findings, the Pi-CON methodology was applied to close these gaps by utilizing a novel sensor that allows the acquisition of vital signs at a distance, without any sensors touching the patient. Pi-CON stands for passive, continuous and non-contact, and describes the ability to acquire vital signs continuously and passively, with limited user interaction. The preference of vital sign acquisition with a newly developed sensor was tested and compared to vital sign tests taken with patient generated health-data devices (ear thermometer, pulse oximeter) measuring heart rate, respiratory rate and body temperature. In addition, the amount of operator errors and the user interfaces were tested and compared. Results show that participants preferred vital signs acquisition with the novel sensor and the developed user interface of the sensor. Results also revealed that participants had a mean error of.85 per vital sign measurement with the patient-generated health data devices and.33 with the developed sensor, confirming the beneficial impact available when using the developed sensor based on the Pi-CON methodology. [ABSTRACT FROM AUTHOR]

Background Objective Methods Results Conclusion Speech dysfunction represents one of the initial motor manifestations to develop in Parkinson's disease (PD) and is measurable through smartphone.The aim was to develop a fully automated and noise‐resistant smartphone‐based system that can unobtrusively screen for prodromal parkinsonian speech disorder in subjects with isolated rapid eye movement sleep behavior disorder (iRBD) in a real‐world scenario.This cross‐sectional study assessed regular, everyday voice call data from individuals with iRBD compared to early PD patients and healthy controls via a developed smartphone application. The participants also performed an active, regular reading of a short passage on their smartphone. Smartphone data were continuously collected for up to 3 months after the standard in‐person assessments at the clinic.A total of 3525 calls that led to 5990 minutes of preprocessed speech were extracted from 72 participants, comprising 21 iRBD patients, 26 PD patients, and 25 controls. With a high area under the curve of 0.85 between iRBD patients and controls, the combination of passive and active smartphone data provided a comparable or even more sensitive evaluation than laboratory examination using a high‐quality microphone. The most sensitive features to induce prodromal neurodegeneration in iRBD included imprecise vowel articulation during phone calls (P = 0.03) and monopitch in reading (P = 0.05). Eighteen minutes of speech corresponding to approximately nine calls was sufficient to obtain the best sensitivity for the screening.We consider the developed tool widely applicable to deep longitudinal digital phenotyping data with future applications in neuroprotective trials, deep brain stimulation optimization, neuropsychiatry, speech therapy, population screening, and beyond. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

While social robots show promise for therapeutic interventions, accurate assessments of (vulnerable) participants' affective outcomes require attention. The careful selection of devices for recording autonomic processes in response to stress-inducing and relaxing exercises is essential to ensure data quality recordings and participants' comfort. This foundational study assessed two commonly utilized devices to record electrodermal activity (EDA), indexed through skin conductance, concerning their sensitivity to stress-relaxation manipulations and social validity during a robot intervention: a sock with a Shimmer device and a wrist-worn Empatica E4. We aimed to select the most sensitive and easy-to-wear one as a precursor to a larger intervention study featuring mindfulness-based relaxation exercises delivered by an NAO robot. The findings, based on 28 healthy Dutch-speaking adult volunteers wearing both devices, revealed sensitivity in detecting EDA variations in arousal following stressful (increase) and Robot-delivered mindfulness-based relaxation (decrease) exercises, further corroborated by self-reports. Bland-Altman results suggested little agreement between the two devices and lower sensitivity for the Empatica E4. No statistically significant differences concerning wearing comfort between the Empatica E4 and the Shimmer devices emerged. Although both devices independently showed sensitivity to stress/relaxation manipulation, the choice for one or the other should be informed by the activities in the intervention. [ABSTRACT FROM AUTHOR]

Soft robots and wearable technologies benefit significantly from stretchable batteries, yet the rigid nature of high‐capacity electrodes creates large trade‐offs in battery performance and stretchability. This study introduces a new approach for realizing stretchable batteries by allowing the electrodes to slide along a stretchable electrolyte. When the sliding‐electrodes battery is stretched, the forces are transmitted through the hydrogel electrolyte and elastomeric enclosure, while the rigid electrodes slide relative to the hydrogel to maintain interfacial contact. The sliding‐electrodes approach allows 100% of the unstretched battery area to be covered by thick electrodes so that the battery areal capacity and power are improved by up to 10X of prior stretchable designs. Three metal‐air batteries achieve areal capacities of up to 104 mWh cm−2. Further mechanical testing, electrochemical characterization, and integration into soft robotic systems demonstrate the potential of these stretchable batteries in practical applications. The sliding‐electrodes battery can stably power multiple servo motors and sensing circuits under stretching, twisting, bending, and after impact. [ABSTRACT FROM AUTHOR]

