In this study, we propose a system that detects falls and estimates walking conditions for elderly people living alone. This system uses floor vibration measured by microphone sensors installed on the floor of an elderly person's home, and it detects falls and walking from the frequency characteristic of the floor vibration. Furthermore, it estimates the walking condition using a method of estimating the sound sources with multiple microphone sensors. To demonstrate the usefulness of the proposed system, an experiment of fall / walk detection with one healthy person and a walking condition estimation experiment were conducted in a model room in a facility for the elderly in Fukui Prefecture. As a result, all falls and walks under arbitrary conditions were able to be detected, and the estimation of the walking state was able to be partially achieved.

Numerous robotic devices have been developed to assist hand rehabilitation; however, a majority of these are difficult for stroke survivors to wear. The purpose of this study was to develop an assistive device for treating flexion contracture, which supports the extension of each finger and may easily be worn on a paralyzed hand. To facilitate ease of use, we suggested a new wearing method for this wire-driven device with an elastic skeleton, allowing users to extend the device from the back of the hand onto the fingertip. The functional capacity of this device was measured through fingertip contact force and estimations of supporting torque. Results showed the device provides sufficient torque for finger extension with controlled wire tension. Moreover, experimental results confirmed that the novel design significantly decreased the time it took users to don the device compared to other designs.

This work reports research and development of a lightweight neuroprosthesis, which can control the impaired motion by using voluntary biological signal. The total weight of the upper limb neuroprosthesis is 900 g, which is 40% lesser than the commercially available ones. For a trans-humeral amputee who had targeted muscle reinnervation (TMR) surgery, pattern classification of five motions was possible by using surface electromyogram (s-EMG) extracted from four dry electrodes.

A myoelectric prosthesis, which can be used as a replacement for a person's upper limb, is able to control many hand motions optionally via surface electromyography (sEMG). Although many researchers have developed myoelectric prostheses that realize various motions, evaluation of their practicality has only been carried out on small numbers of patients. To solve this problem, a myoelectric prosthesis simulator that is able to move like a real prosthesis is needed. In this study, such a simulator that enables persons to grasp virtual objects with virtual prostheses was developed using Augmented Reality (AR). The results of comparative experiments conducted to evaluate the developed system using a pick and place task show that although it is not at present as effective as real prostheses, a virtual prosthesis is able to grasp virtual objects in the proposed simulator.

The purpose of this study is to investigate how sensory feedback affects the cognitive strain of grasping using a prosthesis and to develop an efficient method for decreasing the cognitive strain of grasping. We divided a grasping action into two phases, an “approaching phase” and a “grasping phase”, and assumed that the tactile feedback affect for the cognitive strain induced by grasping phase and the deep sensory feedback affect for cognitive strain induced by approaching phase. Using dual-task method, we compared the effect of sensory feedback method using strength-changed simulator or using spatially changed stimulators. As a result, we concluded that the sensory feedback can decrease the cognitive strain of grasping action, and the sensory feedback from one strength-changed stimulator is better than the one from spatially changed stimulators to decrease a cognitive strain caused by grasping action.