scholarly journals Development of the Home based Virtual Rehabilitation System (HoVRS) to remotely deliver an intense and customized upper extremity training

2020 ◽  
Author(s):  
Qinyin Qiu ◽  
Amanda Cronce ◽  
Jigna Patel ◽  
Gerard G Fluet ◽  
Ashley Mont ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Chronic Stroke ◽  
Hand Rehabilitation ◽  
Kinematic Data ◽  
Virtual Rehabilitation ◽  
Home Based ◽  
Rehabilitation System ◽  
Kinematic Measures ◽  
And Training ◽  
Training Protocol

Abstract Background: After stroke, sustained hand rehabilitation training is required for continuous improvement and maintenance of distal function. Methods: In this paper, we present a system designed and implemented in our lab: the Home based Virtual Rehabilitation System (HoVRS). Fifteen subjects with chronic stroke were recruited to test the feasibility of the system as well as to refine the design and training protocol to prepare for a future efficacy study. HoVRS was placed in subjects’ homes, and subjects were asked to use the system at least 15 minutes every weekday for 3 months (12 weeks) with limited technical support and remote clinical monitoring. Results: All subjects completed the study without any adverse events. Subjects on average spent 13.5 hours using the system. Clinical and kinematic data were collected pre and post study in the subject’s home. Subjects demonstrated a mean increase of 5.2 (SEM=0.69) on the Upper Extremity Fugl-Meyer Assessment (UEFMA). They also demonstrated improvements in six measurements of hand kinematics. In addition, a combination of these kinematic measures was able to predict a substantial portion of the variability in the subjects’ UEFMA score. Conclusion: Persons with chronic stroke were able to use the system safely and productively with minimal supervision resulting in measurable improvements in upper extremity function.

scholarly journals Development of the Home based Virtual Rehabilitation System (HoVRS) to remotely deliver an intense and customized upper extremity training

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Qinyin Qiu ◽  
Amanda Cronce ◽  
Jigna Patel ◽  
Gerard G. Fluet ◽  
Ashley J. Mont ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Chronic Stroke ◽  
Hand Rehabilitation ◽  
Kinematic Data ◽  
Virtual Rehabilitation ◽  
Home Based ◽  
Rehabilitation System ◽  
Kinematic Measures ◽  
And Training ◽  
Training Protocol

Abstract Background After stroke, sustained hand rehabilitation training is required for continuous improvement and maintenance of distal function. Methods In this paper, we present a system designed and implemented in our lab: the Home based Virtual Rehabilitation System (HoVRS). Fifteen subjects with chronic stroke were recruited to test the feasibility of the system as well as to refine the design and training protocol to prepare for a future efficacy study. HoVRS was placed in subjects’ homes, and subjects were asked to use the system at least 15 min every weekday for 3 months (12 weeks) with limited technical support and remote clinical monitoring. Results All subjects completed the study without any adverse events. Subjects on average spent 13.5 h using the system. Clinical and kinematic data were collected pre and post study in the subject’s home. Subjects demonstrated a mean increase of 5.2 (SEM = 0.69) on the Upper Extremity Fugl-Meyer Assessment (UEFMA). They also demonstrated improvements in six measurements of hand kinematics. In addition, a combination of these kinematic measures was able to predict a substantial portion of the variability in the subjects’ UEFMA score. Conclusion Persons with chronic stroke were able to use the system safely and productively with minimal supervision resulting in measurable improvements in upper extremity function.

scholarly journals Development of the Home based Virtual Rehabilitation System (HoVRS) to Remotely Deliver an Intense and Customized Upper Extremity Training

2020 ◽  
Author(s):  
Qinyin Qiu ◽  
Amanda Cronce ◽  
Jigna Patel ◽  
Gerard G Fluet ◽  
Ashley Mont ◽  
...  
Keyword(s):  
Hand Function ◽  
Chronic Stroke ◽  
Hand Rehabilitation ◽  
Kinematic Data ◽  
Virtual Rehabilitation ◽  
Home Based ◽  
Rehabilitation System ◽  
Kinematic Measures ◽  
And Training ◽  
Training Protocol

Abstract Background: After stroke, sustained hand rehabilitation training is required for continuous improvement and maintenance of distal function. Methods: In this paper, we present a system designed and implemented in our lab: the Home based Virtual Rehabilitation System (HoVRS). Fifteen subjects with chronic stroke were recruited to test the feasibility of the system as well as to refine the design and training protocol to prepare for a future efficacy study. HoVRS was placed in subjects’ homes, and subjects were asked to use the system at least 15 minutes every weekday for 3 months (12 weeks) with limited technical support and remote clinical monitoring. Results: All patients completed the study without any adverse events. Subjects on average spent 13.5 hours using the system. Clinical and kinematic data were collected pre and post study. The whole group improved on the Fugl-Meyer (FM) assessment and on six kinematic measurements. In addition, a combination of these kinematic measures was able to predict a substantial portion of subjects’ FM scores. Conclusion: The outcomes of this pilot study warrant further investigation of the system’s ability to promote recovery of hand function in subacute and chronic stroke.

