A deep learning approach for lower back-pain risk prediction during manual lifting.

Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers' compensation claims and missed time for the employer. However, recognizing lifting risk provides a challenge due to typically small datasets and subtle underlying features in accelerometer and gyroscope data. A novel method to classify a lifting dataset using a 2D convolutional neural network (CNN) and no manual feature extraction is proposed in this paper; the dataset consisted of 10 subjects lifting at various relative distances from the body with 720 total trials. The proposed deep CNN displayed greater accuracy (90.6%) compared to an alternative CNN and multilayer perceptron (MLP). A deep CNN could be adapted to classify many other activities that traditionally pose greater challenges in industrial environments due to their size and complexity.

Development of Human Posture Simulation Method for Assessing Posture Angles and Spinal Loads.

Video-based posture analysis employing a biomechanical model is gaining a growing popularity for ergonomic assessments. A human posture simulation method of estimating multiple body postural angles and spinal loads from a video record was developed to expedite ergonomic assessments. The method was evaluated by a repeated measures study design with three trunk flexion levels, two lift asymmetry levels, three viewing angles and three trial repetitions as experimental factors. The study comprised two phases evaluating the accuracy of simulating self and other people's lifting posture via a proxy of a computer-generated humanoid. The mean values of the accuracy of simulating self and humanoid postures were 12° and 15°, respectively. The repeatability of the method for the same lifting condition was excellent (~2°). The least simulation error was associated with side viewing angle. The estimated back compressive force and moment, calculated by a three dimensional biomechanical model, exhibited a range of 5% underestimation. The posture simulation method enables researchers to simultaneously quantify body posture angles and spinal loading variables with accuracy and precision comparable to on-screen posture matching methods.

Efficacy of the revised NIOSH lifting equation to predict risk of low-back pain associated with manual lifting: a one-year prospective study.

OBJECTIVE: The objective was to evaluate the efficacy of the Revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE) to predict risk of low-back pain (LBP). BACKGROUND: In 1993, NIOSH published the RNLE as a risk assessment method for LBP associated with manual lifting. To date, there has been little research evaluating the RNLE as a predictor of the risk of LBP using a prospective design. METHODS: A total of 78 healthy industrial workers' baseline LBP risk exposures and self-reported LBP at one-year follow-up were investigated. The composite lifting index (CLI), the outcome measure of the RNLE for analyzing multiple lifting tasks, was used as the main risk predictor. The risk was estimated using the mean and maximum CLI variables at baseline and self-reported LBP during the follow-up. Odds ratios (ORs) were calculated using a logistic regression analysis adjusted for covariates that included personal factors, physical activities outside of work, job factors, and work-related psychosocial characteristics. RESULTS: The one-year self-reported LBP incidence was 32.1%. After controlling for history of prior LBP, supervisory support, and job strain, the categorical mean and maximum CLI above 2 had a significant relationship (OR = 5.1-6.5) with self-reported LBP, as compared with the CLI below or equal to I. The correlation between the continuous CLI variables and LBP was unclear. CONCLUSIONS: The CLI > 2 threshold may be useful for predicting self-reported LBP. Research with a larger sample size is needed to clarify the exposure-response relationship between the CLI and LBP.

Ergonomic Assessment of Floor-based and Overhead Lifts.

Manual full-body vertical lifts of patients have high risk for developing musculoskeletal disorders. Two primary types of battery-powered lift assist devices are available for these tasks: floor-based and overhead-mounted devices. Studies suggest that the operation of floor-based devices may require excessive pushing and pulling forces and that overhead-mounted devices are safer and require lower operating forces. This study evaluated required operating hand forces and resulting biomechanical spinal loading for overhead-mounted lifts versus floor-based lifts across various floor surfaces and patient weight conditions. We did not examine differences in how operators performed the tasks, but rather focused on differences in required operating forces and estimated biomechanical loads across various exposure conditions for a typical operator. Findings show that the floor-based lifts exceeded recommended exposure limits for pushing and pulling for many of the floor/weight conditions and that the overhead-mounted lifts did not. As expected, forces and spinal loads were greater for nonlinoleum floor surfaces compared with linoleum floors. Based on these findings, it is suggested that overhead-mounted devices be used whenever possible, particularly in instances where carpeted floors would be encountered.

Efficacy of the revised NIOSH lifting equation to predict risk of low back pain due to manual lifting: expanded cross-sectional analysis.

OBJECTIVE: To evaluate whether the Revised NIOSH Lifting Equation (RNLE) is a valid tool for assessing risk of low back pain (LBP) due to manual lifting by using combined data from two cross-sectional studies of 1-year prevalence. METHODS: Results from a symptom and occupational history questionnaire and RNLE analysis for 677 subjects employed in 125 manual lifting jobs at nine industrial sites were combined from two studies. RESULTS: The odds of LBP increased as the lifting index (LI) increased from 1.0 to 3.0. A statistically significant odds ratio (OR) was found for both the 1 < LI ≤ 2 (OR = 1.81) and the 2 < LI ≤ 3 categories (OR = 2.26). For jobs with an LI value greater than 3.0, however, the OR remained nonsignificant. The 2 < LI ≤ 3 group remained statistically significant after adjusting for age, gender, body mass index, and psychosocial factors. CONCLUSIONS: It is clear that as the LI increases, the risk of LBP increases. Longitudinal studies are needed.