Abstract
Trunk flexion represents a risk factor for the onset of low-back disorders, yet limited quantitative data exist regarding flexion exposures in actual working conditions. In this study, we evaluated the potential of using a single inertial measurement unit (IMU) to classify trunk flexion, in terms of amplitude, frequency, and duration, and assessed the influence of alternative time durations on exposure results. Twelve warehouse workers were monitored during two hours of an actual shift while wearing a single IMU on their low back. Trunk flexion data were reduced using exposure variation analysis integrated with recommended exposure thresholds. Workers spent 5.1% of their working time with trunk flexion of 30–60° and 2.3% with flexion of 60–90°. Depending on the level of acceptable error, relatively shorter monitoring periods (up to 50 min) might be sufficient to characterize trunk flexion exposures. Future work is needed, however, to determine if these results generalize to other postural exposures and tasks.
Highlights
Non-neutral trunk postures, in particular those involving flexion [1], represent a risk factor for the onset of low-back disorders [2,3,4,5]
To help overcome some of these concerns, we report here on a study that assessed the feasibility of classifying trunk flexion during actual manual material handling (MMH) tasks among warehouse workers using a miniaturized wearable inertial measurement unit (IMU)
The The proposed approach suitable forassess long‐term actualworking working main purpose of is thepotentially present study was to the feasibility of usingand a simplified setup conditions, since it is based on a sensor that is of limited size, is positioned, provides data conditions, since it is based on a sensor that is of limited size, is positioned, and provides data involving a single miniaturized wearable tothis characterize a worker’s exposure to trunk flexion
Summary
Non-neutral trunk postures, in particular those involving flexion [1], represent a risk factor for the onset of low-back disorders [2,3,4,5] These disorders represent a major health problem, causing absence from work, with consequent reduction in productivity [6], and in the most serious cases disability and impairment of the fundamental activities of daily living. A critical issue when planning prevention/mitigation strategies is obtaining an accurate evaluation of the biomechanical risk associated with each working task, which requires a detailed identification of task intensity, frequency, and duration [8] Such assessments have been typically performed by means of self-reports and observational methods. The former method is easy to use and inexpensive, but can be biased by subjective perceptions of physical work demands, while the latter can be inaccurate and substantially time-consuming, with required analysis durations up to 30 times the actual duration of a video segment [9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
