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

Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and their physiological or psychological state. This review paper investigates the potential of wearable sensors in VR driving simulators. Methods: A literature search was performed on four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) using appropriate search terms to retrieve scientific articles from a period of eleven years, from 2013 to 2023. Results: After removing duplicates and irrelevant papers, 44 studies were selected for analysis. Some important aspects were extracted and presented: the number of publications per year, countries of publication, the source of publications, study aims, characteristics of the participants, and types of wearable sensors. Moreover, an analysis and discussion of different aspects are provided. To improve car simulators that use virtual reality technologies and boost the effectiveness of particular driver training programs, data from the studies included in this systematic review and those scheduled for the upcoming years may be of interest.

Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privacy, a fundamental right of every human. However, it is challenging to extract potential features from 1D multi-sensor data. Thus, this research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data. Wearable sensor data, particularly accelerator and gyroscope data, act as input signals of different daily activities, and provide potential information using time-frequency analysis. This potential time series information is mapped into spectral images through a process called use of \'scalograms\', derived from the continuous wavelet transform. The deep activity features are extracted from the activity image using deep learning models such as CNN, MobileNetV3, ResNet, and GoogleNet and subsequently classified using a conventional classifier. To validate the proposed model, SisFall and PAMAP2 benchmark datasets are used. Based on the experimental results, this proposed model shows the optimal performance for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2, using Morlet as the mother wavelet with ResNet-101 and a softmax classifier, and outperforms state-of-the-art algorithms.

Wearable electronic sensors have recently attracted tremendous attention in applications such as personal health monitoring, human movement detection, and sensory skins as they offer a promising alternative to counterparts made from traditional metallic conductors and bulky metallic conductors. However, the real-world use of most wearable sensors is often hindered by their limited stretchability and sensitivity, and ultimately, their difficulty to integrate into textiles. To overcome these limitations, wearable sensors can incorporate flexible conductive fibers as electrically active components. In this study, we adopt a scalable wet-spinning approach to directly produce flexible and conductive fibers from aqueous mixtures of Ti3C2Tx MXene and natural rubber (NR). The electrical conductivity and stretchability of these fibers were tuned by varying their MXene loading, enabling knittability into textiles for wearable sensors. As individual filaments, these MXene/NR fibers exhibit suitable conductivity dependence on strain variations, making them ideal for motivating sensors. Meanwhile, textiles from knitted MXene/NR fibers demonstrate great stability as capacitive touch sensors. Collectively, we believe that these elastic and conductive MXene/NR-based fibers and textiles are promising candidates for wearable sensors and smart textiles.

UNASSIGNED: More responsive, reliable, and clinically valid endpoints of disability are essential to reduce size, duration, and burden of clinical trials in adult persons with spinal muscular atrophy (aPwSMA).
UNASSIGNED: The aim is to investigate the feasibility of smartphone-based assessments in aPwSMA and provide evidence on the reliability and construct validity of sensor-derived measures (SDMs) of mobility and manual dexterity collected remotely in aPwSMA.
UNASSIGNED: Data were collected from 59 aPwSMA (23 walkers, 20 sitters and 16 non-sitters) and 30 age-matched healthy controls (HC). SDMs were extracted from five smartphone-based tests capturing mobility and manual dexterity, which were administered in-clinic and remotely in daily life for four weeks. Reliability (Intraclass Correlation Coefficients, ICC) and construct validity (ability to discriminate between HC and aPwSMA and correlations with Revised Upper Limb Module, RULM and Hammersmith Functional Scale - Expanded HFMSE) were quantified for all SDMs.
UNASSIGNED: The smartphone-based assessments proved feasible, with 92.1% average adherence in aPwSMA. The SDMs allowed to reliably assess both mobility and dexterity (ICC > 0.75 for 15/22 SDMs). Twenty-one out of 22 SDMs significantly discriminated between HC and aPwSMA. The highest correlations with the RULM were observed for SDMs from the manual dexterity tests in both non-sitters (Typing, ρ= 0.78) and sitters (Pinching, ρ= 0.75). In walkers, the highest correlation was between mobility tests and HFMSE (5 U-Turns, ρ= 0.79).
UNASSIGNED: This exploratory study provides preliminary evidence for the usability of smartphone-based assessments of mobility and manual dexterity in aPwSMA when deployed remotely in participants\' daily life. Reliability and construct validity of SDMs remotely collected in real-life was demonstrated, which is a pre-requisite for their use in longitudinal trials. Additionally, three novel smartphone-based performance outcome assessments were successfully established for aPwSMA. Upon further validation of responsiveness to interventions, this technology holds potential to increase the efficiency of clinical trials in aPwSMA.

