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How to best support sit to stand transfers of geriatric patients: Motion optimization under external forces for the design of physical assistive devices
Institution:1. Department of Physical Medicine and Rehabilitation, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan;2. TOTO Ltd., 2-8-1 Honson, Chigasaki 253-8577, Japan;3. Laison Center for Innovative Dentistry, Tohoku University Graduate School of Dentistry, 4-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan;4. Graduate School of Biomedical Engineering, Tohoku University, 2-1, Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan;1. Electrical Engineering and Computer Sciences, University of California, Berkeley, United States;2. Ecology and Evolutionary Biology, Museum of Zoology, University of Michigan, United States;3. Mechanical Engineering, University of Michigan, United States
Abstract:Sit to stand (STS) transfers form a challenging type of motion, in particular for geriatric patients. Physical assistive devices that are built to enhance the mobility of this class of patients therefore must especially be able to support STS transfers. This paper presents a method to predict geriatric STS movements and compute the best possible actions by external devices to support these movements. We treat three types of active devices that act on different parts of the patient’s body and provide different levels of support. Our approach is based on the solution of optimal control problems for a whole-body multi-phase model of humans standing up from sitting to upright position. Computations are performed for percentiles 20/50/80 of male and female geriatric population. The actions of the external devices are simulated by external forces at moving contact points, which are all determined by the optimization, simultaneously with the expected movements and joint torques of the patients. The results serve as inputs for design optimizations of the different device types.
Keywords:STS transfer  Geriatric patients  Multi-body human modeling  Optimal control  Movement prediction  Physical assistive devices
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