医用電子と生体工学
Online ISSN : 2185-5498
Print ISSN : 0021-3292
ISSN-L : 0021-3292
機能的電気刺激のための人工神経回路を用いた刺激パターン生成に関する基礎研究
村上 肇町野 保渡辺 高志二見 亮弘星宮 望半田 康延
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1997 年 35 巻 4 号 p. 407-413

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Functional Electrical Stimulation (FES) is very useful to restore motor functions of paralyzed extremities. It has been already reported that coordinated movements of the paralyzed upper limb of the quadriplegic patients could be restored by FES with electromyogram (EMG)-based stimulus patterns. The stimulus patterns based on EMG analysis reflect the cooperative activation of muscles. However it is necessary to measure EMG signals from many normal subjects every time when we try to restore a new movement. Hence we have studied a creating method of stimulus patterns for various movements. Stimulus patterns were created with the inverse model of the controlled object, i. e., the musculoskeletal system of the subject. Here we made Artificial Neural Network (ANN) learn the inverse model by the direct inverse modeling. Especially in this paper, the controlled object which has redundancy in an angle-stimulus voltage characteristic was considered. The direct inverse modeling method is not applicable to such redundant object. We introduced a constraint to obtain the angle-stimulus voltage characteristic which is not redundant. We controlled normal subject's wrist angles (angle of radial/ulnar flexion and palmar/dorsi flexion) with stimulus patterns created by this method. First, we measured stimulus voltage-angle characteristic of the controlled object, and obtained a forward model by making ANN learn the measured characteristic. Since the stimulus voltage-angle characteristic was many-to-one relationship, we decided one-to-one stimulus voltage-angle characteristic by using constraint. As the constraint, we minimized sum of normalized stimulus voltages to the muscles. It was expected that the muscle fatigue was suppressed by this constraint. Next, we made ANN learn the inverse model of one-to-one stimulus voltage-angle characteristic by the direct inverse modeling. Then we created stimulus patterns for four trajectories with the obtained inverse model (angle-stimulus voltage characteristic). The created stimulus patterns were applied to the muscles for wrist movements, and were confirmed to restore the desired movements. The results indicate the fundamental validity of the method.

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© 日本生体医工学会
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