Self-Adhesive Electronic Skin with Bio-Inspired 3D Architecture for Mechanical Stimuli Monitoring and Human-Machine Interactions.

Recent development of wearable devices is revolutionizing the way of artificial electronic skins (E-skin), physiological health monitoring and human-machine interactions (HMI). However, challenge remains to fit flexible electronic devices to the human skin with conformal deformation and identifiable electrical feedback according to the mechanical stimuli. Herein, an adhesive E-skin is developed that can firmly attach on the human skin for mechanical stimuli perception. The laser-induced adhesive layer serves as the essential component to ensure the conformal attachment of E-skin on curved surface, which ensures the accurate conversion from mechanical deformation to precise electrical readouts. Especially, the 3D architecture facilitates the non-overlapping outputs that bi-directional joint bending and distinguishes strain/pressure. The optimized E-skin with bio-inspired micro-cilia exhibited significantly improved sensing performances with sensitivity of 0.652 kPa-1 in 0-4 kPa and gauge factor of 8.13 for strain (0-15%) with robustness. Furthermore, the adhesive E-skin can distinguish inward/outward joint bending in non-overlapping behaviors, allowing the establishment of ternary system to expand communication capacity for logic outputs such as effective Morse code and intelligent control. It expects that the adhesive E-skin can serve as a functional bridge between human and electrical terminals for applications from daily mechanical monitoring to efficient HMI.

Exploring Pyrazine-Based Organic Redox Couples for Enhanced Thermoelectric Performance in Wearable Energy Harvesters.

Thermoelectric generators (TEGs) based on thermogalvanic cells can convert low-temperature waste heat into electricity. Organic redox couples are well-suited for wearable devices due to their nontoxicity and the potential to enhance the ionic Seebeck coefficient through functional-group modifications.  Pyrazine-based organic redox couples with different functional groups is comparatively analyzed through cyclic voltammetry under varying temperatures. The results reveal substantial differences in entropy changes with temperature and highlight 2,5-pyrazinedicarboxylic acid dihydrate (PDCA) as the optimal candidate. How the functional groups of the pyrazine compounds impact the ionic Seebeck coefficient is examined, by calculating the electrostatic potential based on density functional theory. To evaluate the thermoelectric properties, PDCA is integrated in different concentrations into a double-network hydrogel comprising poly(vinyl alcohol) and polyacrylamide. The resulting champion device exhibits an impressive ionic Seebeck coefficient (Si) of 2.99 mV K-1, with ionic and thermal conductivities of ≈67.6 µS cm-1 and ≈0.49 W m-1 K-1, respectively. Finally, a TEG is constructed by connecting 36 pieces of 20 × 10-3 m PDCA-soaked hydrogel in series. It achieves a maximum power output of ≈0.28 µW under a temperature gradient of 28.3 °C and can power a small light-emitting diode. These findings highlight the significant potential of TEGs for wearable devices.

Wearable Device with High Thermoelectric Performance and Long-Lasting Usability Based on Gel-Thermocells for Body Heat Harvesting.

Utilizing the thermogalvanic effect, flexible thermoelectric materials present a compelling avenue for converting heat into electricity, especially in the context of wearable electronics. However, prolonged usage is hampered by the limitation imposed on the thermoelectric device's operational time due to the evaporation of moisture. Deep eutectic solvents (DESs) offer a promising solution for low-moisture gel fabrication. In this study, a bacterial cellulose (BC)/polyacrylic acid (PAA)/guanidinium chloride (GdmCl) gel is synthesized by incorporating BC into the DES. High-performance n-type and p-type thermocells (TECs) are developed by introducing Fe(ClO4)2/3 and K3/4Fe(CN)6, respectively. BC enhances the mechanical properties through the construction of an interpenetrating network structure. The coordination of carboxyl groups on PAA with Fe3+ and the crystallization induced by Gdm+ with [Fe(CN)6]4- remarkably improve the thermoelectric performance, achieving a Seebeck coefficient (S) of 2.4 mV K-1 and ion conductivity (σ) of 1.4 S m-1 for the n-type TEC, and ‒2.8 mV K-1 and 1.9 S m-1 for the p-type TEC. A flexible wearable thermoelectric device is fabricated with a S of 82 mV K-1 and it maintains a stable output over one month. This research broadens the application scope of DESs in the thermoelectric field and offers promising strategies for long-lasting wearable energy solutions.

