Annotation of foot-contact and foot-off events is the initial step in post-processing for most quantitative gait analysis workflows. Through analysis of 9092 gait cycle measurements we build a predictive model using Long Short-Term Memory (LSTM) artificia
Annotation of foot-contact and foot-off events is the initial step in post-processing for most quantitative gait analysis workflows. Through analysis of 9092 gait cycle measurements we build a predictive model using Long Short-Term Memory (LSTM) artificial neural networks. The best-performing model identifies foot-contact and foot-off events with an average error of 10 and 13 milliseconds respectively, outperforming popular heuristic-based approaches.