Continuous Monitoring by a Wearable Sensor Did Not Enhance Discharge Decision-Making in an Acute Admission Ward: Results of a Randomized Controlled Trial.

Adding continuous monitoring to usual care at an acute admission ward did not have an effect on the proportion of patients safely discharged. Implementation challenges of continuous monitoring may have contributed to the lack of effect observed.

Towards Automatic Migraine-Event Prediction Using Continuous Sensor Data.

Migraine is a chronic headache disorder. A prototype mobile app-based system was implemented to test data collection and improve data coverage for the Empatica E4 biometric sensor device. Results from the prototype testing are reported. Future iteration of the app will be tested with patients with migraine to predict events and potentially reduce event duration and therefore disease burden.

Optimization of activity-driven event detection for long-term ambulatory urodynamics.

Lower urinary tract dysfunction (LUTD) is a debilitating condition that affects millions of individuals worldwide, greatly diminishing their quality of life. The use of wireless, catheter-free implantable devices for long-term ambulatory bladder monitoring, combined with a single-sensor system capable of detecting various bladder events, has the potential to significantly enhance the diagnosis and treatment of LUTD. However, these systems produce large amounts of bladder data that may contain physiological noise in the pressure signals caused by motion artifacts and sudden movements, such as coughing or laughing, potentially leading to false positives during bladder event classification and inaccurate diagnosis/treatment. Integration of activity recognition (AR) can improve classification accuracy, provide context regarding patient activity, and detect motion artifacts by identifying contractions that may result from patient movement. This work investigates the utility of including data from inertial measurement units (IMUs) in the classification pipeline, and considers various digital signal processing (DSP) and machine learning (ML) techniques for optimization and activity classification. In a case study, we analyze simultaneous bladder pressure and IMU data collected from an ambulating female Yucatan minipig. We identified 10 important, yet relatively inexpensive to compute signal features, with which we achieve an average 91.5% activity classification accuracy. Moreover, when classified activities are included in the bladder event analysis pipeline, we observe an improvement in classification accuracy, from 81% to 89.0%. These results suggest that certain IMU features can improve bladder event classification accuracy with low computational overhead.Clinical Relevance: This work establishes that activity recognition may be used in conjunction with single-channel bladder event detection systems to distinguish between contractions and motion artifacts for reducing the incorrect classification of bladder events. This is relevant for emerging sensors that measure intravesical pressure alone or for data analysis of bladder pressure in ambulatory subjects that contain significant abdominal pressure artifacts.

Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

Apple Watch 6 vs. Galaxy Watch 4: A Validity Study of Step-Count Estimation in Daily Activities.

The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions.

Automated Detection of In-Home Activities with Ultra-Wideband Sensors.

As Canada's population of older adults rises, the need for aging-in-place solutions is growing due to the declining quality of long-term-care homes and long wait times. While the current standards include questionnaire-based assessments for monitoring activities of daily living (ADLs), there is an urgent need for advanced indoor localization technologies that ensure privacy. This study explores the use of Ultra-Wideband (UWB) technology for activity recognition in a mock condo in the Glenrose Rehabilitation Hospital. UWB systems with built-in Inertial Measurement Unit (IMU) sensors were tested, using anchors set up across the condo and a tag worn by patients. We tested various UWB setups, changed the number of anchors, and varied the tag placement (on the wrist or chest). Wrist-worn tags consistently outperformed chest-worn tags, and the nine-anchor configuration yielded the highest accuracy. Machine learning models were developed to classify activities based on UWB and IMU data. Models that included positional data significantly outperformed those that did not. The Random Forest model with a 4 s data window achieved an accuracy of 94%, compared to 79.2% when positional data were excluded. These findings demonstrate that incorporating positional data with IMU sensors is a promising method for effective remote patient monitoring.

Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches.

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting and analyzing data from wearables presents several challenges, such as data entry errors, non-wear periods, missing data, and wearable artifacts. In this work, we explore these data analysis challenges using two real-world datasets (mBrain21 and ETRI lifelog2020). We introduce practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized pipeline for detecting non-wear periods. Additionally, we propose a visualization-oriented approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we present a bootstrapping methodology to evaluate the variability of wearable-derived features in the presence of partially missing data segments. Prioritizing transparency and reproducibility, we provide open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for improving wearable data collection and analysis.

Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations.

