An MMG-based human-assisting manipulator using acceleration sensors

This paper proposes a control method for a human-assisting manipulator using acceleration sensors. The technique involves an arm control part (ACP) and a hand-and-wrist control part (HWCP); the ACP controls the manipulator's shoulder and elbow joints using acceleration signals, while the HWCP controls the corresponding joints using mechanomyogram (MMG) signals measured from the human operator. A distinctive feature of the proposed method is its estimation of information on force and motion from measured acceleration signals using MMG processing and a probabilistic neural network. Experiments demonstrated that the MMG patterns seen during hand and wrist motion can be classified sufficiently (average rate: 94.3 %), and that a prosthetic manipulator can be controlled using the acceleration signals measured. Such manipulators are expected to prove useful as assistive devices for people with physical disabilities.