[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 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.

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.

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.

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.

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.

Seven Things to Know About Exercise Classification With Inertial Sensing Wearables.

OBJECTIVE: Exercise monitoring with low-cost wearables could improve the efficacy of remote physical-therapy prescriptions by tracking compliance and informing the delivery of tailored feedback. While a multitude of commercial wearables can detect activities of daily life, such as walking and running, they cannot accurately detect physical-therapy exercises. The goal of this study was to build open-source classifiers for remote physical-therapy monitoring and provide insight on how data collection choices may impact classifier performance. METHODS: We trained and evaluated multi-class classifiers using data from 19 healthy adults who performed 37 exercises while wearing 10 inertial measurement units (IMUs) on the chest, pelvis, wrists, thighs, shanks, and feet. We investigated the effect of sensor density, location, type, sampling frequency, output granularity, feature engineering, and training-data size on exercise-classification performance. RESULTS: Exercise groups (n = 10) could be classified with 96% accuracy using a set of 10 IMUs and with 89% accuracy using a single pelvis-worn IMU. Multiple sensor modalities (i.e., accelerometers and gyroscopes), high sampling frequencies, and more data from the same population did not improve model performance, but in the future data from diverse populations and better feature engineering could. CONCLUSIONS: Given the growing demand for exercise monitoring systems, our sensitivity analyses, along with open-source tools and data, should reduce barriers for product developers, who are balancing accuracy with product formfactor, and increase transparency and trust in clinicians and patients.

Cloud Native Remote Monitoring Data Ecosystem for Aging Population based on Commercial AAL Sensors.

In the recent years, Active Assisted Living (AAL) technologies used for autonomous tracking and activity recognition have started to play major roles in geriatric care. From fall detection to remotely monitoring behavioral patterns, vital functions and collection of air quality data, AAL has become pervasive in the modern era of independent living for the elderly section of the population. However, even with the current rate of progress, data access and data reliability has become a major hurdle especially when such data is intended to be used in new age modelling approaches such as those using machine learning. This paper presents a comprehensive data ecosystem comprising remote monitoring AAL sensors along with extensive focus on cloud native system architecture, secured and confidential access to data with easy data sharing. Results from a validation study illustrate the feasibility of using this system for remote healthcare surveillance. The proposed system shows great promise in multiple fields from various AAL studies to development of data driven policies by local governments in promoting healthy lifestyles for the elderly alongside a common data repository that can be beneficial to other research communities worldwide.Clinical Relevance- This study creates a cloud-based smart home data ecosystem, which can achieve the remote healthcare monitoring for aging population, enabling them to live more independently and decreasing hospital admission rates.

Continuous Long-Term Physical Activity Monitoring in Hemodialysis Patients.

Background: Physical inactivity is common in patients receiving hemodialysis, but activity patterns throughout the day and in relation to dialysis are largely unknown. This knowledge gap can be addressed by long-term continuous activity monitoring, but this has not been attempted and may not be acceptable to patients receiving dialysis. Methods: Ambulatory patients with end-stage kidney disease receiving thrice-weekly hemodialysis wore commercially available wrist-worn activity monitors for 6 months. Step counts were collected every 15 minutes and were linked to dialysis treatments. Physical function was assessed using the Short Physical Performance Battery (SPPB). Fast time to recovery from dialysis was defined as ≤2 hours. Mixed effects models were created to estimate step counts over time. Results: Of 52 patients enrolled, 48 were included in the final cohort. The mean age was 60 years, and 75% were Black or Hispanic. Comorbidity burden was high, 38% were transported to and from dialysis by paratransit, and 79% had SPPB <10. Median accelerometer use (199 days) and adherence (95%) were high. Forty-two patients (of 43 responders) reported wearing the accelerometer every day, and few barriers to adherence were noted. Step counts were lower on dialysis days (3991 [95% CI, 3187 to 4796] versus 4561 [95% CI, 3757 to 5365]), but step-count intensity was significantly higher during the hour immediately after dialysis than during the corresponding time on nondialysis days (188 steps per hour increase [95% CI, 171 to 205]); these levels were the highest noted at any time. Postdialysis increases were more pronounced among patients with fast recovery time (225 [95% CI, 203 to 248] versus 134 [95% CI, 107 to 161] steps per hour) or those with SPPB ≥7. Estimates were unchanged after adjustment for demographics, diabetes status, and ultrafiltration rate. Conclusions: Long-term continuous monitoring of physical activity is feasible in patients receiving hemodialysis. Highly granular data collection and analysis yielded new insights into patterns of activity after dialysis treatments.