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

Wearables like the Apple Watch, Fitbit Charge 6, Garmin vivoactive 4S, and the Samsung Galaxy Sport are popular devices that tell time, monitor sleep, count calories, and track steps. These devices are increasingly expanding their features to include a more specialized focus on fitness goals, health monitoring and management, and promoting physical activity in various ways. The question is: What is their impact on increasing physical activity? Comparing the features and costs of the most popular wearables helps determine whether users should invest in these devices. [ABSTRACT FROM AUTHOR]

The capability to record data in passive, image-based wearable sensors can simplify data readouts and eliminate the requirement for the integration of electronic components on the skin. Here, we developed a skin-strain-actuated microfluidic pump (SAMP) that utilizes asymmetric aspect ratio channels for the recording of human activity in the fluidic domain. An analytical model describing the SAMP's operation mechanism as a wearable microfluidic device was established. Fabrication of the SAMP was achieved using soft lithography from polydimethylsiloxane (PDMS). Benchtop experimental results and theoretical predictions were shown to be in good agreement. The SAMP was mounted on human skin and experiments conducted on volunteer subjects demonstrated the SAMP's capability to record human activity for hundreds of cycles in the fluidic domain through the observation of a stable liquid meniscus. Proof-of-concept experiments further revealed that the SAMP could quantify a single wrist activity repetition or distinguish between three different shoulder activities. [ABSTRACT FROM AUTHOR]

AbstractVibrotactile feedback is known to have a positive impact on the user performance and experience of touchscreen input. Unfortunately, not all touchscreen devices have built-in haptics capabilities, public displays being a notable example, while the design options for vibrotactile feedback on conventional mobile devices, such as smartphones and tablets, are still limited. In this work, we propose leveraging commodity wearable devices, such as smartwatches, rings, and armbands, to enable vibrotactile feedback as closely as possible to the touch point when the touchscreen does not natively support haptics, and to complement existing haptics capabilities when the touchscreen already features them. To this end, we introduce Wear + Touch, a set of four interaction techniques designed to augment touchscreen input with vibrations delivered through wearable devices, from substituting to complementing, extending, and expanding on-screen vibrotactile feedback. We demonstrate Wear + Touch with several practical implementations involving a large touchscreen display, smartphone, tablet, smartwatch, smart ring, and armband as well as a custom wearable device that we designed for vibrations delivered simultaneously on the user’s index finger, wrist, and forearm. We also report empirical results from two controlled experiments involving a total of 40 participants, conducted to understand the multi-faceted user experience of vibrotactile feedback delivered through the Wear + Touch techniques. In the first experiment, we reveal a favorable user experience of vibrations delivered on the index finger, wrist, and forearm, in this order, when the touchscreen lacks haptics capabilities. In the second experiment, we reveal benefits of reinforcing and expanding on-screen vibrations with vibrotactile feedback on the index finger, wrist, and arm, respectively. We use our findings to suggest future work opportunities for combined vibrotactile feedback involving both touchscreens and wearable devices. [ABSTRACT FROM AUTHOR]

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Digital measures and digital health-care delivery have been rarely implemented in lower-and-middle-income countries (LMICs), contributing to worsening global disparities and inequities. Sustainable ways to implement and use digital approaches will help to improve time to access, management, and quality of life in persons with epilepsy, goals that remain unreachable in under-resourced communities. As under-resourced environments differ in human and economic resources, no one approach will be appropriate to all LMICs. Digital health and tools to monitor and measure digital endpoints and metrics of quality of life will need to be developed or adapted to the specific needs of under-resourced areas. Portable technologies may partially address the urban-rural divide. Careful delineation of stakeholders and their engagement and alignment in all efforts is critically important if these initiatives are to be successfully sustained. Privacy issues, neglected in many regions globally, must be purposefully addressed. Epilepsy care in under-resourced environments has been limited by the lack of relevant technologies for diagnosis and treatment. Digital biomarkers, and investigative technological advances, may finally make it feasible to sustainably improve care delivery and ultimately quality of life including personalized epilepsy care. [ABSTRACT FROM AUTHOR]