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

Abstract

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 (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21–64 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−1 – 3.0 m s−1), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94% across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 ± 86.70 ms, while TO had a lead time of 63.96 ± 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99% accuracy, with a lead time of 59.41 ± 54.41 ms for IC and 90.79 ± 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.

Keywords

• Inertial measurement units
• real-world gait event detection
• unsupervised machine learning