A Self-Sensing and Self-Powered Wearable System Based on Multi-Source Human Motion Energy Harvesting.

Wearable devices play an indispensable role in modern life, and the human body contains multiple wasted energies available for wearable devices. This study proposes a self-sensing and self-powered wearable system (SS-WS) based on scavenging waist motion energy and knee negative energy. The proposed SS-WS consists of a three-degree-of-freedom triboelectric nanogenerator (TDF-TENG) and a negative energy harvester (NEH). The TDF-TENG is driven by waist motion energy and the generated triboelectric signals are processed by deep learning for recognizing the human motion. The triboelectric signals generated by TDF-TENG can accurately recognize the motion state after processing based on Gate Recurrent Unit deep learning model. With double frequency up-conversion, the NEH recovers knee negative energy generation for powering wearable devices. A model wearing the single energy harvester can generate the power of 27.01 mW when the movement speed is 8 km h-1, and the power density of NEH reaches 0.3 W kg-1 at an external excitation condition of 3 Hz. Experiments and analysis prove that the proposed SS-WS can realize self-sensing and effectively power wearable devices.

Acoustic Stimulation to Improve Slow-Wave Sleep in Alzheimer's Disease: A Multiple Night At-Home Intervention.

OBJECTIVES: To investigate the efficacy of closed-loop acoustic stimulation (CLAS) during slow-wave sleep (SWS) to enhance slow-wave activity (SWA) and SWS in patients with Alzheimer's disease (AD) across multiple nights and to explore associations between stimulation, participant characteristics, and individuals' SWS response. DESIGN: A 2-week, open-label at-home intervention study utilizing the DREEM2 headband to record sleep data and administer CLAS during SWS. SETTING AND PARTICIPANTS: Fifteen older patients with AD (6 women, mean age: 76.27 [SD = 6.06], mean MOCA-score: 16.07 [SD = 6.94]), living at home with their partner, completed the trial. INTERVENTION: Patients first wore the device for two baseline nights, followed by 14 nights during which the device was programmed to randomly either deliver acoustic stimulations of 50 ms pink noise (± 40 dB) targeted to the slow-wave up-phase during SWS or only mark the wave (sham). RESULTS: On a group level, stimulation significantly enhanced SWA and SWS with consistent SWS enhancement throughout the intervention. However, substantial variability existed in individual responses to stimulation. Individuals received more stimulations on nights with increased SWS compared to baseline than on nights with no change or a decrease. In individuals, having lower baseline SWS correlated with receiving fewer stimulations on average during the intervention. CONCLUSION: CLAS during SWS is a promising nonpharmacological method to enhance SWA and SWS in AD. However, patients with lower baseline SWS received fewer stimulations during the intervention, possibly resulting in less SWS enhancement. Individual variability in response to stimulation underscores the need to address personalized stimulation parameters in future research and therapy development.

Statistical methods for predicting e-cigarette use events based on beat-to-beat interval (BBI) data collected from wearable devices.

The prevalence of e-cigarette use among young adults in the USA is high (14%). Although the majority of users plan to quit vaping, the motivation to make a quit attempt is low and available support during a quit attempt is limited. Using wearable sensors to collect physiological data (eg, heart rate) holds promise for capturing the right timing to deliver intervention messages. This study aims to fill the current knowledge gap by proposing statistical methods to (1) de-noise beat-to-beat interval (BBI) data from smartwatches worn by 12 young adult regular e-cigarette users for 7 days; and (2) summarize the de-noised data by event and control segments. We also conducted a comprehensive review of conventional methods for summarizing heart rate variability (HRV) and compared their performance with the proposed method. The results show that the proposed singular spectrum analysis (SSA) can effectively de-noise the highly variable BBI data, as well as quantify the proportion of total variation extracted. Compared to existing HRV methods, the proposed second order polynomial model yields the highest area under the curve (AUC) value of 0.76 and offers better interpretability. The findings also indicate that the average heart rate before vaping is higher and there is an increasing trend in the heart rate before the vaping event. Importantly, the development of increasing heart rate observed in this study implies that there may be time to intervene as this physiological signal emerges. This finding, if replicated in a larger scale study, may inform optimal timings for delivering messages in future intervention.

