A structural analysis of the rail unit of an indoor assistive mobility system.

BACKGROUND: Individuals with gait disturbances, such as that post-stroke, are discharged home to undergo outpatient rehabilitation. Rehabilitation in the community is not as effective as that in hospital, due to long travel times and short program duration. OBJECTIVE: This study analyzed rail unit structure, with the aim of assisting home indoor assistive mobility system (HIAMS) development, allowing patients to undergo gait-related rehabilitation training at home. METHODS: The HIAMS consists of a mobile rail running around the whole room, a turn-table for movement between rails, and a weight-supporting component. Structural analysis was performed using the Abaqus/CAE solution (Version 6.14, Dassault systems, Inc.) to verify device safety, according to the load applied to the rail and turn-table units. The load was applied vertically at 150 kg to reflect the weight of potential users. RESULTS: Structural analysis was performed on the weight-supporting components, which was consist of turn-table case, bearing components (center, left), connective bracket and rail rollers. The safety factors of each components were estimated as 1.31, 5.39 (bearing, center), 8.45 (bearing, left), 1.43 and 3.61 in sequence. CONCLUSION: We demonstrated a safety factor of ⩾ 1.3 for the key system units, suggesting this technology is safe for use in the home rehabilitation training of individuals with gait impairment post-ICU stay.

Recognition of sign language with an inertial sensor-based data glove.

Communication between people with normal hearing and hearing impairment is difficult. Recently, a variety of studies on sign language recognition have presented benefits from the development of information technology. This study presents a sign language recognition system using a data glove composed of 3-axis accelerometers, magnetometers, and gyroscopes. Each data obtained by the data glove is transmitted to a host application (implemented in a Window program on a PC). Next, the data is converted into angle data, and the angle information is displayed on the host application and verified by outputting three-dimensional models to the display. An experiment was performed with five subjects, three females and two males, and a performance set comprising numbers from one to nine was repeated five times. The system achieves a 99.26% movement detection rate, and approximately 98% recognition rate for each finger's state. The proposed system is expected to be a more portable and useful system when this algorithm is applied to smartphone applications for use in some situations such as in emergencies.