OBJECTIVE: This research aims to extract human gait parameters from floor vibrations. The proposed approach provides an innovative methodology on occupant activity, contributing to a broader understanding of how human movements interact within their built environment. METHODS AND PROCEDURES: A multilevel probabilistic model was developed to estimate cadence and walking speed through the analysis of floor vibrations induced by walking. The model addresses challenges related to missing or incomplete information in the floor acceleration signals. Following the Bayesian Analysis Reporting Guidelines (BARG) for reproducibility, the model was evaluated through twenty-seven walking experiments, capturing floor vibration and data from Ambulatory Parkinson's Disease Monitoring (APDM) wearable sensors. The model was tested in a real-time implementation where ten individuals were recorded walking at their own selected pace. RESULTS: Using a rigorous combined decision criteria of 95% high posterior density (HPD) and the Range of Practical Equivalence (ROPE) following BARG, the results demonstrate satisfactory alignment between estimations and target values for practical purposes. Notably, with over 90% of the 95% HPD falling within the region of practical equivalence, there is a solid basis for accepting the estimations as probabilistically aligned with the estimations using the APDM sensors and video recordings. CONCLUSION: This research validates the probabilistic multilevel model in estimating cadence and walking speed by analyzing floor vibrations, demonstrating its satisfactory comparability with established technologies such as APDM sensors and video recordings. The close alignment between the estimations and target values emphasizes the approach's efficacy. The proposed model effectively tackles prevalent challenges associated with missing or incomplete data in real-world scenarios, enhancing the accuracy of gait parameter estimations derived from floor vibrations. CLINICAL IMPACT: Extracting gait parameters from floor vibrations could provide a non-intrusive and continuous means of monitoring an individual's gait, offering valuable insights into mobility and potential indicators of neurological conditions. The implications of this research extend to the development of advanced gait analysis tools, offering new perspectives on assessing and understanding walking patterns for improved diagnostics and personalized healthcare.Clinical and Translational Impact Statement: This manuscript introduces an innovative approach for unattended gait assessments with potentially significant implications for clinical decision-making. By utilizing floor vibrations to estimate cadence and walking speed, the technology can provide clinicians with valuable insights into their patients' mobility and functional abilities in real-life settings. The strategic installation of accelerometers beneath the flooring of homes or care facilities allows for uninterrupted daily activities during these assessments, reducing the reliance on specialized clinical environments. This technology enables continuous monitoring of gait patterns over time and has the potential for integration into healthcare platforms. Such integration can enhance remote monitoring, leading to timely interventions and personalized care plans, ultimately improving clinical outcomes. The probabilistic nature of our model enables uncertainty quantification in the estimated parameters, providing clinicians with a nuanced understanding of data reliability.

[Analysis of the Current Application Status for Clinical and Home Monitoring of Obstructive Sleep Apnea-Hypopnea Syndrome].

The study provides an overview of the development status of sleep disorder monitoring devices. Currently, polysomnography (PSG) is the gold standard for diagnosing sleep disorders, necessitating multiple leads and requiring overnight monitoring in a sleep laboratory, which can be cumbersome for patients. Nevertheless, the performance of PSG has been enhanced through research on sleep disorder monitoring and sleep staging optimization. An alternative device is the home sleep apnea testing (HSAT), which enables patients to monitor their sleep at home. However, HSAT does not attain the same level of accuracy in sleep staging as PSG, rendering it inappropriate for screening individuals with asymptomatic or mild obstructive sleep apnea-hypopnea syndrome (OSAHS). The study suggests that establishing a Chinese sleep staging database and developing home sleep disorder monitoring devices that can serve as alternatives to PSG will represent a future development direction.

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis.

Nocturnal enuresis (NE) is involuntary bedwetting during sleep, typically appearing in young children. Despite the potential benefits of the long-term home monitoring of NE patients for research and treatment enhancement, this area remains underexplored. To address this, we propose NEcare, an in-home monitoring system that utilizes wearable devices and machine learning techniques. NEcare collects sensor data from an electrocardiogram, body impedance (BI), a three-axis accelerometer, and a three-axis gyroscope to examine bladder volume (BV), heart rate (HR), and periodic limb movements in sleep (PLMS). Additionally, it analyzes the collected NE patient data and supports NE moment estimation using heuristic rules and deep learning techniques. To demonstrate the feasibility of in-home monitoring for NE patients using our wearable system, we used our datasets from 30 in-hospital patients and 4 in-home patients. The results show that NEcare captures expected trends associated with NE occurrences, including BV increase, HR increase, and PLMS appearance. In addition, we studied the machine learning-based NE moment estimation, which could help relieve the burdens of NE patients and their families. Finally, we address the limitations and outline future research directions for the development of wearable systems for NE patients.

A Lifestyle Monitoring System for Older Adults Living Independently Using Low-Resolution Smart Meter Data.

