A wheelchair is a mandatory device for people with mobility problems. Typical wheelchairs maneuvered by muscular forces are physically demanding in various conditions, e.g., uphill or step. For this purpose, electric wheelchairs have been developed and commercialized for enhancement of the maneuverability and safety of people with muscular weakness. Such systems involve complete human-machine interaction, and thus it is necessary to accurately observe the physical state and the environmental condition in real-time. For this reasoning, a sensor-fusion method and a decision making algorithm for the complete observation of both the physical states of the wheelchair and the intention of a user are introduced in this paper. The physical states to be observed include the wheel speed, the pitch angle and the external forces, which are the most important physical quantities for the control of a power-assisted wheelchair (PAW) system in daily-life conditions. The estimated physical quantities are further processed in real-time in order to detect the environmental conditions and the intentional motions by the human operator. The proposed methods are all verified by experiments in this paper.
['Sehoon Oh', 'Kyoungchul Kong']
Operation condition recognition for the control of power-assisted wheelchair