A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training

Measurement properties of activity monitoring for a rehabilitation (AMoR) platform in post-stroke individuals in a simulated home environment

AIM

The aim of this study was to evaluate the measurement properties of activity monitoring for a rehabilitation (AMoR) platform for step counting, time spent in sedentary behavior, and postural changes during activities of daily living (ADLs) in a simulated home environment.

METHODS

Twenty-one individuals in the post-stroke chronic phase used the AMoR platform during an ADL protocol and were monitored by a video camera. Spearman's correlation coefficient, mean absolute percent error (MAPE), intraclass correlation coefficient (ICC), and Bland-Altman plot analyses were used to estimate the validity and reliability between the AMoR platform and the video for step counting, time spent sitting/lying, and postural changes from sit-to-stand (SI-ST) and sit-to-stand (ST-SI).

RESULTS

Validity of the platform was observed with very high correlation values for step counting (rs = 0.998) and time spent sitting/lying (rs = 0.992) and high correlation for postural change of SI-ST (rs = 0.850) and ST-SI (rs = 0.851) when compared to the video. An error percentage above 5% was observed only for the SI-ST postural change (7.13%). The ICC values show excellent agreement for step counting (ICC3, k = 0.999) and time spent sitting/lying (ICC3, k = 0.992), and good agreement for SI-ST (ICC3, k = 0.859) and ST-SI (ICC3, k = 0.936) postural change. Values of the differences for step counting, sitting/lying time, and postural change were within the limits of agreement according to the analysis of the Bland-Altman graph.

CONCLUSION

The AMoR platform presented validity and reliability for step counting, time spent sitting/lying, and identification of SI-ST and ST-SI postural changes during tests in a simulated environment in post-stroke individuals.

Motor Learning Following Stroke: Mechanisms of Learning and Techniques to Augment Neuroplasticity

Sensorimotor impairments are common after stroke requiring stroke survivors to relearn lost motor skills or acquire new ones in order to engage in daily activities. Thus, motor skill learning is a cornerstone of stroke rehabilitation. This article provides an overview of motor control and learning theories that inform stroke rehabilitation interventions, discusses principles of neuroplasticity, and provides a summary of practice conditions and techniques that can be used to augment motor learning and neuroplasticity in stroke rehabilitation.

'Smart reminder': A feasibility pilot study on the effects of a wearable device treatment on the hemiplegic upper limb in persons with stroke

INTRODUCTION

Emerging literature suggests that wearable devices offer a promising option for self-directed home-based upper limb training for persons with stroke. However, little research is available to explore integrating smartphone applications with wearable devices to provide upper limb telerehabilitation to stroke survivors at home. This study examined the feasibility and potential therapeutic effects of a wearable device integrated with a smartphone-based telerehabilitation system to provide upper limb rehabilitation to stroke survivors at home.

METHODS

Twelve stroke survivors from community support groups participated in a treatment consisting of 4-week telerehabilitation using a wearable device and 4-week conventional therapy successively in a single-blind, randomised crossover study. A 3-week washout period was administered between the two 4-week treatments. The primary outcome measures were the Fugl Meyer Assessment, the Action Research Arm Test, and the active range of motion (ROM) of the upper limb. Secondary outcome measures included the Motor Activity Log and exercise adherence.

RESULTS

Results showed that the active ROM of participants' hemiplegic shoulder improved more significantly after 4 weeks of telerehabilitation with the wearable device than with conventional therapy. No significant differences were found in other outcome measures.

CONCLUSIONS

A 4-week telerehabilitation programme using a wearable device improves the hemiplegic upper limb in community-dwelling stroke survivors and may be feasible as an effective intervention for self-directed upper limb rehabilitation at home.

Integrating EEG and Machine Learning to Analyze Brain Changes during the Rehabilitation of Broca's Aphasia

The fusion of electroencephalography (EEG) with machine learning is transforming rehabilitation. Our study introduces a neural network model proficient in distinguishing pre- and post-rehabilitation states in patients with Broca's aphasia, based on brain connectivity metrics derived from EEG recordings during verbal and spatial working memory tasks. The Granger causality (GC), phase-locking value (PLV), weighted phase-lag index (wPLI), mutual information (MI), and complex Pearson correlation coefficient (CPCC) across the delta, theta, and low- and high-gamma bands were used (excluding GC, which spanned the entire frequency spectrum). Across eight participants, employing leave-one-out validation for each, we evaluated the intersubject prediction accuracy across all connectivity methods and frequency bands. GC, MI theta, and PLV low-gamma emerged as the top performers, achieving 89.4%, 85.8%, and 82.7% accuracy in classifying verbal working memory task data. Intriguingly, measures designed to eliminate volume conduction exhibited the poorest performance in predicting rehabilitation-induced brain changes. This observation, coupled with variations in model performance across frequency bands, implies that different connectivity measures capture distinct brain processes involved in rehabilitation. The results of this paper contribute to current knowledge by presenting a clear strategy of utilizing limited data to achieve valid and meaningful results of machine learning on post-stroke rehabilitation EEG data, and they show that the differences in classification accuracy likely reflect distinct brain processes underlying rehabilitation after stroke.

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are 0.8918 0 , 2.6753 0 and 8.0258 0 , respectively. These experimental results demonstrate the feasibility of a safe home-based training paradigm for stroke survivors. The proposed framework will help overcome the technological barriers, being relevant for IT experts in health-related domains and pave the way to setting up a telerehabilitation system increasing implementation of home-based robotic rehabilitation. The proposed novel framework includes:•A low-cost and easy to wear upper limb robotic orthosis which is suitable for use at home.•A paradigm of IoRT which is used in conjunction with the robotic orthosis for home-based rehabilitation.•A machine learning-based protocol which combines and analyse the data from robot sensors for efficient and quick decision making.

An Automated Electronic System in a Motorized Wheelchair for Telemonitoring: Mixed Methods Study Based on Internet of Things

BACKGROUND

Wheelchair positioning systems can prevent postural deficits and pressure injuries. However, a more effective professional follow-up is needed to assess and monitor positioning according to the specificities and clinical conditions of each user.

OBJECTIVE

This study aims to present the concept of an electronic system embedded in a motorized wheelchair, based on the Internet of Things (IoT), for automated positioning as part of a study on wheelchairs and telemonitoring.

METHODS

We conducted a mixed methods study with a user-centered design approach, interviews with 16 wheelchair users and 66 professionals for the development of system functions, and a formative assessment of 5 participants with descriptive analysis to design system concepts.

RESULTS

We presented a new wheelchair system with hardware and software components developed based on coparticipation with singular components in an IoT architecture. In an IoT solution, the incorporation of sensors from the inertial measurement unit was crucial. These sensors were vital for offering alternative methods to monitor and control the tilt and recline functions of a wheelchair. This monitoring and control could be achieved autonomously through a smartphone app. In addition, this capability addressed the requirements of real users.

CONCLUSIONS

The technologies presented in this system can benefit telemonitoring and favor real feedback, allowing quality provision of health services to wheelchair users. User-centered development favored development with specific functions to meet the real demands of users. We emphasize the importance of future studies on the correlation between diagnoses and the use of the system in a real environment to help professionals in treatment.