BACKGROUND: Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables, ambient sensors, and smart household meters. While wearables can be intrusive, ambient sensors require extra installation, and smart meters are becoming integral to smart city infrastructure. Research Gap: The previous studies primarily utilized high-resolution smart meter data by applying Non-Intrusive Appliance Load Monitoring (NIALM) techniques, leading to significant privacy concerns. Meanwhile, some Japanese power companies have successfully employed low-resolution data to monitor lifestyle patterns discreetly. SCOPE AND METHODOLOGY: This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents' daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people's care.

Continuously monitoring the human machine? - A cross-sectional study to assess the acceptance of wearables in Germany.

Background: Wearables have the potential to transform healthcare by enabling early detection and monitoring of chronic diseases. This study aimed to assess wearables' acceptance, usage, and reasons for non-use. Methods: Anonymous questionnaires were used to collect data in Germany on wearable ownership, usage behaviour, acceptance of health monitoring, and willingness to share data. Results: Out of 643 respondents, 550 participants provided wearable acceptance data. The average age was 36.6 years, with 51.3% female and 39.6% residing in rural areas. Overall, 33.8% reported wearing a wearable, primarily smartwatches or fitness wristbands. Men (63.3%) and women (57.8%) expressed willingness to wear a sensor for health monitoring, and 61.5% were open to sharing data with healthcare providers. Concerns included data security, privacy, and perceived lack of need. Conclusion: The study highlights the acceptance and potential of wearables, particularly for health monitoring and data sharing with healthcare providers. Addressing data security and privacy concerns could enhance the adoption of innovative wearables, such as implants, for early detection and monitoring of chronic diseases.

Estimation of Gait Parameters in Huntington's Disease Using Wearable Sensors in the Clinic and Free-living Conditions.

In Huntington's disease (HD), wearable inertial sensors could capture subtle changes in motor function. However, disease-specific validation of methods is necessary. This study presents an algorithm for walking bout and gait event detection in HD using a leg-worn accelerometer, validated only in the clinic and deployed in free-living conditions. Seventeen HD participants wore shank- and thigh-worn tri-axial accelerometers, and a wrist-worn device during two-minute walk tests in the clinic, with video reference data for validation. Thirteen participants wore one of the thigh-worn tri-axial accelerometers (AP: ActivPAL4) and the wrist-worn device for 7 days under free-living conditions, with proprietary AP data used as reference. Gait events were detected from shank and thigh acceleration using the Teager-Kaiser energy operator combined with unsupervised clustering. Estimated step count (SC) and temporal gait parameters were compared with reference data. In the clinic, low mean absolute percentage errors were observed for stride (shank/thigh: 0.6/0.9%) and stance (shank/thigh: 3.3/7.1%) times, and SC (shank/thigh: 3.1%). Similar errors were observed for proprietary AP SC (3.2%), with higher errors observed for the wrist-worn device (10.9%). At home, excellent agreement was observed between the proposed algorithm and AP software for SC and time spent walking (ICC [Formula: see text]). The wrist-worn device overestimated SC by 34.2%. The presented algorithm additionally allowed stride and stance time estimation, whose variability correlated significantly with clinical motor scores. The results demonstrate a new method for accurate estimation of HD gait parameters in the clinic and free-living conditions, using a single accelerometer worn on either the thigh or shank.

A method for calculating fall risk parameters from discrete stride time series regardless of sensor placement.

BACKGROUND: To complement traditional clinical fall risk assessments, research is oriented towards adding real-life gait-related fall risk parameters (FRP) using inertial sensors fixed to a specific body position. While fixing the sensor position can facilitate data processing, it can reduce user compliance. A newly proposed step detection method, Smartstep, has been proven to be robust against sensor position and real-life challenges. Moreover, FRP based on step variability calculated from stride times (Standard deviation (SD), Coefficient of Variance (Cov), fractal exponent, and sample entropy of stride duration) proved to be useful to prospectively predict the fall risk. RESEARCH QUESTIONS: To evaluate whether Smartstep is convenient for calculating FRP from different sensor placements. METHODS: 29 elderly performed a 6-minute walking test with IMU placed on the waist and the wrist. FRP were computed from step-time estimated from Smartstep and compared to those obtained from foot-mounted inertial sensors: precision and recall of the step detection, Root mean square error (RMSE) and Intraclass Correlation Coefficient (ICC) of stride durations, and limits of agreement of FRP. RESULTS: The step detection precision and recall were respectively 99.5% and 95.9% for the waist position, and 99.4% and 95.7% for the wrist position. The ICC and RMSE of stride duration were 0.91 and 54 ms respectively for both the waist and the hand position. The limits of agreement of Cov, SD, fractal exponent, and sample entropy of stride duration are respectively 2.15%, 25 ms, 0.3, 0.5 for the waist and 1.6%, 16 ms, 0.23, 0.4 for the hand. SIGNIFICANCE: Robust against the elderly's gait and different body locations, especially the wrist, this method can open doors toward ambulatory measurements of steps, and calculation of different discrete stride-related falling risk indicators.