Using Consumer Wearable Devices to Profile Postoperative Complications After Pediatric Appendectomy.

INTRODUCTION: Markers of postoperative recovery in pediatric patients are difficult for parents to evaluate after hospital discharge, who use subjective proxies to assess recovery and the onset of complications. Consumer-grade wearable devices (e.g., Fitbit) generate objective recovery data in near real time and thus may provide an opportunity to remotely monitor postoperative patients and identify complications beyond the initial hospitalization. The aim of this study was to use daily step counts from a Fitbit to compare recovery in patients with complications to those without complications after undergoing appendectomy for complicated appendicitis. METHODS: Children ages 3-17 years old undergoing laparoscopic appendectomy for complicated appendicitis were recruited. Patients wore a Fitbit device for 21 d after operation. After collection, patient data were included in the analysis if minimum wear-time criteria were achieved. Postoperative complications were identified through chart review, and step count trajectories for patients recovering with and without complications were compared. Additionally, to account for the patients experiencing a complication on different postoperative days, median daily step count for pre- and post-complication were analyzed. RESULTS: Eighty-six patients with complicated appendicitis were enrolled in the study, and fourteen children developed a postoperative complication. Three patients were excluded because they did not meet the minimum wear time requirements. Complications were divided into abscesses (n = 7, 64%), surgical site infections (n = 2, 18%), and other, which included small bowel obstruction and Clostridioides difficile infection (n = 2, 18%). Patients presented with a complication on mean postoperative day 8, while deviation from the normative recovery trajectory was evident 4 d prior. When compared to children with normative recovery, the patients with surgical complications experienced a slower increase in step count postoperatively, but the recovery trajectory was specific to each complication type. When corrected for day of presentation with complication, step count remained low prior to the discovery of the complication and increased after treatment resembling the normative recovery trajectory. CONCLUSIONS: This study profiled variations from the normative recovery trajectory in patients with complication after appendectomy for complicated appendicitis, with distinct trajectory patterns by complication type. Our findings have potentially profound clinical implications for monitoring pediatric patients postoperatively, particularly in the outpatient setting, thus providing objective data for potentially earlier identification of complications after hospital discharge.

Continuous timely monitoring of core temperature with two wearable devices in pediatric patients undergoing chemotherapy for cancer - a comparison study.

PURPOSE: Pediatric patients with cancer often develop chemotherapy-induced fever in neutropenia (FN), requiring emergency broad-spectrum antibiotics. Continuous temperature monitoring can lead to earlier FN detection and therapy with improved outcomes. We aimed to compare the feasibility of continuous core temperature monitoring with timely data availability between two wearable devices (WDs) in pediatric oncology patients undergoing chemotherapy. METHODS: In this prospective observational two-center study, 20 patients (median age: 8 years) undergoing chemotherapy simultaneously wore two WDs (CORE®, Everion®) for 14 days. The predefined goal was core temperature recorded in sufficient quality and available within ≤ 30 min during ≥ 18/24 h for ≥ 7/14 days in more than 15 patients. RESULTS: More patients reached the goal with CORE® (n = 13) versus Everion® (n = 3) (difference, 50% p < 0.001). After correcting for the transmission bottleneck caused by two WDs transmitting via one gateway, these numbers increased (n = 15 versus n = 14; difference, 5%; p = 0.69). CORE® measurements corresponded better to ear temperatures (n = 528; mean bias, - 0.07 °C; mean absolute difference, 0.35 °C) than Everion® measurements (n = 532; - 1.06 °C; 1.10 °C). Acceptance rates for the WDs were 95% for CORE® and 89% for Everion®. CONCLUSION: The CORE® fulfilled the predefined feasibility criterion (15 of 20 patients) after correction for transmission bottleneck, and the Everion® nearly fulfilled it. Continuous core temperature recording of good quality and with timely data availability was feasible from preschool to adolescent patients undergoing chemotherapy for cancer. These results encourage the design of randomized controlled trials on continuously monitored core temperature in pediatric patients. CLINICALTRIALS: gov (NCT04914702) on June 7, 2021.

