An autoregressive modeling approach to analyzing wheelchair propulsion forces

Biomechanical signals collected during wheelchair propulsion are often anal yzed by computing averages and/or peak values over several strokes. Due to the complex nature of the signals, this type of analysis may not be specifi c to identifying factors that may predispose a wheelchair user to joint pai n/injury. Hence, a new technique is introduced thai uses a system identific ation approach, autoregressive (AR) modeling, to analyze wheelchair propuls ion force waveforms. In this application an AR method was used to create a model force waveform based on current and past values of digital pushrim fo rce data. The feasibility of the AR modeling method over point-wise methods to detect asymmetry among force waveforms was tested with a group of 20 wh eelchair users. Subjects propelled at a constant 0.9 m/s on a roller system during which 20 s of force data were collected from the SMART(Wheels), for ce and torque sensing pushrims. Both methods showed that the wheelchair use rs as a group propelled evenly, however, individual analysis using the AR m odel error estimates indicated that twenty-five percent demonstrated signif icant asymmetry in their force waveforms. If only point-wise means and vari ances of the applied bilateral forces were considered, most subjects would have appeared symmetrical. Thus, the AR modeling approach is more sensitive to detecting anomalies in propulsion technique. Published by Elsevier Scie nce Ltd on behalf of IPEM.