Automatically Determining Lumbar Load during Physically Demanding Work: A Validation Study.

Charlotte Christina Roossien,Mitchel Willem Pieter Van Der Waard,Michiel Felix Reneman,Gijsbertus Jacob Verkerke,Christian Theodoor Maria Baten

doi:10.3390/s21072476

Abstract

A sensor-based system using inertial magnetic measurement units and surface electromyography is suitable for objectively and automatically monitoring the lumbar load during physically demanding work. The validity and usability of this system in the uncontrolled real-life working environment of physically active workers are still unknown. The objective of this study was to test the discriminant validity of an artificial neural network-based method for load assessment during actual work. Nine physically active workers performed work-related tasks while wearing the sensor system. The main measure representing lumbar load was the net moment around the L5/S1 intervertebral body, estimated using a method that was based on artificial neural network and perceived workload. The mean differences (MDs) were tested using a paired t-test. During heavy tasks, the net moment (MD = 64.3 ± 13.5%, p = 0.028) and the perceived workload (MD = 5.1 ± 2.1, p < 0.001) observed were significantly higher than during the light tasks. The lumbar load had significantly higher variances during the dynamic tasks (MD = 33.5 ± 36.8%, p = 0.026) and the perceived workload was significantly higher (MD = 2.2 ± 1.5, p = 0.002) than during static tasks. It was concluded that the validity of this sensor-based system was supported because the differences in the lumbar load were consistent with the perceived intensity levels and character of the work tasks.

Highlights

Active workers sometimes can experience muscle and spinal overload while performing their physically demanding jobs [1]
Out of the 23 workers who participated in this study, the data of 12 subjects were not useable because of data-acquisition errors in either the inertial magnetic measurement units (IMMUs) or sEMG hardware during essential trials for artificial neural network (ANN) training
Lumbar loads could be distinguished with the ANN-based method in terms of intensity and variance levels

Summary

Introduction

Active workers sometimes can experience muscle and spinal overload while performing their physically demanding jobs [1] Such an overload is hypothesized to be due to a misbalance between the physical workload and the individual capacity of each worker [2]. This misbalance may cause health problems among these workers, such as musculoskeletal disorders like lower back pain [3,4,5,6,7]. There is a need for a device that can measure the individual work-related lumbar load exposure objectively while performing a physically demanding job [16,17] This lumbar load is represented by the net moment around the center of the intervertebral body at the spinal level L5/S1. All of these methods use 3D body segment kinematics data acquired using a marker-based motion analysis system, and load conditions were “known” through the direct measurement of the reaction forces exerted to the feet or hands or from detailed information on the loads handled

Objectives

Methods

Results

Discussion

Conclusion