A Systematic Review of the Long-Term Effects of Using Smartphone- and Tablet-Based Rehabilitation Technology for Balance and Gait Training and Exercise Programs

Recent advances in wearable motion sensors, mobile devices, the Internet of Things, and telecommunications have created new potential for telerehabilitation. Recognizing that there is no systematic review of smartphone- or tablet-based balance and gait telerehabilitation technology for long-term use (i.e., four weeks or more), this systematic review summarizes the effects of smartphone- or tablet-based rehabilitation technology on balance and gait exercise and training in balance and gait disorders. The review examined studies written in English published from 2013 to 2023 in Web of Science, Pubmed, Scopus, and Google Scholar. Of the 806 studies identified, 14 were selected, and the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was applied to evaluate methodological quality. The systematic review concluded that all 14 studies found balance and gait performance improvement after four weeks or more of balance and gait telerehabilitation. Ten of the 14 studies found that carry-over effects (improved functional movements, muscle strength, motor capacity, cognition, and reduced fear of falling and anxiety levels) were maintained for weeks to months. The results of the systematic review have positive technical and clinical implications for the next-generation design of rehabilitation technology in balance and gait training and exercise programs.

Visualization Analysis of Research Trends and Hotspots in Inspiratory Muscle Training

BACKGROUND Inspiratory muscle training (IMT) aims to train inspiratory muscles based mainly on the diaphragm by applying a load resistance during the inspiratory process. Many papers related to IMT have been published in various journals; however, no articles objectively and directly present the development trends and research hotspots of IMT. Therefore, this study used CiteSpace to visually analyze recent IMT-related publications to provide valuable information for future IMT-related studies. MATERIAL AND METHODS CiteSpace was applied to analyze the IMT-related publications by countries, institutions, journals, authors, references, and keywords. RESULTS We included 504 papers. The number of IMT-related publications trended upward between 2009 and 2022. Leuven had the highest number of publications by an institution. The American Journal of Respiratory and Critical Care Medicine was the most frequently co-cited journal. Half of the top 10 references cited were from Journal Citation Reports (JCR) Q1 and half were about the application of IMT in chronic obstructive pulmonary disorder. Gosselink was the author with the highest number of publications and Aldrich was the author with the highest co-citation frequency. The preponderance of studies on the surgical population and postoperative pulmonary complications reflects potential application of IMT in enhanced recovery after surgery. CONCLUSIONS This study provides scholars with important information related to IMT research. It analyzes IMT research trends and status, which can help researchers identify primary topics in the field and find ways to explore new research directions to promote the application of IMT in clinical practice and the cooperation of IMT-related disciplines.

Effectiveness and therapeutic compliance of digital therapy in shoulder rehabilitation: a randomized controlled trial

BACKGROUND

Interactive videogames, virtual reality, and robotics represent a new opportunity for multimodal treatments in many rehabilitation contexts. However, several commercial videogames are designed for leisure and are not oriented toward definite rehabilitation goals. Among the many, Playball® (Playwork, Alon 10, Ness Ziona, Israel) is a therapeutic ball that measures both movement and pressure applied on it while performing rehabilitation games. This study aimed: (i) to evaluate whether the use of this novel digital therapy gaming system was clinically effective during shoulder rehabilitation; (ii) to understand whether this gaming rehabilitation program was effective in improving patients' engagement (perceived enjoyment and self-efficacy during therapy; attitude and intention to train at home) in comparison to a control non-gaming rehabilitation program.

METHODS

A randomized controlled experimental design was outlined. Twenty-two adults with shoulder pathologies were recruited for a rehabilitation program of ten consecutive sessions. A control (CTRL; N = 11; age: 62.0 ± 10.9 yrs) and an intervention group (PG; N = 11; age: 59.9 ± 10.2 yrs) followed a non-digital and a digital therapy, respectively. The day before (T0) and after (T1) the rehabilitation program, pain, strength, and mobility assessments were performed, together with six questionnaires: PENN shoulder Score, PACES-short, Self-efficacy, Attitudes to train at home, Intention to train at home, and System usability scale (SUS).

RESULTS

MANOVA analysis showed significant improvements in pain (p < 0.01), strength (p < 0.05), and PENN Shoulder Score (p < 0.001) in both groups. Similarly, patients' engagement improved, with significant increments in Self-efficacy (p < 0.05) and attitude (p < 0.05) scores in both groups after the rehabilitation. Pearson correlation showed significant correlations of the Δ scores (T1 - T0) between PACES and Self-efficacy (r = 0.623; p = 0.041) and between PACES and Intention to train at home (r = 0.674; p = 0.023) only in the PG. SUS score after the rehabilitation (74.54 ± 15.60) overcame the cut-off value of 68, representative of good usability of a device.

CONCLUSIONS

The investigated digital therapy resulted as effective as an equivalent non-digital therapy in shoulder rehabilitation. The reported positive relationship between the subject's enjoyment during digital therapy and intention to train at home suggests promising results in possible patient's exercise engagement at home after the rehabilitation in the medical center.

RETROSPECTIVELY REGISTERED

NCT05230056.

A reliability and validity study of the electronic health literacy scale among stroke patients in China

BACKGROUND

Patients with stroke usually use smartphones to obtain online information to maintain their health. But their ability to identify, evaluate and apply this information is still unknown.

AIM

This study was designed to examine the reliability and validity of the electronic Health Literacy Scale among patients with stroke in China.

DESIGN

This is a cross-sectional survey.

METHODS

A demographic questionnaire, the electronic Health Literacy Scale (e-HLS) and the eHealth Literacy Scale (eHEALS) were administered to a sample of 648 patients with ischemic stroke recruited from December 2020 to March 2021 in a tertiary hospital.

RESULTS

The Cronbach'α coefficient on the e-HLS-CHI was 0.907. Kappa consistency coefficient of test-retest reliability was 0.691 (p < .05). Three factors were extracted by Exploratory Factor Analysis (EFA), accounting for 90.84% of the total variance. Confirmatory Factory Analysis (CFA) revealed that three factors of e-HLS-CHI fit well (NFI = 0.979, RFI = 0.955, IFI = 0.987, TLI = 0.972, CFI = 0.987, RMSEA = 0.070, CMIN/DF = 2.586). Good simultaneous validity was suggested by the positive correlation of 0.94 (p < .001) between the e-HLS-CHI and eHEALS. When using eHEALS as the standard, the area under the ROC curve of e-HLS-CHI was 0.896 (95% CI: 0.831-0.960, p < .001). The sensitivity and specificity were 97.8% and 70.4% respectively.

CONCLUSIONS

The e-HLS can be used to evaluate electronic health literacy of patients with stroke in China after translation and cultural adaption.

Clinical Study of a Wearable Remote Rehabilitation Training System for Patients With Stroke: Randomized Controlled Pilot Trial

BACKGROUND

In contrast to the large and increasing number of patients with stroke, clinical rehabilitation resources cannot meet their rehabilitation needs. Especially for those discharged, ways to carry out effective rehabilitation training without the supervision of physicians and receive guidance from physicians remain urgent problems to be solved in clinical rehabilitation and have become a research hot spot at home and abroad. At present, there are many studies on home rehabilitation training based on wearable devices, Kinect, among others, but these have disadvantages (eg, complex systems, high price, and unsatisfactory rehabilitation effects).

OBJECTIVE

This study aims to design a remote intelligent rehabilitation training system based on wearable devices and human-computer interaction training tasks, and to evaluate the effectiveness and safety of the remote rehabilitation training system for nonphysician-supervised motor rehabilitation training of patients with stroke through a clinical trial study.

METHODS

A total of 120 inpatients with stroke having limb motor dysfunction were enrolled via a randomized, parallel-controlled method in the rehabilitation institutions, and a 3-week clinical trial was conducted in the rehabilitation hall with 60 patients in the experimental group and 60 in the control group. The patients in the experimental group used the remote rehabilitation training system for rehabilitation training and routine clinical physical therapy (PT) training and received routine drug treatment every day. The patients in the control group received routine clinical occupational therapy (OT) training and routine clinical PT training and routine drug treatment every day. At the beginning of the training (baseline) and after 3 weeks, the Fugl-Meyer Motor Function Rating scale was scored by rehabilitation physicians, and the results were compared and analyzed.

