Abnormal Behavior Detection Based on Activity Level Using Fuzzy Inference System for Wheelchair Users

With the ever increasing average age worldwide, wheelchairs are becoming a fundamental aiding tool to facilitate mobility among elder and people with disabilities. Lacking of continuous nursing care service, wheelchair users might encounter dangerous conditions caused by long time sitting activities and suffer physical discomfort, musculoskeletal disorders, pressure ulcers, cardiovascular diseases, etc. Because wheelchair users need proper amount of exercises, distinguishing abnormal physical behavior from training activities is undoubtedly necessary. In this paper, we propose an abnormal behavior detection method based on activity level assessment for wheelchair users. Using fuzzy inference system, we construct the fuzzy sets for accelerometer, gyroscope and center-of-pressure in sitting condition based on data collected with a multisensor smart cushion. In addition, combined with posture recognition, we construct the fuzzy sets of activity levels and posture transition percentage to ultimately detect abnormal activity states, which can represent safety risk. Experiment results demonstrate that the proposed algorithm can accurately recognize activity levels and detect abnormal states.