Effect of Electronic Symptom Monitoring on Patient-Reported Outcomes Among Patients With Metastatic Cancer: A Randomized Clinical Trial.

Importance: Electronic systems that facilitate patient-reported outcome (PRO) surveys for patients with cancer may detect symptoms early and prompt clinicians to intervene. Objective: To evaluate whether electronic symptom monitoring during cancer treatment confers benefits on quality-of-life outcomes. Design, Setting, and Participants: Report of secondary outcomes from the PRO-TECT (Alliance AFT-39) cluster randomized trial in 52 US community oncology practices randomized to electronic symptom monitoring with PRO surveys or usual care. Between October 2017 and March 2020, 1191 adults being treated for metastatic cancer were enrolled, with last follow-up on May 17, 2021. Interventions: In the PRO group, participants (n = 593) were asked to complete weekly surveys via an internet-based or automated telephone system for up to 1 year. Severe or worsening symptoms triggered care team alerts. The control group (n = 598) received usual care. Main Outcomes and Measures: The 3 prespecified secondary outcomes were physical function, symptom control, and health-related quality of life (HRQOL) at 3 months, measured by the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (QLQ-C30; range, 0-100 points; minimum clinically important difference [MCID], 2-7 for physical function; no MCID defined for symptom control or HRQOL). Results on the primary outcome, overall survival, are not yet available. Results: Among 52 practices, 1191 patients were included (mean age, 62.2 years; 694 [58.3%] women); 1066 (89.5%) completed 3-month follow-up. Compared with usual care, mean changes on the QLQ-C30 from baseline to 3 months were significantly improved in the PRO group for physical function (PRO, from 74.27 to 75.81 points; control, from 73.54 to 72.61 points; mean difference, 2.47 [95% CI, 0.41-4.53]; P = .02), symptom control (PRO, from 77.67 to 80.03 points; control, from 76.75 to 76.55 points; mean difference, 2.56 [95% CI, 0.95-4.17]; P = .002), and HRQOL (PRO, from 78.11 to 80.03 points; control, from 77.00 to 76.50 points; mean difference, 2.43 [95% CI, 0.90-3.96]; P = .002). Patients in the PRO group had significantly greater odds of experiencing clinically meaningful benefits vs usual care for physical function (7.7% more with improvements of ≥5 points and 6.1% fewer with worsening of ≥5 points; odds ratio [OR], 1.35 [95% CI, 1.08-1.70]; P = .009), symptom control (8.6% and 7.5%, respectively; OR, 1.50 [95% CI, 1.15-1.95]; P = .003), and HRQOL (8.5% and 4.9%, respectively; OR, 1.41 [95% CI, 1.10-1.81]; P = .006). Conclusions and Relevance: In this report of secondary outcomes from a randomized clinical trial of adults receiving cancer treatment, use of weekly electronic PRO surveys to monitor symptoms, compared with usual care, resulted in statistically significant improvements in physical function, symptom control, and HRQOL at 3 months, with mean improvements of approximately 2.5 points on a 0- to 100-point scale. These findings should be interpreted provisionally pending results of the primary outcome of overall survival. Trial Registration: ClinicalTrials.gov Identifier: NCT03249090.

Respiratory fabric sensor based on the side luminescence and photosensitivity mechanism of polymer optical fibers.