RESULTS

Statistics were performed using SAS software (version 9.4). The total mean Fugl-Meyer score improved by 11.98 (SD 8.46; 95% CI 9.69-14.27) in the control group and 17.56 (SD 11.65; 95% CI 14.37-20.74) in the experimental group, and the difference between the 2 groups was statistically significant (P=.005). Among them, the mean Fugl-Meyer upper extremity score improved by 7.45 (SD 7.24; 95% CI 5.50-9.41) in the control group and 11.28 (SD 8.59; 95% CI 8.93-13.62) in the experimental group, and the difference between the 2 groups was statistically significant (P=.01). The mean Fugl-Meyer lower extremity score improved by 4.53 (SD 4.42; 95% CI 3.33-5.72) in the control group and 6.28 (SD 5.28; 95% CI 4.84-7.72) in the experimental group, and there was no significant difference between the 2 groups (P=.06). The test results showed that the experimental group was better than the control group, and that the patients' motor ability was improved.

CONCLUSIONS

The remote rehabilitation training system designed based on wearable devices and human-computer interaction training tasks can replace routine clinical OT training. In the future, through medical device registration certification, the system will be used without the participation of physicians or therapists, such as in rehabilitation training halls, and in remote environments, such as communities and homes.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR2200061310; https://tinyurl.com/34ka2725.

Opportunities for Improving Motor Assessment and Rehabilitation After Stroke by Leveraging Video-Based Pose Estimation

ABSTRACT

Stroke is a leading cause of long-term disability in adults in the United States. As the healthcare system moves further into an era of digital medicine and remote monitoring, technology continues to play an increasingly important role in post-stroke care. In this Analysis and Perspective article, opportunities for using human pose estimation-an emerging technology that uses artificial intelligence to track human movement kinematics from simple videos recorded using household devices (e.g., smartphones, tablets)-to improve motor assessment and rehabilitation after stroke are discussed. The focus is on the potential of two key applications: (1) improving access to quantitative, objective motor assessment and (2) advancing telerehabilitation for persons post-stroke.

Opportunities and challenges in the development of exoskeletons for locomotor assistance

Exoskeletons can augment the performance of unimpaired users and restore movement in individuals with gait impairments. Knowledge of how users interact with wearable devices and of the physiology of locomotion have informed the design of rigid and soft exoskeletons that can specifically target a single joint or a single activity. In this Review, we highlight the main advances of the past two decades in exoskeleton technology and in the development of lower-extremity exoskeletons for locomotor assistance, discuss research needs for such wearable robots and the clinical requirements for exoskeleton-assisted gait rehabilitation, and outline the main clinical challenges and opportunities for exoskeleton technology.

Effects of Internet of Things-based power cycling and neuromuscular training on pain and walking ability in elderly patients with KOA: protocol for a randomized controlled trial

BACKGROUND

Osteoarthritis (OA) is a common and highly disabling disease that imposes a heavy burden on individuals and society. Although physical therapy is recommended as an important method to relieve OA symptoms, patients cannot continue treatment after returning home. Research on Internet telerehabilitation for knee osteoarthritis (KOA) can reduce pain and improve patient quality of life, and Internet of Things (IoT)-based telerehabilitation is a new form of delivering rehabilitation. This study will evaluate the effect of telerehabilitation via IoT, as a medium to deliver exercises, on pain and walking in patients with KOA.

METHODS

This study is a single-blind randomized controlled trial. We will recruit 42 middle-aged and elderly patients with KOA aged ≥ 50 years and randomly divided into power cycling group, neuromuscular exercise group, and control group, and intervention will last for 12 weeks. Outcome measures will be taken at baseline and 4 weeks, 8 weeks, and 12 weeks post-intervention. The pre- and posttreatment differences in knee pain and physical function between participants undergoing power cycling and neuromuscular training and those in the control group will be determined by each scale. The effectiveness will be assessed by the Western Ontario and McMaster Universities Osteoarthritis Index Score (WOMAC) and an 11-point numerical pain rating scale. Walking function and quality of life will be assessed by the timed up and go and walk test, 6-min walk test, and quality of life health status questionnaires.

DISCUSSION

The findings from this trial will establish the feasibility and effectiveness of IoT-based power cycling and neuromuscular training on elderly patients with KOA in the community. As a result, this trial may help provide experimental evidence for finding a better exercise method suitable for elderly patients with KOA in the community.

TRAIL REGISTRATION

Chinese Clinical Trials Registry ChiCTR2200058924. Prospectively registered on 6 May 2022.

Home-based exercise using balance disc and smartphone inclinometer application improves balance and activity of daily living in individuals with stroke: A randomized controlled trial

BACKGROUND

Sitting ability is critical for daily activities in individuals who have experienced a stroke. A combination of seated balance training on an unstable surface and real-time visual feedback via a simple mobile inclinometer application may improve trunk control in stroke survivors.

OBJECTIVE

This randomized controlled trial aimed to determine the effects of home-based exercise utilizing a balance disc with input from a smartphone inclinometer application on sitting balance and activities of daily living in stroke survivors.

METHODS

This trial enrolled 32 stroke survivors aged 30 to 75 years. Participants were randomly assigned to one of two groups: intervention or control. Both groups underwent four weeks of traditional therapy. Additionally, the intervention group received four weeks of multidirectional lean training utilizing a balance disc and a smartphone application with an inclinometer. The Postural Assessment Scale for Stroke (PASS), the Function in Sitting Test (FIST), and the Barthel Index (BI) were used to assess the results. To compare between group effects, an ANCOVA analysis was performed using a baseline as a covariate.

RESULTS

The PASS changing posture and BI were considerably greater in the intervention group compared to the control group. Other metrics revealed no statistically significant differences between the groups.

CONCLUSION

Home-based training with balance discs and input from a smartphone inclinometer application may improve postural control and daily activity in stroke patients.

TRIAL REGISTRATION

Clinical trials registry number: TCTR20210617004.

The Effects of an Individualized Smartphone-Based Exercise Program on Self-defined Motor Tasks in Parkinson Disease: Pilot Interventional Study

BACKGROUND

Bradykinesia and rigidity are prototypical motor impairments of Parkinson disease (PD) highly influencing everyday life. Exercise training is an effective treatment alternative for motor symptoms, complementing dopaminergic medication. High frequency training is necessary to yield clinically relevant improvements. Exercise programs need to be tailored to individual symptoms and integrated in patients' everyday life. Due to the COVID-19 pandemic, exercise groups in outpatient setting were largely reduced. Developing remotely supervised solutions is therefore of significant importance.

OBJECTIVE

This pilot study aimed to evaluate the feasibility of a digital, home-based, high-frequency exercise program for patients with PD.

METHODS

In this pilot interventional study, patients diagnosed with PD received 4 weeks of personalized exercise at home using a smartphone app, remotely supervised by specialized therapists. Exercises were chosen based on the patient-defined motor impairment and depending on the patients' individual capacity (therapists defined 3-5 short training sequences for each participant). In a first education session, the tailored exercise program was explained and demonstrated to each participant and they were thoroughly introduced to the smartphone app. Intervention effects were evaluated using the Unified Parkinson Disease Rating Scale, part III; standardized sensor-based gait analysis; Timed Up and Go Test; 2-minute walk test; quality of life assessed by the Parkinson Disease Questionnaire; and patient-defined motor tasks of daily living. Usability of the smartphone app was assessed by the System Usability Scale. All participants gave written informed consent before initiation of the study.

