Papers
1
Total Citations
17
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About
Dan Tang has made significant contributions to the field of rehabilitation engineering, with a primary focus on electromyography (EMG)-based pattern recognition for motor recovery in stroke patients. His most cited work, "Pattern recognition based forearm motion classification for patients with chronic hemiparesis" (2013, 17 citations), pioneered the application of machine learning techniques to decode muscular activity from EMG signals, enabling the classification of multiple forearm motions in individuals with chronic hemiparesis. This research directly addresses a critical challenge in active rehabilitation—translating rich neuromuscular signals into intuitive, real-time control for assistive devices. Tang’s work demonstrated that pattern recognition algorithms could effectively differentiate between distinct movement classes even in impaired muscles, laying groundwork for more responsive myoelectric prosthetics and neurorehabilitation systems. By bridging signal processing and clinical application, his studies have informed the design of patient-specific therapies that leverage residual muscular activity. With a citation count reflecting the foundational nature of his contributions, Tang’s research continues to influence engineers and clinicians seeking to restore motor function through intelligent, adaptive technologies.
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