A performance comparison of accelerometry-based step detection algorithms on a large, non-laboratory sample of healthy and mobility-impaired persons

Accelerometers are frequently used for activity assessment and as reference devices for counting steps. Their performance on healthy subjects´ data is good, but there are doubts as to their applicability on elderly and mobility-impaired subjects. Furthermore, only few step detection algorithms have been published so far, and their performance has not been evaluated on a large, non-laboratory sample. The aim of this paper is to compare the performance of four freely accessible accelerometry-based step detection algorithms in a non-laboratory setting. Two samples of healthy persons (n=140) and mobility-impaired, geriatric in-patients (n=10) wore a single triaxial accelerometer on a waist-belt during unconstrained walking. The relative error rate of the four algorithms on the two samples was compared with reference video recordings. All four algorithms show a fairly poor performance on healthy subjects´ (8.4–30.8% relative error rate) and especially geriatric patients´ data (28.1–62.1%). Among the tested ones, a simple autocorrelation algorithm works best on both data sets together. More complex algorithms might work better, and more research is needed to evaluate the accuracy of step detection methods on mobility-impaired subjects.