RESULTS

In total, 15 individuals with PD completed the intervention phase without any withdrawals or dropouts. The System Usability Scale reached an average score of 72.2 (SD 6.5) indicating good usability of the smartphone app. Patient-defined motor tasks of daily living significantly improved by 40% on average in 87% (13/15) of the patients. There was no significant impact on the quality of life as assessed by the Parkinson Disease Questionnaire (but the subsections regarding mobility and social support improved by 14% from 25 to 21 and 19% from 15 to 13, respectively). Motor symptoms rated by Unified Parkinson Disease Rating Scale, part III, did not improve significantly but a descriptive improvement of 14% from 18 to 16 could be observed. Clinically relevant changes in Timed Up and Go test, 2-minute walk test, and sensor-based gait parameters or functional gait tests were not observed.

CONCLUSIONS

This pilot interventional study presented that a tailored, digital, home-based, and high-frequency exercise program over 4 weeks was feasible and improved patient-defined motor activities of daily life based on a self-developed patient-defined impairment score indicating that digital exercise concepts may have the potential to beneficially impact motor symptoms of daily living. Future studies should investigate sustainability effects in controlled study designs conducted over a longer period.

Effectiveness of home-based upper limb rehabilitation in stroke survivors: A systematic review and meta-analysis

Background

Home-based training is an alternative option to provide intensive rehabilitation without costly supervised therapy. Though several studies support the effectiveness of home-based rehabilitation in improving hemiparetic upper limb function in stroke survivors, a collective evaluation of the evidence remains scarce.

Objectives

This study aims to determine the effects of home-based upper limb rehabilitation for hemiparetic upper limb recovery in stroke survivors.

Methods

The databases of the Cochrane Library, MEDLINE, CINAHL, and Web of Science were systematically searched from January 2000 to September 2020. Only randomized, controlled, and cross-over trials that evaluated the effects of home-based upper limb interventions were selected. The Pedro scale was used to assess the methodological quality of the studies. A meta-analysis of the upper limb function outcomes was performed by calculating the mean difference/standardized mean difference using a fixed/random effect model.

Results

An initial search yielded 1,049 articles. Twenty-six articles were included in the review. The pooled evidence of the meta-analysis showed that home-based upper limb intervention was more effective in improving upper limb function [SMD: 0.28, 95% CI (0.12, 0.44), I² = 0%, p < 0.001, fixed effect model] than conventional therapy. When comparing two types of home-based interventions, subgroup analysis revealed that home-based technology treatment—electrical stimulation—provided more significant improvement in upper limb function than treatment without the use of technology (SMD: 0.64, 95% CI (0.21, 1.07), I² = 0%, p = 0.003, random effect model).

Conclusion

The beneficial effects of home-based upper limb interventions were superior to conventional therapy in improving function and perceived use of the hemiparetic upper limb in daily activities. Among the home-based interventions, home-based electrical stimulation seemed to provide the most optimal benefits.

Internet of Things for beyond-the-laboratory prosthetics research

Research on upper-limb prostheses is typically laboratory-based. Evidence indicates that research has not yet led to prostheses that meet user needs. Inefficient communication loops between users, clinicians and manufacturers limit the amount of quantitative and qualitative data that researchers can use in refining their innovations. This paper offers a first demonstration of an alternative paradigm by which remote, beyond-the-laboratory prosthesis research according to user needs is feasible. Specifically, the proposed Internet of Things setting allows remote data collection, real-time visualization and prosthesis reprogramming through Wi-Fi and a commercial cloud portal. Via a dashboard, the user can adjust the configuration of the device and append contextual information to the prosthetic data. We evaluated this demonstrator in real-time experiments with three able-bodied participants. Results promise the potential of contextual data collection and system update through the internet, which may provide real-life data for algorithm training and reduce the complexity of send-home trials. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Artificial Intelligence and Internet-of-Things Technology Application on Ideological and Political Classroom Teaching Reform

We look at the current state of ideological and political classroom teaching based on artificial intelligence and the Internet of Things. We also look at the problems with traditional ideological and political classroom teaching in order to improve the effectiveness of classroom reform. Also, this paper reforms teaching based on the real world and builds a modern, intelligent system for teaching political and ideological ideas in the classroom. The ideological and political education system is built on the Internet of Things. It has three layers: the perception layer, the network layer, and the application layer. The system collects efficient ideological and political teaching activity data in real time through the Internet of Things and wireless networks, sends the data to the data center through the Internet, and then uses the collected data as the original data for applications, data mining, and modeling simulation. Lastly, this paper proves through simulation experiments and teaching experiments that the system built in this paper can be used to reform ideological and political education.

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

Adam or Eve? Automatic users’ gender classification via gestures analysis on touch devices

Gender classification of mobile devices’ users has drawn a great deal of attention for its applications in healthcare, smart spaces, biometric-based access control systems and customization of user interface (UI). Previous works have shown that authentication systems can be more effective when considering soft biometric traits such as the gender, while others highlighted the significance of this trait for enhancing UIs. This paper presents a novel machine learning-based approach to gender classification leveraging the only touch gestures information derived from smartphones’ APIs. To identify the most useful gesture and combination thereof for gender classification, we have considered two strategies: single-view learning, analyzing, one at a time, datasets relating to a single type of gesture, and multi-view learning, analyzing together datasets describing different types of gestures. This is one of the first works to apply such a strategy for gender recognition via gestures analysis on mobile devices. The methods have been evaluated on a large dataset of gestures collected through a mobile application, which includes not only scrolls, swipes, and taps but also pinch-to-zooms and drag-and-drops which are mostly overlooked in the literature. Conversely to the previous literature, we have also provided experiments of the solution in different scenarios, thus proposing a more comprehensive evaluation. The experimental results show that scroll down is the most useful gesture and random forest is the most convenient classifier for gender classification. Based on the (combination of) gestures taken into account, we have obtained F1-score up to 0.89 in validation and 0.85 in testing phase. Furthermore, the multi-view approach is recommended when dealing with unknown devices and combinations of gestures can be effectively adopted, building on the requirements of the system our solution is built-into. Solutions proposed turn out to be both an opportunity for gender-aware technologies and a potential risk deriving from unwanted gender classification.

Common Bias and Challenges in Physical and Rehabilitation Medicine Research: How to Tackle Them

The importance of evidence-based medicine is crucial, especially in physical and rehabilitation medicine (PRM), where there is a need to conduct rigorous experimental protocols, as in any medical field. Currently, in clinical practice, therapeutic approaches are often based on empirical data rather than evidence-based medicine. However, the field of PRM faces several challenges that may complicate scientific research. In addition, there is often a lack of appropriate research training in educational programs. In this context, we aim to review the methodological challenges in PRM and provide clear examples for each of them as well as potential solutions when possible. This article will cover the following themes: (1) Choosing the right study design and conducting randomized and benchmarking controlled trials; (2). Selecting the appropriate controlled, placebo or sham condition and the issue of blinding in non-pharmacological trials; (3) The impact of populations' heterogeneity and multi-comorbidities; (4). The challenge of recruitment and adherence; (5). The importance of homogeneity and proper quantification of rehabilitative strategies; and (6). Ethical issues. We are convinced that teaching the basics of scientific research in PRM could help physicians and therapists to choose a treatment based on (novel) scientific evidence. It may also promote scientific research in PRM to develop novel and personalized rehabilitation strategies using rigorous methodologies and randomized or benchmarking controlled trials in order to improve patients' management.

Implementation of a Mobile Technology-Supported Diaphragmatic Breathing Intervention in Military mTBI With PTSD

BACKGROUND

Diaphragmatic breathing is an evidence-based intervention for managing stress and anxiety; however, some military veterans with mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) report challenges to learning and practicing the technique. BreatheWell Wear assists performance of breathing exercises through reminders, biofeedback, and visual, tactile, and auditory guidance.

OBJECTIVE

To evaluate feasibility of implementing BreatheWell Wear, a mobile smartwatch application with companion smartphone app, as an intervention for stress management in military veterans with mTBI and PTSD.

