Filter frequency selection for manual wheelchair biomechanics

Rory A Cooper, Carmen P DiGiovine, Michael L Boninger, Sean D Shimada, Alicia M Koontz, Mark A Baldwin
Journal of rehabilitation research and development  
Wheelchair locomotion is an important form of mobility for many individuals with spinal cord injury. However, manual wheelchair propulsion can lead to upper-limb pain and can be very inefficient. This has led investigators to apply biomechanics to the study of wheelchair use. The objectives of this study were (1) to determine the frequency content of the motion of both hands during two speeds of wheelchair propulsion, (2) to obtain the filter frequencies necessary to remove noise from
more » ... oise from wheelchair motion data, and (3) to provide signal-to-noise ratio data for wheelchair kinematics. The participants in this study were a random sample of manual wheelchair users with paraplegia caused by spinal cord injury. Subjects propelled their personal wheelchairs on a computer-controlled dynamometer at speeds of 0.9 m/s and 1.8 m/s. Motion data were collected at 60 Hz with the use of a commercial infrared marker-based system. The main outcome measures were arm motions and noise frequency spectra, filter cutoff frequencies, and signal-to-noise ratio. Our results indicate that there is no useful signal power above 6 Hz during manual wheelchair propulsion at the speeds that we analyzed. In many cases, there was no useful signal power above 4 Hz. This would indicate that the frequency content of manual wheelchair propulsion is similar to that of human gait. The mean signal-to-noise ratio varied from a high of 91 dB to a low of 21.8 dB. The signal-to-noise ratio was greatest in the x direction (along the line of progression) and lowest in the z direction (medial-lateral). Manual wheelchair propulsion kinematic data should be low-pass filtered at approximately 6 Hz for speeds at or below 1.8 m/s. The data presented in the archival literature appear to have been filtered at an appropriate frequency.
pmid:12173753 fatcat:a3ykonnr7zh73oqdnrbul7dlxi