Assessing the current utilization status of wearable devices in clinical research.

BACKGROUND/AIMS: Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations. METHODS: As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords "ActiGraph,""Apple Watch,""Empatica,""Fitbit,""Garmin," and "wearable devices" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables. RESULTS: Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations. CONCLUSIONS: Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future.

Diagnostic accuracy of apple watch ECG outputs in identifying dysrhythmias: A comparison with 12-Lead ECG in emergency department.

BACKGROUND: Wearable devices, particularly smartwatches like the Apple Watch (AW), can record important cardiac information, such as single­lead electrocardiograms (ECGs). Although they are increasingly used to detect conditions such as atrial fibrillation (AF), research on their effectiveness in detecting a wider range of dysrhythmias and abnormal ECG findings remains limited. The primary objective of this study is to evaluate the accuracy of the AW in detecting various cardiac rhythms by comparing it with standard ECG's lead-I. METHODS: This single-center prospective observational study was conducted in a tertiary care emergency department (ED) between 1.10.2023 and 31.10.2023. The study population consisted of all patients assessed in the critical care areas of the ED, all of whom underwent standard 12­lead ECGs for various clinical reasons. Participants in the study were included consecutively. An AW was attached to patients' wrists and an ECG lead-I printout was obtained. Heart rate, rhythm and abnormal findings were evaluated and compared with the lead-I of standard ECG. Two emergency medicine specialists performed the ECG evaluations. Rhythms were categorized as normal sinus rhythm and abnormal rhythms, while ECG findings were categorized as the presence or absence of abnormal findings. AW and 12­lead ECG outputs were compared using the McNemar test. Predictive performance analyses were also performed for subgroups. Bland-Altman analysis using absolute mean differences and concordance correlation coefficients was used to assess the level of heart rate agreement between devices. RESULTS: The study was carried out on 721 patients. When analyzing ECG rhythms and abnormal findings in lead-I, the effectiveness of AW in distinguishing between normal and abnormal rhythms was similar to standard ECGs (p = 0.52). However, there was a significant difference between AW and standard ECGs in identifying abnormal findings in lead-I (p < 0.05). Using Bland-Altman analysis for heart rate assessment, the absolute mean difference for heart rate was 0.81 ± 6.12 bpm (r = 0.94). There was strong agreement in 658 out of 700 (94%) heart rate measurements. CONCLUSION: Our study indicates that the AW has the potential to detect cardiac rhythms beyond AF. ECG tracings obtained from the AW may help evaluate cardiac rhythms prior to the patient's arrival in the ED. However, further research with a larger patient cohort is essential, especially for specific diagnoses.

Three-Day Monitoring of Adhesive Single-Lead Electrocardiogram Patch for Premature Ventricular Complex: Prospective Study for Diagnosis Validation and Evaluation of Burden Fluctuation.