It is significant to monitor respiration conveniently and in real time for people suffering from respiratory diseases. Polymer optical fibers (POFs) have the advantages of flexibility and light weight, which is highly desirable for wearable respiratory monitoring. However, in most current applications, the POFs are stitched on the textile substrates in the form of macro-bending. This method is complex to fix the bending with certain curvatures and uncomfortable compared with the POF sensors woven into the textile. In this paper, a respiratory fabric sensor based on the side luminescence and photosensitivity mechanism of POF is proposed and demonstrated. The 750µm-diameter POFs were woven into a fabric as warp and laser marking was performed at their designed positions to make them release or couple light. The spacing change between the POFs caused by the respiratory movement accordingly makes the light intensity change in the photosensitive fiber. We chose four fabric widths (10cm, 8cm, 6cm and 4cm) and four fabric weaves (plain weave, honeycomb weave, 1/3 right twill weave and 8/3 warp satin weave) to implement the full-factor experiment for exploring the measurement effect of the respiratory fabric sensor. The result is that the fabric with width of 4cm and weave of 8/3 warp satin is optimal. The calm and deep respiratory tests of the human chest and abdomen in sitting and standing posture were carried out and the test performance of the fabric sensor is almost comparable to that of the medical monitor. The proposed respiratory fabric sensor is comfortable, easily woven and high in precision, which is expected to realize industrialized scale production.

Investigation of respiratory rate in patients with cystic fibrosis using a minimal-impact biomotion system.

BACKGROUND: In this study we tested the hypothesis that in patients with cystic fibrosis (pwCF) respiratory rate (RR) is associated with antibiotic treatment, exacerbation status, forced expiratory volume in one second (FEV1) and C-reactive protein (CRP). METHODS: Between June 2018 and May 2019, we consecutively enrolled pwCF who were referred to our hospital. We determined RR and heart rate (HR) by using the minimal-impact system VitaLog during the hospital stay. Furthermore, we performed spirometry and evaluated CRP. RESULTS: We included 47 patients: 20 with pulmonary exacerbation and 27 without. RR decreased in patients with exacerbation (27.5/min (6.0/min) vs. 24.4/min (6.0/min), p = 0.004) and in patients with non-exacerbation (22.5/min (5.0/min) vs. 20.9/min (3.5/min), p = 0.024). Patients with exacerbation showed higher RR than patients with non-exacerbation both at the beginning (p = 0.004) and at the end of their hospital stay (p = 0.023). During the hospital stay, HR did not change in the total cohort (66.8/min (11.0/min) vs. 66.6/min (12.0/min), p = 0.440). Furthermore, we did not find significant differences between patients with exacerbation and patients with non-exacerbation (67.0/min (12.5/min) vs. 66.5/min (10.8/min), p = 0.658). We observed a correlation of ρ = -0.36 between RR and FEV1. Moreover, we found a correlation of ρ = 0.52 between RR and CRP. CONCLUSION: In pwCF requiring intravenous therapy, respiratory rate is higher at their hospital admittance and decreased by the time of discharge; it is also associated with C-reactive protein. Monitoring RR could provide important information about the overall clinical conditions of pwCF.

Transdermal sensor features correlate with ecological momentary assessment drinking reports and predict alcohol-related consequences in young adults' natural settings.

