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Robotic bimanual arm training improves upper limb function in children with hemiparesis: a feasibility study
Talita Campos1,2, Shivakeshavan Ratnadurai-Giridharan2,3, Marion-Victoria Wairimu2
1Department of Nursing, Columbia University, New York, NY, United States.
Bimanual robotic training improved hand function in children with hemiplegia. This novel approach shows promise for pediatric rehabilitation, enhancing motor skills through engaging, repetitive practice.
Area of Science:
- Pediatric rehabilitation
- Neurorehabilitation
- Robotics in medicine
Background:
- Bimanual training offers superior upper limb functional gains in children with hemiparesis compared to unimanual training.
- Repetitive, skilled practice is essential for effective motor interventions in pediatric hemiplegia.
Purpose of the Study:
- To assess the feasibility and effectiveness of a bimanual-to-unimanual robotic training device for improving hand function in children with hemiplegia.
- To evaluate the impact of robotic training on specific motor outcome measures: Assisting Hand Assessment (AHA), Box and Blocks Test (BBT), and Jebsen-Taylor Test of Hand Function (JTTHF).
Main Methods:
- A feasibility study involving 8 children (age 5-17) with hemiparesis using the Bimanual Arm Trainer (BAT) robotic device.
- The BAT facilitates mirrored bimanual movements and incorporates a gaming interface for repetitive practice, offering both bimanual and unimanual training components.
- Participants completed 18 training sessions over 9 weeks (2x/week).
Main Results:
- Despite study interruption by COVID-19, significant improvements were observed in bimanual function as measured by the Assisting Hand Assessment (AHA).
- No significant changes were noted in range of motion.
Conclusions:
- The bimanual-to-unimanual robotic training approach is feasible and effective for enhancing bimanual hand function in children with hemiplegia.
- Children responded positively to the robotic device, providing valuable feedback for interface improvement.
- Further research is required to establish optimal training parameters for maximizing functional gains.
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