Wearable Motion Analysis System for Thoracic Spine Mobility With Inertial Sensors.

This study presents a wireless wearable portable system designed for the automatic quantitative spatio-temporal analysis of continuous thoracic spine motion across various planes and degrees of freedom (DOF). This includes automatic motion segmentation, computation of the range of motion (ROM) for six distinct thoracic spine movements across three planes, tracking of motion completion cycles, and visualization of both primary and coupled thoracic spine motions. To validate the system, this study employed an Inter-days experimental setting to conduct experiments involving a total of 957 thoracic spine movements, with participation from two representatives of varying age and gender. The reliability of the proposed system was assessed using the Intraclass Correlation Coefficient (ICC) and Standard Error of Measurement (SEM). The experimental results demonstrated strong ICC values for various thoracic spine movements across different planes, ranging from 0.774 to 0.918, with an average of 0.85. The SEM values ranged from 0.64° to 4.03°, with an average of 1.93°. Additionally, we successfully conducted an assessment of thoracic spine mobility in a stroke rehabilitation patient using the system. This illustrates the feasibility of the system for actively analyzing thoracic spine mobility, offering an effective technological means for non-invasive research on thoracic spine activity during continuous movement states.

Predicting the onset of psychotic experiences in daily life with the use of ambulatory sensor data - A proof-of-concept study.

INTRODUCTION: Predictive models of psychotic symptoms could improve ecological momentary interventions by dynamically providing help when it is needed. Wearable sensors measuring autonomic arousal constitute a feasible base for predictive models since they passively collect physiological data linked to the onset of psychotic experiences. To explore this potential, we investigated whether changes in autonomic arousal predict the onset of hallucination spectrum experiences (HSE) and paranoia in individuals with an increased likelihood of experiencing psychotic symptoms. METHOD: For 24 h of ambulatory assessment, 62 participants wore electrodermal activity and heart rate sensors and were provided with an Android smartphone to answer questions about their HSE-, and paranoia-levels every 20 min. We calculated random forests to detect the onset of HSEs and paranoia. The generalizability of our models was tested using leave-one-assessment-out and leave-one-person-out cross-validation. RESULTS: Leave-one-assessment-out models that relied on physiological data and participant ID yielded balanced accuracy scores of 80 % for HSE and 66 % for paranoia. Adding baseline information about lifetime experiences of psychotic symptoms increased balanced accuracy to 82 % (HSE) and 70 % (paranoia). Leave-one-person-out models yielded lower balanced accuracy scores (51 % to 58 %). DISCUSSION: Using passively collectible variables to predict the onset of psychotic experiences is possible and prediction models improve with additional information about lifetime experiences of psychotic symptoms. Generalizing to new individuals showed poor performance, so including personal data from a recipient may be necessary for symptom prediction. Completely individualized prediction models built solely with the data of the person to be predicted might increase accuracy further.

Continuous heart monitoring in patients with pulmonary hypertension smartwatches and direct transmission to their electronic health records: A trial design.

BACKGROUND: Pulmonary hypertension is a progressive disease for which early treatment interventions are essential. Traditionally, patients undergo periodic clinical assessments. However, recent advances in wearable technology could improve the quality and efficiency of follow-up monitoring in patients with pulmonary hypertension. TRIAL DESIGN: To our knowledge, this is the first study describing direct data transmission from a smartwatch to patients' electronic health records. It implements a novel update and customised program to continuously and automatically transmit data from a smartwatch to the patient's electronic healthcare records. It will evaluate continuous monitoring in patients with pulmonary hypertension and monitor their physical activity time, heart rate variability, and heart rate at rest and during physical activity via a smartwatch. It will also evaluate the data transmission method, and its data will be assessed by the treating physicians supplemental to clinical practice. Smartwatch integration promises numerous advantages: comprehensive cardiovascular monitoring and improved patient experience. Our continuous smartwatch monitoring approach offers a solution for earlier detection of clinical worsening and could be included as a combined endpoint in future clinical trials. It could improve patient empowerment, enhance precision medicine, and reduce hospitalisations. The user-friendly smartwatch is designed to minimise disruption in daily life. CONCLUSION: The ability to transfer real-time data from wearable devices to electronic health records could help to transform the treatment of patients with pulmonary hypertension and their follow-up monitoring outside a clinical setting, enhancing the efficiency of healthcare delivery.

