Statistical prediction of load carriage mode and magnitude from inertial sensor derived gait kinematics

Abstract

Load carriage induces systematic alterations in gait patterns and pelvic-thoracic coordination. Leveraging this information, the objective of this study was to develop and assess a statistical prediction algorithm that uses body-worn inertial sensor data for classifying load carrying modes and load levels. Nine men participated in an experiment carrying a hand load in four modes: one-handed right and left carry, and two-handed side and anterior carry, each at 50% and 75% of the participant's maximum acceptable weight of carry, and a no-load reference condition. Twelve gait parameters calculated from inertial sensor data for each gait cycle, including gait phase durations, torso and pelvis postural sway, and thoracic-pelvic coordination were used as predictors in a two-stage hierarchical random forest classification model with Bayesian inference. The model correctly classified 96.9% of the carrying modes and 93.1% of the load levels. Coronal thoracic-pelvic coordination and pelvis postural sway were the most relevant predictors although their relative importance differed between carrying mode and load level prediction models. This study presents an algorithmic framework for combining inertial sensing with statistical prediction with potential use for quantifying physical exposures from load carriage.

Keywords: Gait kinematics; Inertial sensors; Load carriage; Load classification.

Conflict of interest statement

Disclosure statement

The authors declare no conflict of interest.

Copyright © 2018 Elsevier Ltd. All rights reserved.

Figures

Figure 1:

Images showing the four carrying modes performed in this study: (a) one-handed right hand carry (1H-R), (b) one-handed left hand carry (1H-L), (c) two-handed side carry (2H-Side), (d) two-handed anterior carry (2H-Anterior) along with the location of four inertial sensors attached on the body at T6, S1, and shank (R, L).

Figure 2:

Overview of the carrying mode and load level classification algorithm developed in the study. The right panel shows example classification results for three consecutive gait cycles at a two-handed anterior carry with 50% MAWC load condition.

Figure 3:

Example results from the random forest classification to predict carrying mode for fifteen consecutive gait cycles from a two-handed anterior carry walk trial without (top-panel) and with (bottom-panel) Bayesian inference applied. In each gait cycle, the mode with the highest predicted probability is labeled as the classification result for that gait cycle. In this example, without Bayesian inference applied (top-panel) 3 of the 15 gait cycles were misclassified as either 1H-L (gait cycle #1) or no-load (gait cycle #9 and #10). In the bottom graph, Bayesian inference was applied to the same data and updated the posterior probability of the gait cycle based on prior gait cycles cumulatively. The probability of the data predicted as the correct class (i.e., two-handed anterior carry in this case) exceeded 0.9 after four gait cycles and converged to 1.0 in subsequent cycles.

Figure 4:

Posterior probabilities of the target carrying mode in each walk trial depicted by gait cycles. Misclassified classes are marked as red dotted lines.

Figure 5:

Relative importance (%) of the predictor variables computed as the mean decrease in the Gini index relative to the maximum (100%) for each of the five classification models, namely, for carrying mode (panel A) and for load level (Panels B-1 to B-4).