Online Ergonomic Evaluation in Realistic Manual Material Handling Task: Proof of Concept

Abstract

Work-related musculoskeletal disorders are globally one of the leading causes of work-related injuries. They significantly impact worker health and business costs. Work task ergonomic risk indices have been developed that use observational assessments to identify potential injuries, and allow safety managers to promptly intervene to mitigate the risks. However, these assessments are very subjective and difficult to perform in real time. This work provides a technique that can digitalize this process by developing an online algorithm to calculate the NIOSH index and provide additional data for ergonomic risk assessment. The method is based on the use of inertial sensors, which are easily found commercially and can be integrated into the industrial environment without any other sensing technology. This preliminary study demonstrates the effectiveness of the first version of the Online Lifting Index (On-LI) algorithm on a common industrial logistic task. The effectiveness is compared to the standard ergonomic assessment method. The results report an average error of 3.6% compared to the NIOSH parameters used to calculate the ergonomic risk and a relative error of the Lifting Index of 2.8% when compared to observational methods.

Keywords: NIOSH; biomechanical analysis; ergonomic risk assessment; event detection; wearable technology; work-related musculoskeletal disorders.

Figure 1

Human in the loop. This work aims at improving working conditions by providing online ergonomic evaluations of the NIOSH Lifting Index.

Figure 2

The On-LI algorithm uses NIOSH evaluation criteria to provide an online assessment of the ergonomic risks associated with the MMH task.

Figure 3

The biomechanical model of the On−LI system. Such a model reconstructs the subject posture. The dark red lines represent the body segments, the red and green squares represent the body joints of the right and left sides, respectively. The inertial reference is the world frame W, whose x, y and z axes are represented by the red, green and blue lines, respectively; on the pelvis there is the local frame P, whose x, y and z axes are represented by the red, green and blue lines, respectively.

Figure 4

The EDA estimations. In (a), the distance $d\left(t\right)$ between hands is shown in blue, the lifting time estimate ${\widehat{t}}_{l0}$ is the dashed line, and the release time estimate ${\widehat{t}}_{r0}$ is the dash−dot line. In (b), the z component of the average velocity vector ${\overline{\mathbf{v}}}_H^B\left(t\right)$, the estimate ${\widehat{t}}_{r1}-{ϵ}_{r1}$ is the dashed line, the estimate ${\widehat{t}}_{r1}$ is the dash−dot line, and the estimate ${\widehat{t}}_{r3}$ is the solid line. In (c), the y component of the hands relative velocity vector $\Delta {\mathbf{v}}_H^B\left(t\right)$, the estimate ${\widehat{t}}_{r1}-{ϵ}_{r1}$ is the dashed line, the estimate ${\widehat{t}}_{r1}$ is the dash-dot line, and the estimate ${\widehat{t}}_{r3}$ is the solid line.

Figure 5

Visual feedback output. The biomechanical model reconstructs the subject’s posture. The On−LI algorithm provides a LI warning and a posture warning.

Figure 6

(a) Side view and (b) top view of the experimental setup reporting the measurements needed to calculate the NIOSH.

Figure 7

Snapshots from the video footage. In (a), the subject picks up the load in position LL. As shown by the red arrow in the circle, the sensors in the gloves are above the box edges, yellow dashed line. In (b), the subject releases the load over the previous box. The subjects modified their grasp mid-way through the motion and now the sensors are below the box edges.

Figure 8

Snapshots from the video footage. In (a), the subject picks up the load in position FH while standing far from the pallet. As shown by the red arrow in the circle, the sensors on the feet are visible in the frame. In (b), the subject picks up the load in position RL and steps closer to the pallet. In this case, the sensors on the feet are hidden by the pallet.