BACKGROUND: Wearable electrocardiogram (ECG) monitoring devices are used worldwide. However, data on the diagnostic yield of an adhesive single-lead ECG patch (SEP) to detect premature ventricular complex (PVC) and the optimal duration of wearing an SEP for PVC burden assessment are limited. OBJECTIVE: We aimed to validate the diagnostic yield of an SEP (mobiCARE MC-100, Seers Technology) for PVC detection and evaluate the PVC burden variation recorded by the SEP over a 3-day monitoring period. METHODS: This is a prospective study of patients with documented PVC on a 12-lead ECG. Patients underwent simultaneous ECG monitoring with the 24-hour Holter monitor and SEP on the first day. On the subsequent second and third days, ECG monitoring was continued using only SEP, and a 3-day extended monitoring was completed. The diagnostic yield of SEP for PVC detection was evaluated by comparison with the results obtained on the first day of Holter monitoring. The PVC burden monitored by SEP for 3 days was used to assess the daily and 6-hour PVC burden variations. The number of patients additionally identified to reach PVC thresholds of 10%, 15%, and 20% during the 3-day extended monitoring by SEP and the clinical factors associated with the higher PVC burden variations were explored. RESULTS: The recruited data of 134 monitored patients (mean age, 54.6 years; males, 45/134, 33.6%) were analyzed. The median daily PVC burden of these patients was 2.4% (IQR 0.2%-10.9%), as measured by the Holter monitor, and 3.3% (IQR 0.3%-11.7%), as measured in the 3-day monitoring by SEP. The daily PVC burden detected on the first day of SEP was in agreement with that of the Holter monitor: the mean difference was -0.07%, with 95% limits of agreement of -1.44% to 1.30%. A higher PVC burden on the first day was correlated with a higher daily (R2=0.34) and 6-hour burden variation (R2=0.48). Three-day monitoring by SEP identified 29% (12/42), 18% (10/56), and 7% (4/60) more patients reaching 10%, 15%, and 20% of daily PVC burden, respectively. Younger age was additionally associated with the identification of clinically significant PVC burden during the extended monitoring period (P=.02). CONCLUSIONS: We found that the mobiCARE MC-100 SEP accurately detects PVC with comparable diagnostic yield to the 24-hour Holter monitor. Performing 3-day PVC monitoring with SEP, especially among younger patients, may offer a pragmatic alternative for identifying more individuals exceeding the clinically significant PVC burden threshold.

Short- and medium-term cumulative effects of traffic-related air pollution on resting heart rate in the elderly: A wearable device study.

BACKGROUND: Epidemiological evidence regarding the association between air pollution and resting heart rate (RHR), a predictor of cardiovascular disease and mortality, is limited and inconsistent. OBJECTIVES: We used wearable devices and time-series analysis to assess the exposure-response relationship over an extended lag period. METHODS: Ninety-seven elderly individuals (>65 years) from the Taipei Basin participated from May to November 2020 and wore Garmin® smartwatches continuously until the end of 2021 for heart rate monitoring. RHR was defined as the daily average of the lowest 30-min heart rate. Air pollution exposure data, covering lag periods from 0 to 60 days, were obtained from nearby monitoring stations. We used distributed lag non-linear models and linear mixed-effect models to assess cumulative effects of air pollution. Principal component analysis was utilized to explore underlying patterns in air pollution exposure, and subgroup analyses with interaction terms were conducted to explore the modification effects of individual factors. RESULTS: After adjusting for co-pollutants in the models, an interquartile range increase of 0.18 ppm in carbon monoxide (CO) was consistently associated with increased RHR across lag periods of 0-1 day (0.31, 95 % confidence interval [CI]: 0.24-0.38), 0-7 days (0.68, 95 % CI: 0.57-0.79), and 0-50 days (1.02, 95 % CI: 0.82-1.21). Principal component analysis identified two factors, one primarily influenced by CO and nitrogen dioxide (NO2), indicative of traffic sources. Increases in the varimax-rotated traffic-related score were correlated with higher RHR over 0-1 day (0.36, 95 % CI: 0.25-0.47), 0-7 days (0.62, 95 % CI: 0.46-0.77), and 0-50 days (1.27, 95 % CI: 0.87-1.67) lag periods. Over a 0-7 day lag, RHR responses to traffic pollution were intensified by higher temperatures (ß = 0.80 vs. 0.29; interaction p-value [P_int] = 0.011). Males (ß = 0.66 vs. 0.60; P_int < 0.0001), hypertensive individuals (ß = 0.85 vs. 0.45; P_int = 0.028), diabetics (ß = 0.96 vs. 0.52; P_int = 0.042), and those with lower physical activity (ß = 0.70 vs. 0.54; P_int < 0.0001) also exhibited stronger responses. Over a 0-50 day lag, males (ß = 0.99 vs. 0.96; P_int < 0.0001), diabetics (ß = 1.66 vs. 0.69; P_int < 0.0001), individuals with lower physical activity (ß = 1.49 vs. 0.47; P_int = 0.0006), and those with fewer steps on lag day 1 (ß = 1.17 vs. 0.71; P_int = 0.029) showed amplified responses. CONCLUSIONS: Prolonged exposure to traffic-related air pollution results in cumulative cardiovascular risks, persisting for up to 50 days. These effects are more pronounced on warmer days and in individuals with chronic conditions or inactive lifestyles.

