The accuracy of a 2D video-based lifting monitor

A systematic literature review of computer vision-based biomechanical models for physical workload estimation

Ergonomic risks, driven by strenuous physical demands in complex work settings, are prevalent across industries. Addressing these challenges through detailed assessment and effective interventions enhances safety and employee well-being. Proper and timely measurement of physical workloads is the initial step towards holistic ergonomic control. This study comprehensively explores existing computer vision-based biomechanical analysis methods for workload assessment, assessing their performance against traditional techniques, and categorising them for easier use. Recent strides in artificial intelligence have revolutionised workload assessment, especially in realistic work settings where conventional methods fall short. However, understanding the accuracy, characteristics, and practicality of computer vision-based methods versus traditional approaches remains limited. To bridge this knowledge gap, a literature review along with a meta-analysis was completed in this study to illuminate model accuracy, advantages, and challenges, offering valuable insights for refined technology implementation in diverse work environments.

Estimating Trunk Angle Kinematics During Lifting Using a Computationally Efficient Computer Vision Method

OBJECTIVE

A computer vision method was developed for estimating the trunk flexion angle, angular speed, and angular acceleration by extracting simple features from the moving image during lifting.

BACKGROUND

Trunk kinematics is an important risk factor for lower back pain, but is often difficult to measure by practitioners for lifting risk assessments.

METHODS

Mannequins representing a wide range of hand locations for different lifting postures were systematically generated using the University of Michigan 3DSSPP software. A bounding box was drawn tightly around each mannequin and regression models estimated trunk angles. The estimates were validated against human posture data for 216 lifts collected using a laboratory-grade motion capture system and synchronized video recordings. Trunk kinematics, based on bounding box dimensions drawn around the subjects in the video recordings of the lifts, were modeled for consecutive video frames.

RESULTS

The mean absolute difference between predicted and motion capture measured trunk angles was 14.7°, and there was a significant linear relationship between predicted and measured trunk angles (R² = .80, p < .001). The training error for the kinematics model was 2.3°.

CONCLUSION

Using simple computer vision-extracted features, the bounding box method indirectly estimated trunk angle and associated kinematics, albeit with limited precision.

APPLICATION

This computer vision method may be implemented on handheld devices such as smartphones to facilitate automatic lifting risk assessments in the workplace.

Load Asymmetry Angle Estimation Using Multiple view Videos

A robust computer vision-based approach is developed to estimate the load asymmetry angle defined in the revised NIOSH lifting equation (RNLE). The angle of asymmetry enables the computation of a recommended weight limit for repetitive lifting operations in a workplace to prevent lower back injuries. An open-source package OpenPose is applied to estimate the 2D locations of skeletal joints of the worker from two synchronous videos. Combining these joint location estimates, a computer vision correspondence and depth estimation method is developed to estimate the 3D coordinates of skeletal joints during lifting. The angle of asymmetry is then deduced from a subset of these 3D positions. Error analysis reveals unreliable angle estimates due to occlusions of upper limbs. A robust angle estimation method that mitigates this challenge is developed. We propose a method to flag unreliable angle estimates based on the average confidence level of 2D joint estimates provided by OpenPose. An optimal threshold is derived that balances the percentage variance reduction of the estimation error and the percentage of angle estimates flagged. Tested with 360 lifting instances in a NIOSH-provided dataset, the standard deviation of angle estimation error is reduced from 10.13° to 4.99°. To realize this error variance reduction, 34% of estimated angles are flagged and require further validation.

A computer vision approach for classifying isometric grip force exertion levels

Exposure to high and/or repetitive force exertions can lead to musculoskeletal injuries. However, measuring worker force exertion levels is challenging, and existing techniques can be intrusive, interfere with human-machine interface, and/or limited by subjectivity. In this work, computer vision techniques are developed to detect isometric grip exertions using facial videos and wearable photoplethysmogram. Eighteen participants (19-24 years) performed isometric grip exertions at varying levels of maximum voluntary contraction. Novel features that predict forces were identified and extracted from video and photoplethysmogram data. Two experiments with two (High/Low) and three (0%MVC/50%MVC/100%MVC) labels were performed to classify exertions. The Deep Neural Network classifier performed the best with 96% and 87% accuracy for two- and three-level classifications, respectively. This approach was robust to leave subjects out during cross-validation (86% accuracy when 3-subjects were left out) and robust to noise (i.e. 89% accuracy for correctly classifying talking activities as low force exertions). Practitioner summary: Forceful exertions are contributing factors to musculoskeletal injuries, yet it remains difficult to measure in work environments. This paper presents an approach to estimate force exertion levels, which is less distracting to workers, easier to implement by practitioners, and could potentially be used in a wide variety of workplaces. Abbreviations: MSD: musculoskeletal disorders; ACGIH: American Conference of Governmental Industrial Hygienists; HAL: hand activity level; MVC: maximum voluntary contraction; PPG: photoplethysmogram; DNN: deep neural networks; LOSO: leave-one-subject-out; ROC: receiver operating characteristic; AUC: area under curve.