COVID-19 Stay-at-home mandates impacts daily ambulatory bout intensity and duration in elementary school-aged children: A wearable sensor based analysis.

INTRODUCTION: SARS COVID-19 pandemic resulted in major changes to how daily life was conducted. Health officials instituted policies to decelerate the spread of the virus, resulting in changes in physical activity patterns of school-aged children. The aim of this study was to utilize a wearable activity monitor to assess ambulatory activity in elementary-school aged children in their home environment during a COVID-19 Stay-at-Home mandate. METHODS: This institutional review board approved research study was performed between April 3rd - May 1st of 2020 during which health officials issued several stay-at-home (shelter-in-place) orders. Participant recruitment was conducted using a convenience sample of 38 typically developing children. Participants wore a StepWatch Activity Monitor for one week and data were downloaded and analyzed to assess global ambulatory activity measures along with ambulatory bout intensity/duration. For comparison purposes, SAM data collected before the pandemic, of a group of 27 age-matched children from the same region of the United States, was included. Statistical analyses were performed comparing SAM variables between children abiding by a stay-at-home mandate (Stay-at-Home) versus the Historical cohort (alpha=0.05). RESULTS: Stay-at-Home cohort took on average 3737 fewer daily total steps compared to the Historical cohort (p<0.001). Daily Total Ambulatory Time (TAT), across all days was significantly lower in the Stay-at-Home cohort compared to the Historical cohort (mean difference: 81.9 minutes, p=0.001). The Stay-at-Home cohort spent a significantly higher percentage of TAT in Easy intensity ambulatory activity (mean difference: 2%, p<0.001) and therefore a significantly lower percentage of TAT in Moderate+ intensity (mean difference: 2%, p<0.001). CONCLUSIONS: The stay-at-home mandates resulted in lower PA levels in elementary school-aged children, beyond global measures to also bout intensity/duration. It appears that in-person school is a major contributor to achieving higher levels of PA and our study provides additional data for policymakers to consider for future decisions.

Feasibility and acceptability of measuring prenatal stress in daily life using smartphone-based ecological momentary assessment and wearable physiological monitors.

High levels of stress during pregnancy can have lasting effects on maternal and offspring health, which disproportionately impacts families facing financial strain, systemic racism, and other forms of social oppression. Developing ways to monitor daily life stress during pregnancy is important for reducing stress-related health disparities. We evaluated the feasibility and acceptability of using mobile health (mHealth) technology (i.e., wearable biosensors, smartphone-based ecological momentary assessment) to measure prenatal stress in daily life. Fifty pregnant women (67% receiving public assistance; 70% Black, 6% Multiracial, 24% White) completed 10 days of ambulatory assessment, in which they answered smartphone-based surveys six times a day and wore a chest-band device (movisens EcgMove4) to monitor their heart rate, heart rate variability, and activity level. Feasibility and acceptability were evaluated using behavioral meta-data and participant feedback. Findings supported the feasibility and acceptability of mHealth methods: Participants answered approximately 75% of the surveys per day and wore the device for approximately 10 hours per day. Perceived burden was low. Notably, participants with higher reported stressors and financial strain reported lower burden associated with the protocol than participants with fewer life stressors, highlighting the feasibility of mHealth technology for monitoring prenatal stress among pregnant populations living with higher levels of contextual stressors. Findings support the use of mHealth technology to measure prenatal stress in real-world, daily life settings, which shows promise for informing scalable, technology-assisted interventions that may help to reduce health disparities by enabling more accessible and comprehensive care during pregnancy.

Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition.

Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable performance in HAR. A pivotal challenge is the trade-off between recognition accuracy and computational efficiency, especially in resource-constrained mobile devices. This challenge necessitates the development of models that enhance feature representation capabilities without imposing additional computational burdens. Addressing this, we introduce a novel HAR model leveraging deep learning, ingeniously designed to navigate the accuracy-efficiency trade-off. The model comprises two innovative modules: 1) Pyramid Multi-scale Convolutional Network (PMCN), which is designed with a symmetric structure and is capable of obtaining a rich receptive field at a finer level through its multiscale representation capability; 2) Cross-Attention Mechanism, which establishes interrelationships among sensor dimensions, temporal dimensions, and channel dimensions, and effectively enhances useful information while suppressing irrelevant data. The proposed model is rigorously evaluated across four diverse datasets: UCI, WISDM, PAMAP2, and OPPORTUNITY. Additional ablation and comparative studies are conducted to comprehensively assess the performance of the model. Experimental results demonstrate that the proposed model achieves superior activity recognition accuracy while maintaining low computational overhead.