User Independent Estimations of Gait Events With Minimal Sensor Data.

Abstract

The purpose of this study was to provide an initial examination of the utility of the Beta Process - Auto Regressive - Hidden Markov Model (BP-AR-HMM) for the prior identification of gait events. A secondary objective was to determine whether the output of the model could be used for classification and prediction of locomotion states. In this study we utilized the output of the BP-AR-HMM to develop user-independent identification of gait events and gait classification from an idealized three-dimensional acceleration signal. The input acceleration data were collected from two walking (1.4 and 1.6 ms^-1) and two running (2.6 and 3.0 ms^-1) steady state speeds, and during two dynamic walk to run and run to walk transitions (1.8-2.4 and 2.4-1.8 ms^-1) on an instrumented force treadmill. The BP-AR-HMM identified 9 unique states. Of these, two states, 4 and 1, were utilized to estimate initial contact and toe off, respectively. The lead time from the first instance of state 4 to initial contact was 0.13 ± 0.02 s. Similarly, the first instance of state 1 occurred 0.14 ± 0.03 s before toe off. Two other states (3 and 7) were examined for possible utilization in a probabilistic model for the prediction of pending locomotion state transitions. The identification of gait events prior to their occurrence by the BP-AR-HMM appears to be an approach that can minimize the quantity of sensor data in an offline approach. Furthermore, there is evidence it could also be used as a basis to build a probabilistic model to estimate locomotion transitions.