Challenges and Opportunities for Statistical Monitoring of Gait Cycle Acceleration Observed from IMU Data for Fatigue Detection

Fatigue deteriorates temporary motor functions in individuals which often leads to performance drop of occupational workers, poor postural control of patients, and falls in elderly persons. Fatigue management and prevention of its adverse effects significantly depend on timely detection of fatigue. Advent of novel wearable sensor technologies enabled real time data collection and gait monitoring. Using IMU data, we propose a new method to detect fatigue with sole acceleration data from ankle. This method uses computationally-light Statistical Process Control (SPC) which does not require big data to set the algorithm and is also robust to noise. Instead of using simple gait parameters that represent intermittent gait data, we used the acceleration profiles of the whole gait cycles to detect fatigue. Workers were recruited to perform walking, loading, and un-loading tasks and their baseline and fatigued gait patterns were recorded. We explored cumulative and non-cumulative statistical process control methods for online monitoring of fatigue using the recorded data. Results from the non-cumulative method showed dominant changes in the gait pattern after participants were fatigued. We envision this method can be used to detect fatigue in real time in occupational workers, patients with ambulatory disorders, and elderly population.