Power assistance algorithm of an E-Trike for older adults based on inverse dynamics

Abstract

Maintaining a regular physical activity routine can promote healthy and successful aging. The use of power assist electric bicycles (e-bikes) has been shown to be a good exercise option since it allows older adults to adjust the degree of physical activity while maintaining higher levels of enjoyment compared to riding conventional bicycles. However, their use may present a fall risk to older adults due to their inherent instability and it is difficult to determine suitable power assistance from motor. We seek to solve this problem by using an electric trike (e-trike) instead of an e-bike and propose two novel power assistance algorithms to determine the assistive torque for our developed e-trike. Inverse dynamics calculations using pedal force, pedal angle, and crank angle are performed for effective assistance. The first algorithm is aimed at older adults with weak lower body muscle strength by analyzing the rear wheel torque, and the second is a joint assistive algorithm that reduces the physical effort for a specific leg joint (hip, knee, or ankle), for those who have joint problems, based on inverse dynamics. Finally, in a controlled environment, an experimental validation successfully verified the performance of the power assistance algorithms. Muscle activity in each test method is also presented using electromyography (EMG) to demonstrate the assistive performance of the muscles.