The effects of Virtual Reality Training with Upper Limb Sensory Exercise Stimulation on the AROM of Upper Limb Joints, Function, and Concentration in Chronic Stroke Patients

2019 ◽  
Vol 30 (02) ◽  
pp. 86-94
Author(s):  
Dong Hoon Kim ◽  
Kyung-Hun Kim ◽  
Suk-Min Lee
Keyword(s):  
Virtual Reality ◽  
Cognitive Ability ◽  
Upper Extremity ◽  
Upper Limb ◽  
Sensory Stimulation ◽  
Chronic Stroke ◽  
Virtual Reality Training ◽  
Stroke Patients ◽  
Angle Function ◽  
Virtual Reality Rehabilitation

Abstract Objective We investigated the effect of upper limb sensory stimulation and virtual reality rehabilitation (SMVR) on upper extremity active joint angle, function and cognitive ability in chronic stroke patients. Methods A total of 30 patients were randomly divided into SMVR group and CON group. SMVR group was performed 60 min three times a week for 8 weeks in upper limb sensory stimulation and robot virtual reality rehabilitation. CON group performed conservative treatment and peripheral joint movement for 60 min. The upper limb function was measured by the Jebsen-Taylor hand function test (JTT) and the cognitive ability test was performed by the Stroop test (ST) and Trail making test (TMT). Results There was a significant difference (P<0.05) between before and after training in both groups, and SMVR group showed significant improvement in both groups. Conclusions In this study, we confirmed that robot virtual reality training in combination with limb motion stimulation for stroke patients positively affects the angle, function, and concentration of upper extremity active joints in chronic stroke patients.

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 Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke

2020 ◽  
Vol 34 (5) ◽  
pp. 428-439 ◽  
Author(s):  
Ceren Tozlu ◽  
Dylan Edwards ◽  
Aaron Boes ◽  
Douglas Labar ◽  
K. Zoe Tsagaris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Upper Extremity ◽  
Upper Limb ◽  
Motor Impairment ◽  
Chronic Stroke ◽  
Response To Therapy ◽  
Stroke Patients ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Difference

Background. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. Objectives. The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. Methods. A total of 102 patients (female: 31%, age 61 ± 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R2. Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median [Formula: see text] P < .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere. Conclusion. Machine learning methods may enable clinicians to accurately predict a chronic stroke patient’s postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients’ response to therapy and, therefore, could be included in prospective studies.

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