In 2015 a powered wheelchair system that can detect and avoid objects was enhanced with a Raspberry Pi to extend the number of inputs the system could use to infer information about its environment. Wheelchair users are not always able to use simple controls such as joystick to drive, they may have to control the wheelchair using tongue, head or feet. This can make it much more difficult to learn how to drive and therefore is important to know how a user is progressing. The research described in this paper employs machine learning to uses wireless access points and predict its location, and with prolonged use will learn routes between rooms and buildings. The system uses location and accelerometer data to present information about driving patterns and collisions behaviour, to inform the wheelchair user and carer of issues while driving the wheelchair.
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