This paper demonstrates a novel approach for motion classification and analysis using pressure sensors worn by a person. The pressure signal is analysed to search for features corresponding to the motion states, and matched against typical human walking pattern. A prototype system is developed which provides motion classification results in real-time. The motion classification results consists of the number of steps taken by the participant together with the corresponding motion state. The system distinguishes the states associated with a person travelling on a lift, walking on stairs, walking on a flat ground and rest. Data from several participants are collected in a measurement campaign using pressure sensors only, which shows a precision rate of over 90% and a recall rate between 89% and 96%, for the states associated with the movement of participant.
['Birendra Ghimire', 'Christian Nickel', 'Jochen Seitz']
Pedestrian motion state classification using pressure sensors