Mobility Pattern Classification for a Bed Activity Monitoring System

Bed Activity Monitoring System (BAMS) monitors and assess the mobility of people on a bed. This is a useful and critical application for patients with mobility issues after stroke or traumatic brain injury. The system is based on processing of data collected from a piezoelectric pressure sensor for discriminating mobility patterns. There are four different types of motion that were simulated by non-patient volunteers and data was collected. In this paper, two methods were used to extract feature parameters (autoregressive and cepstral coefficients) from the acquired data. Two classification algorithms, Euclidean Distance Measure (EDM) and Weighted Distance Measure (WDM) were used to classify and discriminate the mobility patterns of normal person (healthy subject) from people with mobility issues (patient subjects). Experimental result shows that the recognition rate using cepstral parameters was more effective compare to autoregressive parameters.