Development of a Support System for the Physically Handicapped Using EMG-controlled Interface

本研究的目的是为残障人士开发一种由接口装置和人工辅助机械手组成的新型支撑系统。(1)提出了一种新的神经网络--递归对数线性化高斯混合网络(R-LLGMN)，以实现对来自残疾人的各种肌电信号的自适应学习能力。R-LLGMN是在高斯混合模型和隐马尔可夫模型的基础上发展起来的，实验表明，R-LLGMN比传统的神经网络具有更强的模式识别能力。(2)利用R-LLGMN，提出了一种从操作者测量的肌电信号中识别前臂和手的运动的算法，实现了基于肌电的界面。(3)开发了一种通过测量操作者的肌电信号进行操作的新型机器人控制系统，并将其集成到人类辅助机械手中。此外，为了在日常活动中使用肌电控制接口，还对每个任务的特定知识进行了建模，并将其整合到机械手的控制系统中。(4)作为所开发的肌电控制接口的应用实例，开发了一种新型的助餐机器人系统。从肢体残障人士的实验结果来看，这是一种实际应用的可能性。

The purpose of this research is to develop a new support system consisting of an interface device and a human assisting manipulator for the physically handicapped. The obtained results throughout this research can be summarized as follows:(1) A new neural network called Recurrent Log-Linearized Gaussian Mixture Network (R-LLGMN) was proposed to realize an adaptive learning ability to a variety of EMG signals measured from the handicapped. The R-LLGMN was developed on the basis of the Gaussian mixture model and the hidden Markov model, and it was shown from experiments that the R-LLGMN has higher pattern recognition abilities than conventional neural networks.(2) Using R-LLGMN, an algorithm to recognize motions of forearm and hand from measured EMG signals of operators was developed to realize the EMG-based interface.(3) A new robot control system manipulated through EMG signals measured from the operator was then developed, and incorporated into a human assisting manipulator. Also, to use it in daily activities, specific knowledge concerning about each task is modeled and incorporated into the control system of the manipulator.(4) As an example of application of the developed EMG-controlled interface, a new robot system for meal assistance is developed. From experimental results by the physically handicapped, a possibility to come into practical use was shown.