Comparing adaptive algorithms to measure temporal gait parameters using lower body mounted inertial sensors.
The purpose of this research was to compare different adaptive algorithms in terms of their ability to determine temporal gait parameters based on data acquired from inertial measurement units (IMUs). Eight subjects performed 25 walking trials over a force plate under five different conditions; normal, fast, slow, simulated stiff ankle and simulated stiff knee walking. Data from IMUs worn on the shanks and on the feet were used to identify temporal gait features using three different adaptive algorithms (Green, Selles & Sabatini). Each method's ability to estimate temporal events was compared to the gold standard force plate method for stance time (Greene, r= .990, Selles, r= 0.865, Sabatini, r= 0.980) and double support time (Greene, r= .837, Selles, r= .583, Sabatini, r= .745). The Greene method of estimating gait events from inertial sensor data resulted in the most accurate stance and double support times.
CLARITY Centre for Sensor Web Technologies and the School of Public Health, Physiotherapy and Population Science, University College Dublin, Belfield, Dublin 4, Ireland. matt.patterson@ucd.ie
Matthew R
MR
Patterson
Brian
B
Caulfield
D000054:Acceleration; D056044:Actigraphy / Q000295:instrumentation*; D000328:Adult; D000465:Algorithms; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D007866:Leg / Q000502:physiology*; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014159:Transducers