scholarly journals Virtual Rehabilitation of the Paretic Hand and Arm in Persons With Stroke: Translation From Laboratory to Rehabilitation Centers and the Patient's Home

2021 ◽  
Vol 12 ◽  
Author(s):  
Gerard Fluet ◽  
Qinyin Qiu ◽  
Jigna Patel ◽  
Ashley Mont ◽  
Amanda Cronce ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Hand Function ◽  
Treatment Modalities ◽  
Virtual Rehabilitation ◽  
Rehabilitation Centers ◽  
Home Based ◽  
Definitive Solution ◽  
And Training ◽  
Upper Extremity Movement ◽  
Physiological Heterogeneity

The anatomical and physiological heterogeneity of strokes and persons with stroke, along with the complexity of normal upper extremity movement make the possibility that any single treatment approach will become the definitive solution for all persons with upper extremity hemiparesis due to stroke unlikely. This situation and the non-inferiority level outcomes identified by many studies of virtual rehabilitation are considered by some to indicate that it is time to consider other treatment modalities. Our group, among others, has endeavored to build on the initial positive outcomes in studies of virtual rehabilitation by identifying patient populations, treatment settings and training schedules that will best leverage virtual rehabilitation's strengths. We feel that data generated by our lab and others suggest that (1) persons with stroke may adapt to virtual rehabilitation of hand function differently based on their level of impairment and stage of recovery and (2) that less expensive, more accessible home based equipment seems to be an effective alternative to clinic based treatment that justifies continued optimism and study.

Abstract WP205: Home-Based Virtual Reality Therapy for Hand Recovery After Stroke

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Adam MacLellan ◽  
Catherine Legault ◽  
Alay Parikh ◽  
Leonel Lugo ◽  
Stephanie Kemp ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Upper Extremity ◽  
Large Scale ◽  
Controlled Trial ◽  
Hand Function ◽  
Chronic Stroke ◽  
Stroke Survivors ◽  
Hand Rehabilitation ◽  
Stroke Patients ◽  
Home Based

Background: Stroke is the leading cause of disability worldwide, with many stroke survivors having persistent upper limb functional impairment. Aside from therapist-directed rehabilitation, few efficacious recovery tools are available for use by stroke survivors in their own home. Game-based virtual reality systems have already shown promising results in therapist-supervised settings and may be suitable for home-based use. Objective: We aimed to assess the feasibility of unsupervised home-based use of a virtual reality device for hand rehabilitation in stroke survivors. Methodology: Twenty subacute/chronic stroke patients with upper extremity impairment were enrolled in this prospective single-arm study. Participants were instructed to use the Neofect Smart Glove 5 days per week for 8 weeks, in single sessions of 50 minutes or two 25-minute sessions daily. We measured (1) compliance to prescribed rehabilitation dose, (2) patient impression of the intervention, and (3) efficacy measures including the upper extremity Fugl-Meyer (UE-FM), the Jebsen-Taylor hand function test (JTHFT) and the Stroke Impact Scale (SIS). Results: Seven subjects (35%) met target compliance of 40 days use, and 6 subjects (30%) used the device for 20-39 days; there were no age or gender differences in use. Subjective patient experience was favorable, with ninety percent of subjects reporting satisfaction with their overall experience, and 80% reporting perceived improvement in hand function (figure 1). There was a mean improvement of 26.6±48.8 seconds in the JTHFT ( p =0.03) and 16.1±15.3 points in the domain of the SIS that assesses hand function ( p <0.01). There was a trend towards improvement in the UE-FM (2.2±5.5 points, p =0.10). Conclusions: A novel virtual reality gaming device is suitable for unsupervised use in stroke patients and may improve hand/arm function in subacute/chronic stroke patients. A large-scale randomized controlled trial is needed to confirm these results.

scholarly journals 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 (Preprint)