METHODS

Thirty veterans with chronic symptoms of mTBI and PTSD recruited from an interdisciplinary, intensive outpatient program participated in this pilot pragmatic clinical trial. Participants were randomly assigned to the experimental (BreatheWell Wear and conventional care) and control (conventional care) groups for 4 weeks. Conventional care included instruction on relaxation breathing and participation in behavioral health therapy. Effects on goal attainment, treatment adherence, diaphragmatic breathing technique knowledge, and stress were measured through surveys and diaries. Changes in symptoms, mood, and well-being were measured pre/postintervention via the Posttraumatic Checklist for DSM-5, Beck Anxiety Inventory, Beck Depression Inventory, and Flourishing Scale.

RESULTS

Person-centered goal attainment (t = 4.009, P < .001), treatment adherence (t = 2.742, P = .001), diaphragmatic breathing technique knowledge (t = 1.637, P < .001), and reported ease of remembering to practice (t = -3.075, P = .005) were significantly greater in the experimental group. As expected, measures of PTSD, anxiety, depression, and psychological well-being showed clinically meaningful change in both groups, and both groups demonstrated reduced stress following diaphragmatic breathing.

CONCLUSION

These preliminary findings indicate that BreatheWell Wear may be a clinically feasible tool for supporting diaphragmatic breathing as an intervention in veterans with mTBI and PTSD, and a future effectiveness trial is warranted.

Construction of an E-Commerce System Based on 5G and Internet of Things Technology

In order to improve the comprehensive performance of the e-commerce system, this paper combines 5G communication technology and the Internet of Things technology to improve the e-commerce system, and conduct end-point analysis on the e-commerce client data analysis system and smart logistics system. Moreover, this paper uses 5G technology to improve machine learning algorithms to process e-commerce back-end data and improve the efficiency of e-commerce client data processing. In addition, this paper combines the Internet of Things to build an e-commerce smart logistics system model to improve the overall efficiency of the logistics system. Finally, this paper combines the demand analysis to construct the functional module structure of the e-commerce system, and verifies the practical functions of the system through experimental research. From the experimental research results, it can be seen that the e-commerce system based on 5G communication technology and Internet of Things technology constructed in this paper is very reliable.

Application of Multiprocessing Technology of Motion Video Image Based on Sensor Technology in Track and Field Sports

To improve the accuracy of track and field sports feature recognition, this paper combines sensor technology to improve the motion video image multiprocessing technology and gives the basic principles of image registration. Moreover, this paper chooses a model based on projection transformation. When using a high-speed linear CCD, only the image information on the finish line is collected. Unlike the previous high-speed area CCD cameras that can capture runway information, linear CCDs are used to collect only the image information on the finish line, and the data is collected and processed through sensor technology. The research shows that the application effect of the motion video image multiprocessing technology based on sensor technology in track and field sports proposed in this paper has good practical effects.

Clinical Use of Surface Electromyography to Track Acute Upper Extremity Muscle Recovery after Stroke: A Descriptive Case Study of a Single Patient

Arm recovery varies greatly among stroke survivors. Wearable surface electromyography (sEMG) sensors have been used to track recovery in research; however, sEMG is rarely used within acute and subacute clinical settings. The purpose of this case study was to describe the use of wireless sEMG sensors to examine changes in muscle activity during acute and subacute phases of stroke recovery, and understand the participant’s perceptions of sEMG monitoring. Beginning three days post-stroke, one stroke survivor wore five wireless sEMG sensors on his involved arm for three to four hours, every one to three days. Muscle activity was tracked during routine care in the acute setting through discharge from inpatient rehabilitation. Three- and eight-month follow-up sessions were completed in the community. Activity logs were completed each session, and a semi-structured interview occurred at the final session. The longitudinal monitoring of muscle and movement recovery in the clinic and community was feasible using sEMG sensors. The participant and medical team felt monitoring was unobtrusive, interesting, and motivating for recovery, but desired greater in-session feedback to inform rehabilitation. While barriers in equipment and signal quality still exist, capitalizing on wearable sensing technology in the clinic holds promise for enabling personalized stroke recovery.

A Kinematic Data Based Lower Limb Motor Function Evaluation Method for Post-Stroke Rehabilitation

Recent studies have demonstrated that home-based rehabilitation for stroke patients has excellent potential in reducing the cost and enhancing rehabilitation efficiency. Nonetheless, a timely and accurate rehabilitation assessment is required to attain efficacy and provide feedback to both clinicians and patients. In this paper, a lower limb motor function assessment approach based on limb kinematic data has been presented. The kinematic characteristics of lower limbs were quantified into specific evaluation parameters, which were calculated during a set of selected rehabilitation exercises. A body area network composed of two triaxial accelerometers was used to acquire the limb kinematic data of twenty stroke patients and six healthy subjects. While a referenced template was developed using the data from healthy subjects, an empirical score was obtained to evaluate the lower-limb motor function of stroke patients from the calculated parameters. The results have demonstrated that the scoring has a statistically significant strong correlation with the Brunnstrom stage classification, which provides a practical quantitative evaluation approach for home-based rehabilitation for lower limbs of stroke patients.Clinical Relevance- The proposed quality assessment method provides practical technical support for performing early support discharge rehabilitation.

What People with Parkinson's Disease Want

Parkinson’s disease is an incurable, progressive neurodegenerative disease. This condition is complicated by the varying symptoms in individuals who differ in age of onset, symptoms, progression of disease, response to treatment and prognosis. In this paper, we focus on quality of life achieved through a combination of comprehensive health care, continuous support, and self care. Determining what people with Parkinson’s disease want is like assembling multiple puzzles simultaneously. While we surmise that patient centered care, support programs, access to comprehensive health care, and relevant symptom control are pieces of this puzzle, more longitudinal studies— which are observational in nature and correlate the impact of symptoms with patients’ reported needs— are necessary.

Medical Internet of Things to Realize Elderly Stroke Prevention and Nursing Management

Stroke is a major disease that seriously endangers the lives and health of middle-aged and elderly people in our country, but its implementation of secondary prevention needs to be improved urgently. The application of IoT technology in home health monitoring and telemedicine, as well as the popularization of cloud computing, contributes to the early identification of ischemic stroke and provides intelligent, humanized, and preventive medical and health services for patients at high risk of stroke. This article clarifies the networking structure and networking objects of the rehabilitation system Internet of Things, clarifies the functions of each part, and establishes an overall system architecture based on smart medical care; the design and optimization of the mechanical part of the stroke rehabilitation robot are carried out, as well as kinematics and dynamic analysis. According to the functions of different types of stroke rehabilitation robots, strategies are given for the use of lower limb rehabilitation robots; standardized codes are used to identify system objects, and RFID technology is used to automatically identify users and devices. Combined with the use of the Internet and GSM mobile communication network, construct a network database of system networking objects and, on this basis, establish information management software based on a smart medical rehabilitation system that takes care of both doctors and patients to realize the system's Internet of Things architecture. In addition, this article also gives the recovery strategy generation in the system with the design method of resource scheduling method and the theoretical algorithm of rehabilitation strategy generation is given and verified. This research summarizes the application background, advantages, and past practice of the Internet of Things in stroke medical care, develops and applies a medical collaborative cloud computing system for systematic intervention of stroke, and realizes the module functions such as information sharing, regional monitoring, and collaborative consultation within the base.

Investigation of the home-reablement program on rehabilitation outcomes for people with stroke: A pilot study

BACKGROUND

Reablement is 1 approach to conduct rehabilitation in the community (ie, home environment), which aims to enhance an individual's functional ability to perform everyday activities that individuals perceive as important. We investigated the effects of a home-reablement program on different rehabilitation outcomes in people with stroke.

