Comparing the effectiveness of bimanual and unimanual mirror therapy in unilateral neglect after stroke: A pilot study

2021 ◽  
pp. 1-9
Author(s):  
Tae Yong Sim ◽  
Jae Sung Kwon
Keyword(s):  
Pilot Study ◽  
Activities Of Daily Living ◽  
Barthel Index ◽  
Daily Living ◽  
Major Obstacle ◽  
Mirror Therapy ◽  
Stroke Patients ◽  
Unilateral Neglect ◽  
Korean Version ◽  
Significant Difference

BACKGROUND: Unilateral neglect in stroke patients is a major obstacle to rehabilitation, which is a great challenge for therapists. OBJECTIVE: This study aimed to compare the effectiveness of bimanual mirror therapy (BMT) and unimanual mirror therapy (UMT), the two protocols of mirror therapy, for the reduction of the symptoms of unilateral neglect in stroke patients. METHODS: Twenty-eight individuals were randomly assigned to the UMT or BMT groups. Both groups received mirror therapy for 30 minutes per day, 5 days a week, for a period of 4 weeks. The Star Cancelation Test (SCT), Line Bisection Test (LBT), Picture Scanning test (PST), and Korean Catherine Bergego Scale (K-CBS) were used to measure the change in unilateral neglect, and the Korean version of the Modified Barthel Index (K-MBI) was used to evaluate activities of daily living (ADL). RESULTS: The results of SCT, LBT, PST, and K-CBS showed significant decreases in unilateral neglect in both groups (p <  0.05). K-MBI improved significantly in both groups (p <  0.05). There were significant differences between the two groups in the unilateral neglect tests (p <  0.05), but no significant difference in ADL evaluation (p >  0.05). CONCLUSIONS: Mirror therapy protocols can be applied to treat unilateral neglect in stroke patients. However, BMT may be more beneficial for reducing the symptoms of unilateral neglect.

Effects of short-term upper limb robot-assisted therapy on the rehabilitation of sub-acute stroke patients

2020 ◽  
pp. 1-9
Author(s):  
Shangrong Jiang ◽  
Hong You ◽  
Weijing Zhao ◽  
Min Zhang
Keyword(s):  
Activities Of Daily Living ◽  
Acute Stroke ◽  
Motor Function ◽  
Upper Limb ◽  
Daily Living ◽  
Muscle Tone ◽  
Stroke Patients ◽  
Robot Assisted ◽  
Short Term ◽  
Significant Difference

BACKGROUND: Robot-assisted therapy (RT) has become a promising stroke rehabilitation intervention. OBJECTIVE: To examine the effects of short-term upper limb RT on the rehabilitation of sub-acute stroke patients. METHODS: Subjects were randomly assigned to the RT group (n= 23) or conventional rehabilitation (CR) group (n= 22). All subjects received conventional rehabilitation therapy for 30 minutes twice a day, for 2 weeks. In addition, the RT group received RT for 30 minutes twice a day, for 2 weeks. The outcomes before treatment (T0) and at 2 weeks (T1) and 1 month follow-up (T2) were evaluated in the patients using the upper limb motor function test of the Fugl-Meyer assessment (FMA) the Motricity Index (MI), the Modified Ashworth Scale (MAS), the Functional Independence Measure (FIM), and the Barthel Index (BI). RESULTS: There were significant improvements in motor function scales (P< 0.001 for FMA and MI) and activities of daily living (P< 0.001 for FIM and BI) but without muscle tone (MAS, P> 0.05) in the RT and CR groups. Compared to the CR group, the RT group showed improvements in motor function and activities of daily living (P< 0.05 for FMA, MI, FIM, BI) at T1 and T2. There was no significant difference between the two groups in muscle tone (MAS, P> 0.05). CONCLUSIONS: RT may be a useful tool for sub-acute stroke patients’ rehabilitation.

scholarly journals Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study

2021 ◽  
Vol 18 (4) ◽  
pp. 1634
Author(s):  
Pin-Wei Chen ◽  
Nathan A. Baune ◽  
Igor Zwir ◽  
Jiayu Wang ◽  
Victoria Swamidass ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Wearable Sensors ◽  
Classification Model ◽  
Support Vector ◽  
Stroke Patients ◽  
Extreme Gradient Boosting ◽  
Testing Data

Measuring activities of daily living (ADLs) using wearable technologies may offer higher precision and granularity than the current clinical assessments for patients after stroke. This study aimed to develop and determine the accuracy of detecting different ADLs using machine-learning (ML) algorithms and wearable sensors. Eleven post-stroke patients participated in this pilot study at an ADL Simulation Lab across two study visits. We collected blocks of repeated activity (“atomic” activity) performance data to train our ML algorithms during one visit. We evaluated our ML algorithms using independent semi-naturalistic activity data collected at a separate session. We tested Decision Tree, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) for model development. XGBoost was the best classification model. We achieved 82% accuracy based on ten ADL tasks. With a model including seven tasks, accuracy improved to 90%. ADL tasks included chopping food, vacuuming, sweeping, spreading jam or butter, folding laundry, eating, brushing teeth, taking off/putting on a shirt, wiping a cupboard, and buttoning a shirt. Results provide preliminary evidence that ADL functioning can be predicted with adequate accuracy using wearable sensors and ML. The use of external validation (independent training and testing data sets) and semi-naturalistic testing data is a major strength of the study and a step closer to the long-term goal of ADL monitoring in real-world settings. Further investigation is needed to improve the ADL prediction accuracy, increase the number of tasks monitored, and test the model outside of a laboratory setting.

scholarly journals Integration of a Passive Exoskeleton and a Robotic Supernumerary Finger for Grasping Compensation in Chronic Stroke Patients: The SoftPro Wearable System

2021 ◽  
Vol 8 ◽  
Author(s):  
Gionata Salvietti ◽  
Leonardo Franco ◽  
Martin Tschiersky ◽  
Gerjan Wolterink ◽  
Matteo Bianchi ◽  
...  
Keyword(s):  
Pilot Study ◽  
Activities Of Daily Living ◽  
Upper Limb ◽  
Daily Living ◽  
Assistive Technologies ◽  
Robotic Assistance ◽  
Stroke Patients ◽  
Wearable System ◽  
Passive Exoskeleton ◽  
And Robotics

Upper-limb impairments are all-pervasive in Activities of Daily Living (ADLs). As a consequence, people affected by a loss of arm function must endure severe limitations. To compensate for the lack of a functional arm and hand, we developed a wearable system that combines different assistive technologies including sensing, haptics, orthotics and robotics. The result is a device that helps lifting the forearm by means of a passive exoskeleton and improves the grasping ability of the impaired hand by employing a wearable robotic supernumerary finger. A pilot study involving 3 patients, which was conducted to test the capability of the device to assist in performing ADLs, confirmed its usefulness and serves as a first step in the investigation of novel paradigms for robotic assistance.

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