Pursuing high-performance conductive hydrogels is still hot topic in development of advanced flexible wearable devices. Herein, a tough, self-healing, adhesive double network (DN) conductive hydrogel (named as OSA-(Gelatin/PAM)-Ca, O-(G/P)-Ca) was prepared by bridging gelatin and polyacrylamide network with functionalized polysaccharide (oxidized sodium alginate, OSA) through Schiff base reaction. Thanks to the presence of multiple interactions (Schiff base bond, hydrogen bond, and metal coordination) within the network, the prepared hydrogel showed outstanding mechanical properties (tensile strain of 2800 % and stress of 630 kPa), high conductivity (0.72 S/m), repeatable adhesion performance and excellent self-healing ability (83.6 %/79.0 % of the original tensile strain/stress after self-healing). Moreover, the hydrogel-based sensor exhibited high strain sensitivity (GF = 3.66) and fast response time (<0.5 s), which can be used to monitor a wide range of human physiological signals. Based on this, excellent compression sensitivity (GF = 0.41 kPa-1 in the range of 90-120 kPa), a three-dimensional (3D) array of flexible sensor was designed to monitor the intensity of pressure and spatial force distribution. In addition, a gel-based wearable sensor was accurately classified and recognized ten types of gestures, achieving an accuracy rate of >96.33 % both before and after self-healing under three machine learning models (the decision tree, SVM, and KNN). This paper provides a simple method to prepare tough and self-healing conductive hydrogel as flexible multifunctional sensor devices for versatile applications in fields such as healthcare monitoring, human-computer interaction, and artificial intelligence.

BACKGROUND: Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively.
OBJECTIVE: This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system.
METHODS: We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors.
RESULTS: Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution.
CONCLUSIONS: The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.

UNASSIGNED: The number of people with dementia is expected to triple to 152 million in 2050, with 90% having accompanying behavioral and psychological symptoms (BPSD). Agitation is among the most critical BPSD and can lead to decreased quality of life for people with dementia and their caregivers. This study aims to explore objective quantification of agitation in people with dementia by analyzing the relationships between physiological and movement data from wearables and observational measures of agitation.
UNASSIGNED: The data presented here is from 30 people with dementia, each included for 1 week, collected following our previously published multimodal data collection protocol. This observational protocol has a cross-sectional repeated measures design, encompassing data from both wearable and fixed sensors. Generalized linear mixed models were used to quantify the relationship between data from different wearable sensor modalities and agitation, as well as motor and verbal agitation specifically.
UNASSIGNED: Several features from wearable data are significantly associated with agitation, at least the p < .05 level (absolute β: 0.224-0.753). Additionally, different features are informative depending on the agitation type or the patient the data were collected from. Adding context with key confounding variables (time of day, movement, and temperature) allows for a clearer interpretation of feature differences when a person with dementia is agitated.
UNASSIGNED: The features shown to be significantly different, across the study population, suggest possible autonomic nervous system activation when agitated. Differences when splitting the data by agitation type point toward a need for future detection models to tailor to the primary type of agitation expressed. Finally, patient-specific differences in features indicate a need for patient- or group-level model personalization. The findings reported in this study both reinforce and add to the fundamental understanding of and can be used to drive the objective quantification of agitation.

BACKGROUND: Studies examining the joint associations of lifestyle exposures can reveal novel synergistic and joint effects, but no study has examined the joint association of diet and physical activity (PA) with type 2 diabetes (T2D) and hypertension. The aim of this study is to examine the joint associations of PA and diet with incidence of type T2D and hypertension, as a combined outcome and separately in a large sample of UK adults.
METHODS: This prospective cohort study included 144,288 UK Biobank participants aged 40-69. Moderate to vigorous PA (MVPA) was measured using the International Physical Activity Questionnaire and a wrist accelerometer. We categorised PA and diet indicators (diet quality score (DQS) and energy intake (EI)) based on tertiles and derived joint PA and diet variables. Outcome was major cardiometabolic disease incidence (combination of T2D and hypertension).
RESULTS: A total of 14,003(7.1%) participants developed T2D, 28,075(19.2%) developed hypertension, and 30,529(21.2%) developed T2D or hypertension over a mean follow-up of 10.9(3.7) years. Participants with middle and high self-reported MVPA levels had lower risk of major cardiometabolic disease regardless of diet, e.g. among high DQS group, hazard ratios in middle and high MVPA group were 0.90 (95%CI:0.86-0.94), and 0.88(95%CI:0.84-0.92), respectively. Participants with jointly high device-measured MVPA and high DQS levels had lower major cardiometabolic disease risk (HR: 0.84, 95%CI:0.71-0.99). The equivalent joint device-measured MVPA and EI exposure analyses showed no clear pattern of associations with the outcomes.
CONCLUSIONS: Higher PA is an important component in cardiometabolic disease prevention across all diet quality and total EI groups. The observed lack of association between diet health outcomes may stem from a lower DQS.

OBJECTIVE: Recent innovative neurostimulators allow recording local field potentials (LFPs) while performing motor tasks monitored by wearable sensors. Inertial sensors can provide quantitative measures of motor impairment in people with subthalamic nucleus deep brain stimulation. To the best of our knowledge, there is no validated method to synchronize inertial sensors and neurostimulators without an additional device. This study aims to define a new synchronization method to analyze disease-related brain activity patterns during specific motor tasks and evaluate how LFPs are affected by stimulation and medication. Approach: Twelve male subjects treated with subthalamic nucleus deep brain stimulation were recruited to perform motor tasks in four different medication and stimulation conditions. In each condition, a synchronization protocol was performed consisting of taps on the implanted device, which produces artifacts in the LFPs that an inertial sensor can simultaneously record. Main results: In 64% of the recruited subjects, induced artifacts were detected at least once. Among those subjects, 83% of the recordings could be correctly synchronized offline. The remaining recordings were synchronized by video analysis. Significance: The proposed synchronization method does not require an external system and can be easily integrated into clinical practice. The procedure is simple and can be carried out in a short time. A proper and simple synchronization will also be useful to analyze subthalamic neural activity in the presence of specific events (e.g., freezing of gait events) to identify predictive biomarkers. .