Achieving personalized wrist training: apply human factors in the control strategy of rehabilitation robot

Abstract: The wrist is severe based on wear and tear caused by stroke and the rehabilitation process is very complex, nearly 1/3 of stroke patients still have serious upper limb dysfunction after half a year of rehabilitation treatment. The wrist rehabilitation process is very complex, and normally be divided according to the human recovery stage into multiple levels, and each stage requires different rehabilitation treatment methods. For example, according to the well-used Brunnstrom motor assessment method, the motor function of patients is divided into six stages from I to VI, and the rehabilitation process of patients can be roughly divided into early, middle and late terms.Although rehabilitation robot plays more and more important role in the wrist rehabilitation training, most of the existing research focus only on mechanical, electronic or controller design, and ergonomics is rarely discussed, resulting in the lack of consideration of human factors in wrist rehabilitation robot. Moreover, the robot control strategy associated rehabilitation theory is seldom studied, which hardly realize personalized training with different stage. Aiming at the problems involved in the above three rehabilitation stages, there are corresponding comprehensive rehabilitation strategies, namely passive rehabilitation training, assisted rehabilitation training and resistance rehabilitation training. Thus, this paper focus on the control strategies design of wrist rehabilitation robot that meets the requirements of human factors according to the different rehabilitation mechanisms and needs of patients at each stage. To define our research methodologies, we first constructed the methodological framework diagram and applied ergonomics to the control strategy of wrist rehabilitation robot. Apart from that, passive, assisted and resistant rehabilitation training modes are set for individuals in different stages of rehabilitation to realize personalized rehabilitation training. In addition, we evaluated the device satisfaction with the subjective evaluation scale. More specifically, the subjective scale was designed based on the Likert scale, and was set up in the order in which the interaction occurred. After the scale was made, we used SPSS to analyze the reliability and validity of the scale. Subsequently, a questionnaire was administered on the non-probability sample. The statistical analysis of the data by one-way ANOVA found that the device was quite comfortable. In conclusion, our NPU-Wrist operates on human factors throughout the design process and provides a valuable approach for subsequent rehabilitation robots.

Keywords: Human factor, wrist rehabilitation, robot system design, personalized training

DOI: 10.54941/ahfe1003414

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