BACKGROUND: Wearable transdermal alcohol concentration (TAC) sensors allow passive monitoring of alcohol concentration in natural settings and measurement of multiple features from drinking episodes, including peak intoxication level, speed of intoxication (absorption rate) and elimination, and duration. These passively collected features extend commonly used self-reported drink counts and may facilitate the prediction of alcohol-related consequences in natural settings, aiding risk stratification and prevention efforts. METHOD: A total of 222 young adults aged 21-29 (M age = 22.3, 64% female, 79% non-Hispanic white, 84% undergraduates) who regularly drink heavily participated in a 5-day study that included the ecological momentary assessment (EMA) of alcohol consumption (daily morning reports and participant-initiated episodic EMA sequences) and the wearing of TAC sensors (SCRAM-CAM anklets). The analytic sample contained 218 participants and 1274 days (including 554 self-reported drinking days). Five features-area under the curve (AUC), peak TAC, rise rate (rate of absorption), fall rate (rate of elimination), and duration-were extracted from TAC-positive trajectories for each drinking day. Day- and person-level associations of TAC features with drink counts (morning and episodic EMA) and alcohol-related consequences were tested using multilevel modeling. RESULTS: TAC features were strongly associated with morning drink reports (r = 0.6-0.7) but only moderately associated with episodic EMA drink counts (r = 0.3-0.5) at both day and person levels. Higher peaks, larger AUCs, faster rise rates, and faster fall rates were significantly predictive of day-level alcohol-related consequences after adjusting for both morning and episodic EMA drink counts in separate models. Person means of TAC features added little above daily scores to the prediction of alcohol-related consequences. CONCLUSIONS: These results support the utility of TAC sensors in studies of alcohol misuse among young adults in natural settings and outline the specific TAC features that contribute to the day-level prediction of alcohol-related consequences. TAC sensors provide a passive option for obtaining valid and unique information predictive of drinking risk in natural settings.

Home sleep apnea testing: an accuracy study.

AIM: There are no studies comparing tests performed at home with those carried out in the laboratory, using the same device. The only studies that have been performed have compared the device used at home with the standard polygraph used in the laboratory. The purpose of this study was therefore to verify the accuracy of the home diagnosis of obstructive sleep apnea syndrome (OSAS) via unassisted type 2 portable polysomnography, compared with polysomnography using the same equipment in a sleep laboratory. METHODS: To avoid any possible order effect on the apnea-hypopnea index (AHI), we randomly created two groups of 20-total 40 patients, according to the test sequence. One of the groups had the first test at home and the second test in the laboratory (H-L); the other group had the first test in the laboratory and the second at home (L-H). The second test always took place on the night immediately following the first test. All polysomnographic monitoring was undertaken with the same equipment, an Embletta X100 system (Embla, Natus Inc., Middleton, USA). The Embletta X100 is a portable polygraph that records eleven polygraph signs: (1) electroencephalogram C4/A; (2) electroencephalogram O2/M1; (3) submental EMG; (4) electrooculogram of the right side; (5) nasal cannula (air flow); (6) respiratory effort against a plethysmographic chest strap; (7) respiratory effort against an abdominal plethysmographic belt; (8) heart rate; (9) saturation of oxyhemoglobin; (10) snoring; and (11) body position. RESULTS: There was no difference in sleep efficiency between the group monitored in the laboratory and the group tested at home (p = 0.30). There was no difference in total sleep time (p = 0.11) or sleep latency (p = 0.52), or in the latency in phases N2 and N3 between the monitoring in the laboratory and at home (N2 p = 0.24; N3 p = 0.09). Some differences occurred regarding the PSG that took place at home, with longer duration of wake after sleep onset (WASO) and longer latency for REM sleep, due to failure of the patient to start the monitoring by pressing the "events" button on the device. In the distribution of sleep phases, there was no difference between the group monitored in the laboratory and the group tested at home. CONCLUSION: Results from home sleep monitoring correlate well with the laboratory "gold standard" and may be an option for diagnosing OSAS in selected patients.

Validation of a new inertial measurement unit system based on different dynamic movements for future in-field applications.

