Robot-assisted
therapy is regarded as an effective and reliable method for the
delivery of highly repetitive training that is needed to trigger
neuroplasticity following a stroke. However, the lack of fully adaptive
assist-as-needed control of the robotic devices and an inadequate
immersive virtual environment that can promote active participation
during training are obstacles hindering the achievement of better
training results with fewer training sessions required. This study thus
focuses on these research gaps by combining these 2 key components into a
rehabilitation system, with special attention on the rehabilitation of
fine hand motion skills. The effectiveness of the proposed system is
tested by conducting clinical trials on a chronic stroke patient and
verified through clinical evaluation methods by measuring the key
kinematic features such as active range of motion (ROM), finger
strength, and velocity. By comparing the pretraining and post-training
results, the study demonstrates that the proposed method can further
enhance the effectiveness of fine hand motion rehabilitation training by
improving finger ROM, strength, and coordination.
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Correspondence information about the author PhD Xianwei Huang
Email the author PhD Xianwei Huang
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