Statistical Prediction of Hand Force Exertion Levels in a Simulated Push Task using Posture Kinematics

Abstract

This study explored the use of body posture kinematics derived from wearable inertial sensors to estimate force exertion levels in a two-handed isometric pushing and pulling task. A prediction model was developed grounded on the hypothesis that body postures predictably change depending on the magnitude of the exerted force. Five body postural angles, viz., torso flexion, pelvis flexion, lumbar flexion, hip flexion, and upper arm inclination, collected from 15 male participants performing simulated isometric pushing and pulling tasks in the laboratory were used as predictor variables in a statistical model to estimate handle height (shoulder vs. hip) and force intensity level (low vs. high). Individual anthropometric and strength measurements were also included as predictors. A Random Forest algorithm implemented in a two-stage hierarchy correctly classified 77.2% of the handle height and force intensity levels. Results represent early work in coupling unobtrusive, wearable instrumentation with statistical learning techniques to model occupational activities and exposures to biomechanical risk factors in situ.

Figures

Figure 1

Schematic representation of the experiment apparatus and instrumentation showing anatomical reference locations for the inertial sensors attachment.

Figure 2

Structural differences in Model-1: Multiclass prediction with four classes as the response variable (left-panel) and Model-2: Hierarchical structure (right-panel) where handle height is classified at the first stage and then force intensity. Prediction accuracy at each stage is noted under each sub-model (denoted as an oval), and the overall prediction accuracy at the bottom of the panel.

Figure 3

Graphs showing the top-five important variables in each stage of the final hierarchical Model-2 (A: handle height at hip vs. shoulder, B: force intensity at hip handle height, C: force intensity at shoulder handle height) by plotting the mean decrease in Gini Index, a measure of relative importance (%) when the corresponding predictor variable is dropped from the model. A greater relative importance suggests greater importance of the predictor variable.