Modest Effects of Neurofeedback-Assisted Meditation Using a Wearable Device on Stress Reduction: A Randomized, Double-Blind, and Controlled Study.

BACKGROUND: To evaluate the therapeutic effectiveness and safety of a neurofeedback wearable device for stress reduction. METHODS: A randomized, double-blind, controlled study was designed. Participants had psychological stress with depression or sleep disturbances. They practiced either neurofeedback-assisted meditation (n = 20; female, 15 [75.0%]; age, 49.40 ± 11.76 years) or neurofeedback non-assisted meditation (n = 18; female, 11 [61.1%]; age, 48.67 ± 12.90 years) for 12 minutes twice a day for two weeks. Outcome variables were self-reported questionnaires, including the Korean version of the Perceived Stress Scale, Beck Depression Inventory-II, Insomnia Severity Index, Pittsburgh Sleep Quality Index, and State Trait Anxiety Index, quantitative electroencephalography (qEEG), and blood tests. Satisfaction with device use was measured at the final visit. RESULTS: The experimental group had a significant change in PSS score after two weeks of intervention compared with the control group (6.45 ± 0.95 vs. 3.00 ± 5.54, P = 0.037). State anxiety tended to have a greater effect in the experimental group than in the control group (P = 0.078). Depressive mood and sleep also improved in each group, with no significant difference between the two groups. There were no significant differences in stress-related physiological parameters, such as stress hormones or qEEG, between the two groups. Subjective device satisfaction was significantly higher in the experimental group than in the control group (P = 0.008). CONCLUSION: Neurofeedback-assisted meditation using a wearable device can help improve subjective stress reduction compared with non-assisted meditation. These results support neurofeedback as an effective adjunct to meditation for relieving stress. TRIAL REGISTRATION: Clinical Research Information Service Identifier: KCT0007413.

Implementation difficulties and solutions for a smart-clothes assisted home nursing care program for older adults with dementia or recovering from hip fracture.

BACKGROUND: Wearable devices have the advantage of always being with individuals, enabling easy detection of their movements. Smart clothing can provide feedback to family caregivers of older adults with disabilities who require in-home care. METHODS: This study describes the process of setting up a smart technology-assisted (STA) home-nursing care program, the difficulties encountered, and strategies applied to improve the program. The STA program utilized a smart-vest, designed specifically for older persons with dementia or recovering from hip-fracture surgery. The smart-vest facilitated nurses' and family caregivers' detection of a care receiver's movements via a remote-monitoring system. Movements included getting up at night, time spent in the bathroom, duration of daytime immobility, leaving the house, and daily activity. Twelve caregivers of older adults and their care receiver participated; care receivers included persons recovering from hip fracture (n = 5) and persons living with dementia (n = 7). Data about installation of the individual STA in-home systems, monitoring, and technical difficulties encountered were obtained from researchers' reports. Qualitative data about the caregivers' and care receivers' use of the system were obtained from homecare nurses' reports, which were explored with thematic analysis. RESULTS: Compiled reports from the research team identified three areas of difficulty with the system: incompatibility with the home environment, which caused extra hours of manpower and added to the cost of set-up and maintenance; interruptions in data transmissions, due to system malfunctions; and inaccuracies in data transmissions, due to sensors on the smart-vest. These difficulties contributed to frustration experienced by caregivers and care receivers. CONCLUSIONS: The difficulties encountered impeded implementation of the STA home nursing care. Each of these difficulties had their own unique problems and strategies to resolve them. Our findings can provide a reference for future implementation of similar smart-home systems, which could facilitate ease-of-use for family caregivers.

Wearable biofeedback device to assess gait features and improve gait pattern in people with parkinson's disease: a case series.