METHODS

A single-blind randomized clinical trial was conducted. Twenty-six people with stroke were randomly assigned to the home-reablement group or control group. For 6 weeks, participants in the home-reablement group received training for activities of daily living (ADL) that they perceived as important but difficult to perform. Participants in the control group received conventional rehabilitation in the hospital. Outcome measures included the Fugl-Meyer Assessment for the upper-extremity (FMA-UE) and the Stroke Impact Scale 3.0 (SIS 3.0) subscales.

RESULTS

No statistically significant differences between the 2 groups were noticed in the FMA-UE and the SIS 3.0 subscales (P = .226-1.000). Small effect size (success rate difference = 0.12-0.25) were noticed in the FMA-UE and the 5 SIS 3.0 subscales. The home-reablement group exhibited a greater proportion of participants with scores greater than the minimal detectable change in the FMA-UE and the 6 SIS 3.0 subscales (ie, strength, ADL/instrumental ADL, mobility, emotion, memory, and participation).

CONCLUSIONS

People with stroke that underwent the 6-week home-reablement program showed potential for improving their motor function, ADL/instrumental ADL, emotion, memory, and activity participation.

Impairment and Compensation in Dexterous Upper-Limb Function After Stroke. From the Direct Consequences of Pyramidal Tract Lesions to Behavioral Involvement of Both Upper-Limbs in Daily Activities

Impairments in dexterous upper limb function are a significant cause of disability following stroke. While the physiological basis of movement deficits consequent to a lesion in the pyramidal tract is well demonstrated, specific mechanisms contributing to optimal recovery are less apparent. Various upper limb interventions (motor learning methods, neurostimulation techniques, robotics, virtual reality, and serious games) are associated with improvements in motor performance, but many patients continue to experience significant limitations with object handling in everyday activities. Exactly how we go about consolidating adaptive motor behaviors through the rehabilitation process thus remains a considerable challenge. An important part of this problem is the ability to successfully distinguish the extent to which a given gesture is determined by the neuromotor impairment and that which is determined by a compensatory mechanism. This question is particularly complicated in tasks involving manual dexterity where prehensile movements are contingent upon the task (individual digit movement, grasping, and manipulation…) and its objective (placing, two step actions…), as well as personal factors (motivation, acquired skills, and life habits…) and contextual cues related to the environment (presence of tools or assistive devices…). Presently, there remains a lack of integrative studies which differentiate processes related to structural changes associated with the neurological lesion and those related to behavioral change in response to situational constraints. In this text, we shall question the link between impairments, motor strategies and individual performance in object handling tasks. This scoping review will be based on clinical studies, and discussed in relation to more general findings about hand and upper limb function (manipulation of objects, tool use in daily life activity). We shall discuss how further quantitative studies on human manipulation in ecological contexts may provide greater insight into compensatory motor behavior in patients with a neurological impairment of dexterous upper-limb function.

Application of Infusion Control System Based on Internet of Things Technology in Joint Orthopedics Nursing Work

In recent years, the Internet of Things technology has flourished, and there have been corresponding practical results in various fields. In medical care, the introduction of Internet of Things technology must also be a new trend in the development of hospital informatization, and it is the development stage of the digital medical process. The traditional infusion system shows that the infusion bottle is not replaced in time, the infusion waiting time is too long, the infusion efficiency is too low, and the existing medical staff is far from meeting the needs of the huge infusion population. Therefore, this article proposes a technology based on the Internet of Things application of the infusion control system in joint orthopedics nursing work to improve the efficiency of infusion in nursing work. This article deeply learns and uses the Internet of Things technology to build a new infusion management and control system, which is applied to joint orthopedics nursing treatment. This paper designs the application research experiment of the infusion control system. Through the Internet of Things technology, the relevant data in the infusion process are uploaded and sent to the network center of the hospital. Nursing staff can directly see the infusion situation directly through the computer console. This article compares and analyzes two different infusion systems and draws conclusions. The infusion ringing rate of the control group was 81.3%, and the infusion ringing rate of the IoT group was 29.8%; the time for timely replacement of the infusion bottle after IoT data control was 13.89 min, compared to 19.76 min before. A variety of data results show that the infusion management and control system based on the Internet of Things technology has played a great role in joint orthopedics care, which can greatly improve the efficiency of infusion, replace the infusion or deal with failures in time for patients, and improve patient satisfaction.

Wearables in der Schlaganfallmedizin

Unter Wearables versteht man in die Kleidung oder in tragbare Geräte integrierte Sensoren, die eine kontinuierliche Langzeitmessung von physiologischen Parametern, wie Herzfrequenz, Blutdruck, Atmung, Bewegung, Hautwiderstand usw. und/oder Bewegungsmustern ermöglichen. In der Schlaganfallmedizin eröffnen Wearables neue Optionen in der Diagnostik, Prävention und Rehabilitation.

Design of smart campus management system based on internet of things technology

With the vigorous promotion of the construction of smart campus by the ministry of education, the development concept of smart campus will have broad application prospects. However, colleges and universities are still at the stage of digital campus and there are many problems left. It is difficult to complete the transition from digital campus to smart campus. The main problem is that the campus data has only been digitized but not informational. The purpose of this article is to study a smart campus management system based on the Internet of Things technology. This research uses the unified data collection source of face recognition terminal hardware products based on the Internet of Things technology, unified management in the background of the system, and calculates and analyzes the data to obtain valuable campus big data. This study designed and implemented a complete smart campus management system by analyzing the system design principles and design goals. This system is mainly divided into the face recognition terminal hardware and smart campus software system based on the Internet of Things. By analyzing the data generated by students and faculty and staff, it can provide a reference for campus managers to improve management quality, and help teachers and students to formulate more efficient learning and teaching and research plans. This article tests the practicability of the system and obtains the user’s satisfaction as 8.0.

Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments

BACKGROUND

Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient's habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties.

METHODS

A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review.

RESULTS

Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm's accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported.

CONCLUSION

This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.

Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study

Background

Recent advancements in wearable sensor technology have shown the feasibility of remote physical therapy at home. In particular, the current COVID-19 pandemic has revealed the need and opportunity of internet-based wearable technology in future health care systems. Previous research has shown the feasibility of human activity recognition technologies for monitoring rehabilitation activities in home environments; however, few comprehensive studies ranging from development to clinical evaluation exist.

Objective

This study aimed to (1) develop a home-based rehabilitation (HBR) system that can recognize and record the type and frequency of rehabilitation exercises conducted by the user using a smartwatch and smartphone app equipped with a machine learning (ML) algorithm and (2) evaluate the efficacy of the home-based rehabilitation system through a prospective comparative study with chronic stroke survivors.

Methods

The HBR system involves an off-the-shelf smartwatch, a smartphone, and custom-developed apps. A convolutional neural network was used to train the ML algorithm for detecting home exercises. To determine the most accurate way for detecting the type of home exercise, we compared accuracy results with the data sets of personal or total data and accelerometer, gyroscope, or accelerometer combined with gyroscope data. From March 2018 to February 2019, we conducted a clinical study with two groups of stroke survivors. In total, 17 and 6 participants were enrolled for statistical analysis in the HBR group and control group, respectively. To measure clinical outcomes, we performed the Wolf Motor Function Test (WMFT), Fugl-Meyer Assessment of Upper Extremity, grip power test, Beck Depression Inventory, and range of motion (ROM) assessment of the shoulder joint at 0, 6, and 12 months, and at a follow-up assessment 6 weeks after retrieving the HBR system.