Using inertial measurement units (IMUs) in monitoring and analysing sport movements has become popular in sports research since it avoids the laboratory limitation. However, the accuracy of modern IMU-systems (hardware combined with software) needs to be validated using gold-standard systems as baseline. In this study, we investigated the feasibility of the aktos-t IMU-system for in-field biomechanical research by comparing its outputs in various tasks (repetitive movements, gait and jumping) undertaken by 14 participants, with those of an optoelectronic system. The results showed that the accuracy of aktos-t varies according to the task performed. The accuracy of pelvis, hip and knee joints ranged between acceptable (root mean squared error (RMSE) < 5°) and tolerable (RMSE < 10°) in gait, while the upper limb joints showed inaccuracy (RMSE > 10°) and imprecision (coefficient of repeatability > 10°) during the repetitive movement test. Jump impact appeared not to influence the IMU outcomes (p > 0.05). The main sources of error could be related to the IMU-alignment during the reference T-pose. Finally, the study provides researchers the means for evaluating the accuracy of aktos-t (hardware, software and biomechanical model) as sufficiently precise for its application in their in-field investigations.

Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications.

Gait and waist motions always contain massive personnel information and it is feasible to extract these data via wearable electronics for identification and healthcare based on the Internet of Things (IoT). There also remains a demand to develop a cost-effective human-machine interface to enhance the immersion during the long-term rehabilitation. Meanwhile, triboelectric nanogenerator (TENG) revealing its merits in both wearable electronics and IoT tends to be a possible solution. Herein, the authors present wearable TENG-based devices for gait analysis and waist motion capture to enhance the intelligence and performance of the lower-limb and waist rehabilitation. Four triboelectric sensors are equidistantly sewed onto a fabric belt to recognize the waist motion, enabling the real-time robotic manipulation and virtual game for immersion-enhanced waist training. The insole equipped with two TENG sensors is designed for walking status detection and a 98.4% identification accuracy for five different humans aiming at rehabilitation plan selection is achieved by leveraging machine learning technology to further analyze the signals. Through a lower-limb rehabilitation robot, the authors demonstrate that the sensory system performs well in user recognition, motion monitoring, as well as robot and gaming-aided training, showing its potential in IoT-based smart healthcare applications.

Use of a mobile application for the control of anticoagulation with warfarin: a cluster randomized controlled clinical trial - Ijui/RS/Brazil

Abstract To evaluate the effectiveness of an anticoagulation protocol adapted in a mobile application (appG) for patients using warfarin. This was a cluster randomized controlled clinical trial carried out in basic health centers of Ijui, RS, Brazil, between April and October 2017. The appG was installed on the cell phones of all physicians belonging to the intervention group. Primary outcomes were bleeding and thrombosis, and secondary outcomes were changes in the dose of warfarin, use of new drugs, drug interactions, search for health services, and remaining on the target international normalized ratio. Thirty-three patients belonging to 11 basic health centers were included in this study. From these, 15 patients were in the intervention group which used the appG, and 18 were in the control group. After 6 months, patients in the appG group had fewer bleeding events (7% versus 50%, p-value=0.028) and a lower weekly dose of warfarin (29.3 ± 9.7 mg versus 41.7 ± 12.5 mg, p-value=0.030) when compared to the control group. The anticoagulation protocol adapted in a mobile app reduced bleeding in patients using warfarin.

Unobtrusive, Continuous LIDAR-Based Measurement of Gait Characteristics at Home.

This paper describes a novel approach to the unobtrusive assessment of a subset of gait characteristics using a light detection and ranging (LIDAR) device. The developed device is poised to enable unobtrusive, nearly continuous monitoring and inference of patients' gait characteristics to assess physical and cognitive states. The device provides a rapidly sampled signal representing the distance of a participant's body from the LIDAR device. The densely sampled distance estimation is processed by custom algorithms that can potentially be used to estimate various gait characteristics such as step size, cadence, double support, and even step-size symmetry.Clinical Relevance- Since gait is a complex behavior that requires seamless cooperation of multiple systems, including sensation, perception, muscular synergies, and even cognition. Subtle changes in gait may, therefore, indicate issues with physical and mental functionality. In addition to the walking speed, the gait monitoring results can provide inferences about the physical and cognitive states of the unobtrusively monitored individuals using their own data as a baseline.