INTRODUCTION: People with Parkinson's Disease (PD) show abnormal gait patterns compromising their independence and quality of life. Among all gait alterations due to PD, reduced step length, increased cadence, and decreased ground-reaction force during the loading response and push-off phases are the most common. Wearable biofeedback technologies offer the possibility to provide correlated single or multi-modal stimuli associated with specific gait events or gait performance, hence promoting subjects' awareness of their gait disturbances. Moreover, the portability and applicability in clinical and home settings for gait rehabilitation increase the efficiency in the management of PD. The Wearable Vibrotactile Bidirectional Interface (BI) is a biofeedback device designed to extract gait features in real-time and deliver a customized vibrotactile stimulus at the waist of PD subjects synchronously with specific gait phases. The aims of this study were to measure the effect of the BI on gait parameters usually compromised by the typical bradykinetic gait and to assess its usability and safety in clinical practice. METHODS: In this case series, seven subjects (age: 70.4 ± 8.1 years; H&Y: 2.7 ± 0.3) used the BI and performed a test on a 10-meter walkway (10mWT) and a two-minute walk test (2MWT) as pre-training (Pre-trn) and post-training (Post-trn) assessments. Gait tests were executed in random order with (Bf) and without (No-Bf) the activation of the biofeedback stimulus. All subjects performed three training sessions of 40 min to familiarize themselves with the BI during walking activities. A descriptive analysis of gait parameters (i.e., gait speed, step length, cadence, walking distance, double-support phase) was carried out. The 2-sided Wilcoxon sign-test was used to assess differences between Bf and No-Bf assessments (p < 0.05). RESULTS: After training subjects improved gait speed (Pre-trn_No-Bf: 0.72(0.59,0.72) m/sec; Post-trn_Bf: 0.95(0.69,0.98) m/sec; p = 0.043) and step length (Pre-trn_No-Bf: 0.87(0.81,0.96) meters; Post-trn_Bf: 1.05(0.96,1.14) meters; p = 0.023) using the biofeedback during the 10mWT. Similarly, subjects' walking distance improved (Pre-trn_No-Bf: 97.5 (80.3,110.8) meters; Post-trn_Bf: 118.5(99.3,129.3) meters; p = 0.028) and the duration of the double-support phase decreased (Pre-trn_No-Bf: 29.7(26.8,31.7) %; Post-trn_Bf: 27.2(24.6,28.7) %; p = 0.018) during the 2MWT. An immediate effect of the BI was detected in cadence (Pre-trn_No-Bf: 108(103.8,116.7) step/min; Pre-trn_Bf: 101.4(96.3,111.4) step/min; p = 0.028) at Pre-trn, and in walking distance at Post-trn (Post-trn_No-Bf: 112.5(97.5,124.5) meters; Post-trn_Bf: 118.5(99.3,129.3) meters; p = 0.043). SUS scores were 77.5 in five subjects and 80.3 in two subjects. In terms of safety, all subjects completed the protocol without any adverse events. CONCLUSION: The BI seems to be usable and safe for PD users. Temporal gait parameters have been measured during clinical walking tests providing detailed outcomes. A short period of training with the BI suggests improvements in the gait patterns of people with PD. This research serves as preliminary support for future integration of the BI as an instrument for clinical assessment and rehabilitation in people with PD, both in hospital and remote environments. TRIAL REGISTRATION: The study protocol was registered (DGDMF.VI/P/I.5.i.m.2/2019/1297) and approved by the General Directorate of Medical Devices and Pharmaceutical Service of the Italian Ministry of Health and by the ethics committee of the Lombardy region (Milan, Italy).

Effectiveness of mixed reality-based rehabilitation on hands and fingers by individual finger-movement tracking in patients with stroke.

