Finger motor dysfunction caused by nerve injury or musculoskeletal diseases seriously affects the quality of life of patients, while traditional rehabilitation methods lack accuracy and scalability. Although exoskeleton (Exo) robots offer promising solutions, existing designs still face challenges in terms of structural rigidity, control accuracy, and human-computer interaction for finger joint rehabilitation. This paper systematically reviews the finger Exo technology, with a focus on analyzing three key aspects: structural design, driving mechanism, and interaction mode. Through literature comparison and analysis, it is found that the rigid structure has a relatively high torque output (about 10.3 Newtons of grip), but insufficient comfort, while the flexible design improves wear resistance (weight 150-200 grams), but sacrifices load-bearing capacity. Hybrid power solutions and advanced drivers show the potential to bridge these gaps. Human-computer interaction technologies such as Surface electromyography (SEMG) can achieve a motion range of 58.5° for joints, but there is a problem of signal noise. The key research results reveal the trade-offs among various performance indicators: the balance between accuracy (0.2 mm position error) and adaptability, as well as the trade-off between force output (15 N·m torque) and portability. The future development direction emphasizes artificial intelligence-driven control algorithms, modular design, and the use of cost-effective materials to enhance clinical applicability. This study provides a systematic framework for optimizing finger Exo, indicating the need for multidisciplinary innovation to achieve personalized and efficient rehabilitation treatment.
Mingqin Wen (Tue,) studied this question.
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