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Monitoring health status at home has garnered increasing interest. Therefore, this study investigated the potential feasibility of using noncontact sensors in actual home settings. We searched PubMed for relevant studies published until February 19, 2024, using the keywords "home-based," "home," "monitoring," "sensor," and "noncontact." The studies included in this review involved the installation of noncontact sensors in actual home settings and the evaluation of their performance for health status monitoring. Among the 3 included studies, 2 monitored respiratory status during sleep and 1 monitored body weight and cardiopulmonary physiology. Measurements such as heart rate, respiratory rate, and body weight obtained with noncontact sensors were compared with the results obtained from polysomnography, polygraphy, and commercial scales. All included studies demonstrated that noncontact sensors produced results comparable to those of standard measurement tools, confirming their excellent capability for biometric measurements. Overall, noncontact sensors have sufficient potential for monitoring health status at home.

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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.

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Falls among the elderly population pose significant health risks, often leading to morbidity and decreased quality of life. Traditional fall detection methods, namely wearable devices and cameras, have limitations such as lighting conditions and privacy concerns. Radar-based fall detection has emerged as a promising alternative, offering unobtrusive technique. In this study, an attempt has been made to classify fall detection using smoothed pseudo wigner-ville distribution (SPWVD) images and XGBoost learning. For this, online publicly available radar database (N=15) is considered. Radar signals is employed to SPWVD for time-frequency representation images. Ten features are extracted and applied to XGBoost learning. Experiments are performed and performance is evaluated using 10-fold cross validation. The proposed approach is able to discriminate elderly fall. Using XGBoost learning, the approach yields a maximum average classification accuracy, f1-score, precision, sensitivity, specificity, and kappa scores of 87.47%, 87.38%, 88.12%, 86.81%, 88.31% and 74.94% respectively. The combination of conventional features with concentration measures and median frequency obtained the second best performance. Thus, the proposed framework could be utilized for accurate and efficient detection of falls among the elderly population in their private spaces.

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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.

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With the rise in global life expectancy, ensuring healthier aging experiences for the older population becomes paramount. This scoping review delves into the technologies employed in the remote health monitoring of the elderly over the past 15 years. Exploring the concept of "Healthy Ageing" as proposed by the World Health Organization, this paper attempts to highlight the techniques and technologies used in health monitoring of the elderly population. The integration of wearable sensors in health monitoring presents promising avenues for enhancing healthcare delivery to older adults. However, challenges such as limited digital literacy and privacy concerns persist, necessitating innovative solutions for unobtrusive monitoring. This paper discusses the potential of passive and ambient sensors to address these challenges, offering insights into enhancing the well-being of the older population while preserving their independence and privacy.

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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.

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OBJECTIVE: Evaluate the performance of a custom application developed for tonic-clonic seizure (TCS) monitoring on a consumer-wearable (Apple Watch) device. METHODS: Participants with a history of convulsive epileptic seizures were recruited for either Epilepsy Monitoring Unit (EMU) or ambulatory (AMB) monitoring; participants without epilepsy (normal controls [NC]) were also enrolled in the AMB group. Both EMU and AMB participants wore an Apple Watch with a research app that continuously recorded accelerometer and photoplethysmography (PPG) signals, and ran a fixed-and-frozen tonic-clonic seizure detection algorithm during the testing period. This algorithm had been previously developed and validated using a separate training dataset. All EMU convulsive events were validated by video-electroencephalography (video-EEG); AMB events were validated by caregiver reporting and follow-ups. Device performance was characterized and compared to prior monitoring devices through sensitivity, false alarm rate (FAR; false-alarms per 24 h), precision, and detection delay (latency). RESULTS: The EMU group had 85 participants (4,279 h, 19 TCS from 15 participants) enrolled across four EMUs; the AMB group had 21 participants (13 outpatient, 8 NC, 6,735 h, 10 TCS from 3 participants). All but one AMB participant completed the study. Device performance in the EMU group included a sensitivity of 100 % [95 % confidence interval (CI) 79-100 %]; an FAR of 0.05 [0.02, 0.08] per 24 h; a precision of 68 % [48 %, 83 %]; and a latency of 32.07 s [standard deviation (std) 10.22 s]. The AMB group had a sensitivity of 100 % [66-100 %]; an FAR of 0.13 [0.08, 0.24] per 24 h; a precision of 22 % [11 %, 37 %]; and a latency of 37.38 s [13.24 s]. Notably, a single AMB participant was responsible for 8 of 31 false alarms. The AMB FAR excluding this participant was 0.10 [0.07, 0.14] per 24 h. DISCUSSION: This study demonstrates the practicability of TCS monitoring on a popular consumer wearable (Apple Watch) in daily use for people with epilepsy. The monitoring app had a high sensitivity and a substantially lower FAR than previously reported in both EMU and AMB environments.

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This study formed part of a diagnostic test accuracy study to quantify the ability of three index home monitoring (HM) tests (one paper-based and two digital tests) to identify reactivation in Neovascular age-related macular degeneration (nAMD). The aim of the study was to investigate views about acceptability and explore adherence to weekly HM. Semi-structured interviews were held with 98 patients, family members, and healthcare professionals. A thematic approach was used which was informed by theories of technology acceptance. Various factors influenced acceptability including a patient's understanding about the purpose of monitoring. Training and ongoing support were regarded as essential for overcoming unfamiliarity with digital technology. Findings have implications for implementation of digital HM in the care of older people with nAMD and other long-term conditions.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Upper extremity pain and injury are among the most common musculoskeletal complications manual wheelchair users face. Assessing the temporal parameters of manual wheelchair propulsion, such as propulsion duration, cadence, push duration, and recovery duration, is essential for providing a deep insight into the mobility, level of activity, energy expenditure, and cumulative exposure to repetitive tasks and thus providing personalized feedback. The purpose of this paper is to investigate the use of inertial measurement units (IMUs) to estimate these temporal parameters by identifying the start and end time of hand contact with the push-rim during each propulsion cycle. We presented a model based on data collected from 23 participants (14 males and 9 females, including 9 experienced manual wheelchair users) to guarantee the reliability and generalizability of our method. The obtained outcomes from our IMU-based model were then compared against an instrumented wheelchair (SMARTWheel) as a reference criterion. The results illustrated that our model was able to accurately detect hand contact and hand release and predict temporal parameters, including the push duration and recovery duration in manual wheelchair users, with the mean error ± standard deviation of 10 ± 60 milliseconds and -20 ± 80 milliseconds, respectively. The findings of this study demonstrate the potential of hand-mounted IMUs as a reliable and objective tool for analyzing temporal parameters in manual wheelchair propulsion. IMUs offer significant strides towards inclusivity and accessibility due to their portability and user-friendliness and can democratize health monitoring of manual wheelchair users by making it accessible to a broader range of users compared to traditional technologies.

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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.

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Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

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