BACKGROUND: Mixed reality (MR) is helpful in hand training for patients with stroke, allowing them to fully submerge in a virtual space while interacting with real objects. The recognition of individual finger movements is required for MR rehabilitation. This study aimed to assess the effectiveness of updated MR-board 2, adding finger training for patients with stroke. METHODS: Twenty-one participants with hemiplegic stroke (10 with left hemiplegia and 11 with right hemiplegia; nine female patients; 56.7 ± 14.2 years of age; and onset of stroke 32.7 ± 34.8 months) participated in this study. MR-board 2 comprised a board plate, a depth camera, plastic-shaped objects, a monitor, a palm-worn camera, and seven gamified training programs. All participants performed 20 self-training sessions involving 30-min training using MR-board 2. The outcome measurements for upper extremity function were the Fugl-Meyer assessment (FMA) upper extremity score, repeated number of finger flexion and extension (Repeat-FE), the thumb opposition test (TOT), Box and Block Test score (BBT), Wolf Motor Function Test score (WMFT), and Stroke Impact Scale (SIS). One-way repeated measures analysis of variance and the post hoc test were applied for the measurements. MR-board 2 recorded the fingers' active range of motion (AROM) and Dunnett's test was used for pairwise comparisons. RESULTS: Except for the FMA-proximal score (p = 0.617) and TOT (p = 0.005), other FMA scores, BBT score, Repeat-FE, WMFT score, and SIS stroke recovery improved significantly (p < 0.001) during MR-board 2 training and were maintained until follow-up. All AROM values of the finger joints changed significantly during training (p < 0.001). CONCLUSIONS: MR-board 2 self-training, which includes natural interactions between humans and computers using a tangible user interface and real-time tracking of the fingers, improved upper limb function across impairment, activity, and participation. MR-board 2 could be used as a self-training tool for patients with stroke, improving their quality of life. TRIAL REGISTRATION NUMBER: This study was registered with the Clinical Research Information Service (CRIS: KCT0004167).

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices.

BACKGROUND: Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. METHODS: This study included 92 participants with variable degrees of KOA. A modified Kellgren-Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). RESULTS: Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. CONCLUSION: Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.

OptiGait: Gait Monitoring Using An Ankle-Worn Stereo Camera System.

We developed an ankle-worn gait monitoring system for tracking gait parameters, including length, width, and height. The system utilizes ankle bracelets equipped with wide-angle infrared (IR) stereo cameras tasked with monitoring a marker on the opposing ankle. A computer vision algorithm we have also developed processes the imaged marker positions to estimate the length, width, and height of the person's gait. Through testing on multiple participants, the prototype of the proposed gait monitoring system exhibited notable performance, achieving an average accuracy of 96.52%, 94.46%, and 95.29% for gait length, width, and height measurements, respectively, despite distorted wide-angle images. The OptiGait system offers a cost-effective and user-friendly alternative compared to existing gait parameter sensing systems, delivering comparable accuracy in measuring gait length and width. Notably, the system demonstrates a novel capability in measuring gait height, a feature not previously reported in the literature.

Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals.

Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data. We developed a deep learning model that cleans ECG measurements from wearable devices and detects arrhythmias using refined data. We pretrained our model using the MIT-BIH Arrhythmia and Noise databases. Least squares GANs were used for noise reduction, maintaining the integrity of the original ECG signal, while a residual network classified the type of arrhythmia. After initial training, we applied transfer learning with actual ECG data. Our noise removal model significantly enhanced data clarity, achieving over 30 dB in a signal-to-noise ratio. The arrhythmia detection model was highly accurate, with an F1-score of 99.10% for noise-free data. The developed model is capable of real-time, accurate arrhythmia detection using wearable ECG devices, allowing for immediate patient notification and facilitating timely medical response.

Bio-Skin-Inspired Flexible Pressure Sensor Based on Carbonized Cotton Fabric for Human Activity Monitoring.

With the development of technology, people's demand for pressure sensors with high sensitivity and a wide working range is increasing. An effective way to achieve this goal is simulating human skin. Herein, we propose a facile, low-cost, and reproducible method for preparing a skin-like multi-layer flexible pressure sensor (MFPS) device with high sensitivity (5.51 kPa-1 from 0 to 30 kPa) and wide working pressure range (0-200 kPa) by assembling carbonized fabrics and micro-wrinkle-structured Ag@rGO electrodes layer by layer. In addition, the highly imitated skin structure also provides the device with an extremely short response time (60/90 ms) and stable durability (over 3000 cycles). Importantly, we integrated multiple sensor devices into gloves to monitor finger movements and behaviors. In summary, the skin-like MFPS device has significant potential for real-time monitoring of human activities in the field of flexible wearable electronics and human-machine interaction.