Balance control probably has the greatest impact on independence in activities of daily living (ADL), because it is a fundamental motor skill and prerequisite to the maintenance of a myriad of postures and mobile activities. A consequence of weakened balance control is fall.

We propose a Mobile Robotic Assistive Balance Trainer to administer standing and mobile balance control assistance and training at home or in community. The targeted group is people with high to medium fall risk. The proposed system consists of a follow-me robotic wheelchair with a robotic arm to provide balance assistance to the user only when the centre of mass of the body deviates beyond the predefined safety boundary, mimicking the helping hands of a parent when a toddler learns to walk. Special attention has been paid to the timing of balance assistance intervention in the design of the control algorithm.  
To promote motor re-learning, the system allows the execution of central nervous system controlled balance mechanism before the robot intervenes to provide balance assistance. By allowing more balance practices in standing and mobile ADL without the danger of fall, we hypothesize that this approach will promote true recovery in motor skills dependent on balance ability and eventually lead to lower risk of fall.
The project aims to perform proof-of-concept trials on healthy elderly subjects, and plans to target patient trials in the future.
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