The hand is involved very deeply in our lives in daily activities. When a person loses some hand function, their life can be greatly affected. The use of robotic rehabilitation to assist patients in performing daily actions might help alleviate this problem. However, how to meet individual needs is a major problem in the application of robotic rehabilitation. A biomimetic system (artificial neuromolecular system, ANM) implemented on a digital machine is proposed to deal with the above problems. Two important biological features (structure-function relationship and evolutionary friendliness) are incorporated into this system. With these two important features, the ANM system can be shaped to meet the specific needs of each individual. In this study, the ANM system is used to help patients with different needs perform 8 actions similar to those that people use in everyday life. The data source of this study is our previous research results (data of 30 healthy people and 4 hand patients performing 8 activities of daily life). The results show that while each patient's hand problem is different, the ANM can successfully translate each patient's hand posture into normal human motion. In addition, the system can respond to this difference smoothly rather than dramatically when the patient's hand motions vary both temporally (finger motion sequence) and spatially (finger curvature).
Keywords: computational intelligence; evolutionary learning; robotic control; self-organizing learning.