2019 ◽  
Author(s):  
Sang Hoon Chae ◽  
Yushin Kim ◽  
Kyoung-Soub Lee ◽  
Hyung-Soon Park
Keyword(s):  
Machine Learning ◽  
Home Care ◽  
Comparative Study ◽  
Internal Rotation ◽  
Chronic Stroke ◽  
Control Group ◽  
Stroke Survivors ◽  
Home Based ◽  
Rehabilitation System ◽  
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 (<i>P</i>=.02) and ROM of flexion (<i>P</i>=.004) and internal rotation (<i>P</i>=.001). The control group (n=6) showed a significant change only in shoulder internal rotation (<i>P</i>=.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. CLINICALTRIAL Clinical Research Information Service KCT0004818; https://tinyurl.com/y92w978t

scholarly journals 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

10.2196/17216 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e17216
Author(s):  
Sang Hoon Chae ◽  
Yushin Kim ◽  
Kyoung-Soub Lee ◽  
Hyung-Soon Park
Keyword(s):  
Machine Learning ◽  
Home Care ◽  
Comparative Study ◽  
Internal Rotation ◽  
Chronic Stroke ◽  
Control Group ◽  
Stroke Survivors ◽  
Home Based ◽  
Rehabilitation System ◽  
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

scholarly journals Development of a Compensation-aware Virtual Rehabilitation System for Upper Extremity Rehabilitation in Community-Dwelling Older Adults with Stroke

2021 ◽  
Author(s):  
Zhiqiang Luo ◽  
Audrey Ei-Ping Lim ◽  
Ponraj Durairaj ◽  
Kim Kiow Tan ◽  
Verawaty Verawaty
Keyword(s):  
Older Adults ◽  
Upper Extremity ◽  
Community Dwelling ◽  
Virtual Rehabilitation ◽  
Motor Practice ◽  
Significant Difference ◽  
Rehabilitation System ◽  
Before And After ◽  
Upper Extremity Rehabilitation ◽  
Community Dwelling Older Adults

Abstract Background: Compensatory movements are commonly observed in older adults with stroke when they take motor practice for rehabilitation, which could limit their motor recovery.Aim: This study aims to develop one virtual rehabilitation system (VRS) that can detect and reduce compensatory movements to improve the quality of upper extremity (UE) movements and hence the outcome of rehabilitation in community-dwelling older adults with stroke. Method: To design and validate the algorithm of compensation detection equipped in VRS, a study was first conducted to recruit 17 healthy and 6 stroke participants to identify and quantify compensatory movements when they played rehabilitation games provided by the VRS. Then a pilot study was conducted to test the feasibility and efficacy of the VRS deployed in community, where 18 stroke participants were assigned to either virtual reality (VR) group or conventional treatment (CT) group, and each participant underwent 10 sessions of an additional 6 minutes of VR games or CT respectively, on top of their usual rehabilitation programme. Participants were assessed before and after interventions using Fugl-Meyer Assessment-Upper Extremity (FMA-UE), Wolf Motor Function Test(WMFT), Stroke Rehabilitation Motivation Scale (SRMS), Range of Motion (ROM) measurements and the number of compensatory movements.Results: VR group demonstrated a trend in reduction of trunk and upper-extremity compensations, increased intrinsic motivation scores, and statistically significant improvements in FMA-UE (p=0.045) and WMFT (p=0.009, p=0.0355) scores. There was, however, no significant difference in all outcome measures between two groups. Conclusion: The compensation-aware VRS demonstrates a trend towards reduced compensation and higher motivation level, which could be an effective adjunct to the conventional therapy with less supervision from a therapist as well as be potentially deployed in a community center or at an elder adult’s home.

scholarly journals Novel integrative rehabilitation system for the upper extremity: Design and usability evaluation

2021 ◽  
Vol 8 ◽  
pp. 205566832110128
Author(s):  
Grigore Burdea ◽  
Nam Kim ◽  
Kevin Polistico ◽  
Ashwin Kadaru ◽  
Doru Roll ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Usability Evaluation ◽  
Ease Of Use ◽  
The Novel ◽  
Robotic Rehabilitation ◽  
Virtual Rehabilitation ◽  
Therapeutic Game ◽  
Rehabilitation System ◽  
Bilateral Training ◽  
Bimanual Training

Purpose Design and test the usability of a novel virtual rehabilitation system for bimanual training of gravity supported arms, pronation/supination, grasp strengthening, and finger extension. Methods A robotic rehabilitation table, therapeutic game controllers, and adaptive rehabilitation games were developed. The rehabilitation table lifted/lowered and tilted up/down to modulate gravity loading. Arms movement was measured simultaneously, allowing bilateral training. Therapeutic games adapted through a baseline process. Four healthy adults performed four usability evaluation sessions each, and provided feedback using the USE questionnaire and custom questions. Participant’s game play performance was sampled and analyzed, and system modifications made between sessions. Results Participants played four sessions of about 50 minutes each, with training difficulty gradually increasing. Participants averaged a total of 6,300 arm repetitions, 2,200 grasp counts, and 2,100 finger extensions when adding counts for each upper extremity. USE questionnaire data averaged 5.1/7 rating, indicative of usefulness, ease of use, ease of learning, and satisfaction with the system. Subjective feedback on the custom evaluation form was 84% favorable. Conclusions The novel system was well-accepted, induced high repetition counts, and the usability study helped optimize it and achieve satisfaction. Future studies include examining effectiveness of the novel system when training patients acute post-stroke.

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