Results

The ML model created with personal data involving accelerometer combined with gyroscope data (5590/5601, 99.80%) was the most accurate compared with accelerometer (5496/5601, 98.13%) or gyroscope data (5381/5601, 96.07%). In the comparative study, the drop-out rates in the control and HBR groups were 40% (4/10) and 22% (5/22) at 12 weeks and 100% (10/10) and 45% (10/22) at 18 weeks, respectively. The HBR group (n=17) showed a significant improvement in the mean WMFT score (P=.02) and ROM of flexion (P=.004) and internal rotation (P=.001). The control group (n=6) showed a significant change only in shoulder internal rotation (P=.03).

Conclusions

This study found that a home care system using a commercial smartwatch and ML model can facilitate participation in home training and improve the functional score of the WMFT and shoulder ROM of flexion and internal rotation in the treatment of patients with chronic stroke. This strategy can possibly be a cost-effective tool for the home care treatment of stroke survivors in the future.

Trial Registration

Clinical Research Information Service KCT0004818; https://tinyurl.com/y92w978t

IoT Services and Applications in Rehabilitation: An Interdisciplinary and Meta-Analysis Review

Internet of things (IoT) is a designation given to a technological system that can enhance possibilities of connectivity between people and things and has been showing to be an opportunity for developing and improving smart rehabilitation systems and helps in the e-Health area.

OBJECTIVE

to identify works involving IoT that deal with the development, architecture, application, implementation, use of technological equipment in the area of patient rehabilitation. Technology or Method: A systematic review based on Kitchenham's suggestions combined to the PRISMA protocol. The search strategy was carried out comprehensively in the IEEE Xplore Digital Library, Web of Science and Scopus databases with the data extraction method for assessment and analysis consist only of primary studies articles related to the IoT and Rehabilitation of patients.

RESULTS

We found 29 studies that addressed the research question, and all were classified based on scientific evidence.

CONCLUSIONS

This systematic review presents the current state of the art on the IoT in health rehabilitation and identifies findings in interdisciplinary researches in different clinical cases with technological systems including wearable devices and cloud computing. The gaps in IoT for rehabilitation include the need for more clinical randomized controlled trials and longitudinal studies. Clinical Impact: This paper has an interdisciplinary feature and includes areas such as Internet of Things Information and Communication Technology with their application to the medical and rehabilitation domains.

Treadmill-Based Locomotor Training With Robotic Pelvic Assist and Visual Feedback: A Feasibility Study

BACKGROUND AND PURPOSE

Gait asymmetries are common after stroke, and often persist despite conventional rehabilitation. Robots provide training at a greater practice frequency than conventional approaches. However, prior studies of have found the transfer of learned skills outside of the device to be inadequate. The tethered pelvic assist device (TPAD) promotes weight shifting, yet allows users to independently navigate spatiotemporal aspects of gait. The purpose of this study was to evaluate feasibility and preliminary efficacy of a 5-day intervention combining TPAD training with visual feedback and task-specific overground training to promote improved force and stance symmetry in individuals after stroke.

METHODS

After baseline assessments, 11 participants chronically after stroke received 1 hour of practice for 5 consecutive days. Training sessions included visual feedback during TPAD treadmill training followed by overground gait training. Safety, perceived exertion, and adherence were recorded as measures of feasibility. Load and stance symmetry were reassessed after the intervention (posttraining) and again 1 week later.

RESULTS

No adverse events were reported. Mean (SD) perceived exertion (3.61 (0.23)) was low and did not significantly change throughout the intervention. Overall adherence was 96.4%. Load asymmetry was not significantly reduced on the treadmill from baseline to posttraining (P = 0.075). Overground stance symmetry significantly improved on posttraining (F = 8.498, P = 0.002), but was not sustained at follow-up. (See the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A311, which summarizes the study background, methods, and results.) DISCUSSION AND CONCLUSIONS:: Results demonstrate this combined interventional approach was feasible and improved stance symmetry overground, yet further work should consider increasing training intensity and/or duration to induce gains lasting through follow-up.

A Mobile Health App for the Collection of Functional Outcomes After Inpatient Stroke Rehabilitation: Pilot Randomized Controlled Trial

BACKGROUND

Monitoring the functional status of poststroke patients after they transition home is significant for rehabilitation. Mobile health (mHealth) technologies may provide an opportunity to reach and follow patients post discharge. However, the feasibility and validity of functional assessments administered by mHealth technologies are unknown.

OBJECTIVE

This study aimed to evaluate the feasibility, validity, and reliability of functional assessments administered through the videoconference function of a mobile phone-based app compared with administration through the telephone function in poststroke patients after rehabilitation hospitalization.

METHODS

A randomized controlled trial was conducted in a rehabilitation hospital in Southeast China. Participants were randomly assigned to either a videoconference follow-up (n=60) or a telephone follow-up (n=60) group. We measured the functional status of participants in each group at 2-week and 3-month follow-up periods. Half the participants in each group were followed by face-to-face home visit assessments as the gold standard. Validity was assessed by comparing any score differences between videoconference follow-up and home visit assessments, as well as telephone follow-up and home visit assessments. Reliability was assessed by computing agreements between videoconference follow-up and home visit assessments, as well as telephone follow-up and home visit assessments. Feasibility was evaluated by the levels of completion, satisfaction, comfort, and confidence in the 2 groups.

RESULTS

Scores obtained from the videoconference follow-up were similar to those of the home visit assessment. However, most scores collected from telephone administration were higher than those of the home visit assessment. The agreement between videoconference follow-up and home visit assessments was higher than that between telephone follow-up and home visit assessments at all follow-up periods. In the telephone follow-up group, completion rates were 95% and 82% at 2-week and 3-month follow-up points, respectively. In the videoconference follow-up group, completion rates were 95% and 80% at 2-week and 3-month follow-up points, respectively. There were no differences in the completion rates between the 2 groups at all follow-up periods (X²₁=1.6, P=.21 for 2-week follow-up; X²₁=1.9, P=.17 for 3-month follow-up). Patients in the videoconference follow-up group perceived higher confidence than those in the telephone follow-up group at both 2-week and 3-month follow-up periods (X²₃=6.7, P=.04 for 2-week follow-up; X²₃=8.0, P=.04 for 3-month follow-up). The videoconference follow-up group demonstrated higher satisfaction than the telephone follow-up group at 3-month follow-up (X²₃=13.9; P=.03).

CONCLUSIONS

The videoconference follow-up assessment of functional status demonstrates higher validity and reliability, as well as higher confidence and satisfaction perceived by patients, than the telephone assessment. The videoconference assessment provides an efficient means of assessing functional outcomes of patients after hospital discharge. This method provides a novel solution for clinical trials requiring longitudinal assessments.

TRIAL REGISTRATION

chictr.org.cn: ChiCTR1900027626; http://www.chictr.org.cn/edit.aspx?pid=44831&htm=4.

Monitoring the training dose and acute fatigue response during elbow flexor resistance training using a custom-made resistance band

BACKGROUND

Home-based resistance training offers an alternative to traditional, hospital-based or rehabilitation center-based resistance training and has attracted much attention recently. However, without the supervision of a therapist or the assistance of an exercise monitoring system, one of the biggest challenges of home-based resistance training is that the therapist may not know if the patient has performed the exercise as prescribed. A lack of objective measurements limits the ability of researchers to evaluate the outcome of exercise interventions and choose suitable training doses.

OBJECTIVE

To create an automated and objective method for segmenting resistance force data into contraction phase-specific segments and calculate the repetition number and time-under-tension (TUT) during elbow flexor resistance training. A pilot study was conducted to evaluate the performance of the segmentation algorithm and to show the capability of the system in monitoring the compliance of patients to a prescribed training program in a practical resistance training setting.

