Occupational Safety and Health with Technological Developments in Livestock Farms: A Literature Review.

In recent decades, there have been considerable technological developments in the agriculture sector to automate manual processes for many factors, including increased production demand and in response to labor shortages/costs. We conducted a review of the literature to summarize the key advances from installing emerging technology and studies on robotics and automation to improve agricultural practices. The main objective of this review was to survey the scientific literature to identify the uses of these new technologies in agricultural practices focusing on new or reduced occupational safety risks affecting agriculture workers. We screened 3248 articles with the following criteria: (1) relevance of the title and abstract with occupational safety and health; (2) agriculture technologies/applications that were available in the United States; (3) written in English; and (4) published 2015-2020. We found 624 articles on crops and harvesting and 80 articles on livestock farming related to robotics and automated systems. Within livestock farming, most (78%) articles identified were related to dairy farms, and 56% of the articles indicated these farms were using robotics routinely. However, our review revealed gaps in how the technology has been evaluated to show the benefits or potential hazards to the safety and well-being of livestock owners/operators and workers.

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.

Case Studies of Robots and Automation as Health/Safety Interventions in Small Manufacturing Enterprises.

This paper reviews the experiences of 63 case studies of small businesses (< 250 employees) with manufacturing automation equipment acquired through a health/safety intervention grant program. The review scope included equipment technologies classified as industrial robots (n = 17), computer numerical control (CNC) machining (n = 29), or other programmable automation systems (n = 17). Descriptions of workers' compensation (WC) claim injuries and identified risk factors that motivated acquisition of the equipment were extracted from grant applications. Other aspects of the employer experiences, including qualitative and quantitative assessment of effects on risk factors for musculoskeletal disorders (MSD), effects on productivity, and employee acceptance of the intervention were summarized from the case study reports. Case studies associated with a combination of large reduction in risk factors, lower cost per affected employee, and reported increases in productivity were: CNC stone cutting system, CNC/vertical machining system, automated system for bottling, CNC/routing system for plastics products manufacturing, and a CNC/Cutting system for vinyl/carpet. Six case studies of industrial robots reported quantitative reductions in MSD risk factors in these diverse manufacturing industries: Snack Foods; Photographic Film, Paper, Plate, and Chemical; Machine Shops; Leather Good and Allied Products; Plastic Products; and Iron and Steel Forging. This review of health/safety intervention case studies indicates that advanced (programmable) manufacturing automation, including industrial robots, reduced workplace musculoskeletal risk factors and improved process productivity in most cases.

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.