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The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (&#x2264; 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21&#x2013;64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s<sup>&#x2212;1</sup> &#x2013; 3.0 m s<sup>&#x2212;1</sup>), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94&#x0025; across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 &#x00B1; 86.70 ms, while TO had a lead time of 63.96 &#x00B1; 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99&#x0025; accuracy, with a lead time of 59.41 &#x00B1; 54.41 ms for IC and 90.79 &#x00B1; 35.51 ms for TO. These approaches are consistent in their identification of gait events and have the potential to be utilized for classification and prediction of locomotion mode.

Feature Identification With a Heuristic Algorithm and an Unsupervised Machine Learning Algorithm for Prior Knowledge of Gait Events