METHODS

Six subjects (three male and three female) volunteered to participate in a fatigue and recovery experiment (5 min intermittent submaximal contraction (ISC); 1 min rest; 2 min ISC). A custom-made resistance band was used to help subjects perform biceps curl resistance exercises and the resistance was recorded through a load cell. The maximum and minimum values of the force-derivative were obtained as distinguishing features and a segmentation algorithm was proposed to divide the biceps curl cycle into concentric, eccentric and isometric contraction, and rest phases. Two assessors, who were unfamiliar with the study, were recruited to manually pick the visually observed cut-off point between two contraction phases and the TUT was calculated and compared to evaluate performance of the segmentation algorithm.

RESULTS

The segmentation algorithm was programmatically implemented and the repetition number and contraction-phase specific TUT were calculated. During isometric, the average TUT (3.75 ± 0.62 s) was longer than the prescribed 3 s, indicating that most subjects did not perform the exercise as prescribed. There was a good TUT agreement and contraction segment agreement between the proposed algorithm and the assessors.

CONCLUSION

The good agreement in TUT between the proposed algorithm and the assessors indicates that the proposed algorithm can correctly segment the contraction into contraction phase-specific parts, thereby providing clinicians and researchers with an automated and objective method for quantifying home-based elbow flexor resistance training. The instrument is easy to use and cheap, and the segmentation algorithm is programmatically implemented, indicating good application prospect of the method in a practical setting.

Exercise and Parkinson's Disease

Parkinson's disease (PD) is an aging-related neurodegenerative disorder characterized by progressive motor impairment.The etiology of PD is poorly understood but likely involves both genetic and environmental factors; the management of the disease is still with symptomatic therapy without any interference on the progression of neurodegeneration. In the past two decades, the results of a series of prospective cohort studies suggested that lifestyle factors likely modify the risk of developing PD. Among these, physical activity is known to reduce the risk of a wide range of diseases and conditions, including cardiovascular disease, stroke, and diabetes.Recently, a growing body of evidence has suggested that increased physical activity may also reduce the risk of PD and partly improve motor and non-motor symptoms during the disease course.Here we report the main findings on the effect of physical activity on both mobility and cognition either in animal models of PD or in people with PD. We also highlighted the structural and functional links between gait and cognition by reporting evidence from neuroimaging studies.

Home program practices for supporting and measuring adherence in post-stroke rehabilitation: a scoping review

BACKGROUND

After stroke, individuals face a variety of impairments that impact function. Increasingly, rehabilitation for these impairments has moved into the community and home settings through the use of home programs. However, adherence to these programs is often low, limiting effectiveness.

OBJECTIVE

This scoping review investigated home program implementation and measurement of adherence with persons post-stroke to identify commonly reported practices and determine areas for further research.

METHODS

The electronic databases of PubMed, CINAHL, Scopus, Cochrane Database of Systematic Reviews, and PEDro were searched. Studies focused on post-stroke rehabilitation with an independent home program were selected. Qualitative studies, commentaries, and single-case studies were excluded. Title and abstract screenings were completed by two reviewers with a third for tie-breaking. The full-text review was completed by two reviewers using consensus to resolve any differences. Of the 1,197 articles initially found only 6% (n = 70) met criteria for data extraction. Elements for data extraction included: type of study, area of intervention, description of home program, presence of strategies to support adherence, methods to measure adherence and reported adherence.

RESULTS

Most commonly reported strategies to support home practice were the use of technology, personalization, and written directions. Only 20 studies reported achieving adherence at or greater than 75% and 18 studies did not report adherence outcomes.

CONCLUSIONS

Future investigations that directly compare and identify the most effective strategies to support adherence to home programs for this population are warranted. The implementation of guidelines for reporting adherence to home programs is recommended.

Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment

Stroke is one of the main causes of long-term disability worldwide, placing a large burden on individuals and society. Rehabilitation after stroke consists of an iterative process involving assessments and specialized training, aspects often constrained by limited resources of healthcare centers. Wearable technology has the potential to objectively assess and monitor patients inside and outside clinical environments, enabling a more detailed evaluation of the impairment and allowing the individualization of rehabilitation therapies. The present review aims to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity. We summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. Finally, suggestions concerning data acquisition and processing to guide future studies performed by clinicians and engineers alike are provided.

The problem with self-management: Problematising self-management and power using a Foucauldian lens in the context of stroke care and rehabilitation

Self-management is a concept which is now firmly established in Western healthcare policy and practice. However, the term remains somewhat ambiguous, multi-faceted and contentious. This is evident in stroke care and rehabilitation, in which a self-management approach is increasingly adopted and advocated, yet interpreted in different ways, resulting in contradictions and tensions around control, responsibility, power and discipline. This paper aims to further our understanding of tensions and contradictions in stroke self-management, by critically examining contemporary self-management practices. We use a Foucauldian theoretical lens to explore the various power dynamics in the operationalisation of self-management, in addition to the complexity of the term self-management itself. Conducting a secondary analysis of interview and focus group data from the Self-Management VOICED study, supplemented with analysis of relevant documentary evidence from policy and practice, we describe the multiple aspects of power in operation. These include rhetorical, hierarchical, personal and mutual forms of power, representing interweaving dynamics evident in the data. These aspects of power demonstrate underlying agendas and tacit and explicit understandings of self-management which exist in clinical practice. These aspects of power also give insight into the multiple identities of ‘self-management’, acting as a simultaneous repressor and liberator, directly in keeping with Foucauldian thinking. The findings are also consistent with Foucault’s notions of bodily docility, discussions around governance and biopower, and contemporary discipline. Our analysis positions self-management as a highly nuanced and complex concept, which can fluctuate in its conceptualisation depending on the structures, routines, and the individual. We encourage healthcare professionals, policymakers and commissioners in the field of self-management to reflect on these complexities, to make transparent their assumptions and to explicitly position their own practice accordingly.

Effects of Home-Based Robotic Therapy Involving the Single-Joint Hybrid Assistive Limb Robotic Suit in the Chronic Phase of Stroke: A Pilot Study

Introduction

Robotic therapy has drawn attention in the rehabilitation field including home-based rehabilitation. A previous study has reported that home-based therapy could be more effective for increasing upper limb activity than facility-based therapy. The single-joint hybrid assistive limb (HAL-SJ) is an exoskeleton robot developed according to the interactive biofeedback theory, and several studies have shown its effectiveness for upper limb function in stroke patients. A study of home-based robotic therapy has shown to enhance rehabilitation effectiveness for stroke patient with a paretic upper limb. However, home-based therapy involving a HAL-SJ in stroke patients with paretic upper limbs has not been investigated. The present study aimed to investigate paretic upper limb activity and function with home-based robotic therapy involving a HAL-SJ in stroke patients.

Materials and Methods

A home-based robotic therapy program involving a HAL-SJ was performed for 30 min per session followed by standard therapy for 30 min per session, 2 times a week, for 4 weeks (i.e., completion of all 8 sessions involved 8 h of rehabilitation), at home. After the intervention, patients were followed up by telephone and home visits for 8 weeks. The paretic upper limb activity and function were assessed using the Motor Activity Log (MAL; amount of use (AOU)), arm triaxial accelerometry (laterality index (LI)), the Fugl–Meyer assessment (FMA), and the action research arm test (ARAT), at baseline and week 4 and week 12 after the start of training.

Results

The study included 10 stroke patients (5 men; mean age, 61.1 ± 7.1 years). The AOU scores and LI significantly improved at week 4 after the start of training (p<0.05). However, no significant changes were observed in the LI at week 12 (p=0.161) and the FMA scores at both week 4 and week 12 (p=0.059 and p=0.083, respectively). The ARAT scores significantly improved at both week 4 and week 12 (p<0.05).

Conclusion

Home-based robotic therapy combined with conventional therapy could be a valuable approach for increasing paretic upper limb activity and maintaining paretic upper limb function in the chronic phase of stroke.