Occupational falls: interventions for fall detection, prevention and safety promotion

Abstract

Falls and fall-related injuries are remarkably common in occupational settings. In 2017, 227,760 workers sustained fall-related injuries leading to a loss of working days and 887 workers sustained lethal falls. Causation of falls could be multifactorial with the contribution of extrinsic and intrinsic factors. The outcome of such falls could vary from simple contusions to fatal injuries. Occupational falls not only affect employees’ health and quality of life but also decrease workplace productivity due to loss of working days and worker performance insufficiency as a result of sustained injuries. Furthermore, health costs, worker compensations, and replacement workers add to the economic burden of the workplace. Thus, fall detection and prevention in the workplace is mandatory. There are distinct traditional and contemporary methods of fall prevention established in the human factors and ergonomics field with unique advantages and disadvantages. Selection of an appropriate method should be done vigilantly considering feasibility, availability of facilities, and affordability by the administration. This article summarises and expands on previous literature suggested for early detection and prevention of occupational falls. Concepts discussed in the paper will benefit human factors engineers, ergonomists, health and safety engineers, occupational therapists, and other health care professionals.

Relevance to human factors / ergonomics theory

Falls are unavoidable in the ergonomic settings but could be minimized. This article provides the currently available fall prevention and detection methods along with the strengths and weaknesses of each method. The article will provide an overview of such methods to the employers in order to select and implement the most applicable methods to their workplace.

Keywords:

• Workplace
• ergonomics
• fall prevention
• sensors

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Sachini N. K. Kodithuwakku Arachchige

Sachini N. K. Kodithuwakku Arachchige is a doctoral student in Exercise Science at Mississippi State University. She obtained her medical degree from Sri Lanka and the master’s degree in Exercise Physiology from Mississippi State University. Her research areas of interest are posture, balance gait and falls. She is mainly focused on the measurements to minimize lower extremity injuries in the ergonomic population.

Harish Chander

Harish Chander is an Associate Professor of Biomechanics and the Co-director of the Neuromechanics Laboratory at Mississippi State University. His research are primarily focused on human factors and ergonomics with special emphasis on postural stability, gait, slips, trips, falls and fall prevention. He works with multiple populations such as athletic, ergonomic, and clinical populations.

Adam C. Knight

Adam C. Knight is an Associate Professor of Biomechanics and the Co-director of the Neuromechanics Laboratory at Mississippi State University. His research areas are lateral ankle sprain mechanics, sport biomechanics and occupational Biomechanics. Chronic ankle stability in athletic population is one of his major interests.

Reuben F. Burch V

Reuben F. Burch, V is an Assistant Professor in Industrial & Systems Engineering and an Associate Director of Human Factors & Athlete Engineering in the Center for Advanced Vehicular Systems (CAVS) at Mississippi State University. His research are focused on human factors and ergonomics, cognitive engineering, macroergonomics, and human-technology interactions with athletes.

Daniel W. Carruth

Daniel W. Carruth is an Associate Director for Human Factors and Advanced Vehicle Systems at the Center for Advanced Vehicular Systems (CAVS) and an Assistant Research Professor at Mississippi State University. His work is mainly concentrated on modeling and simulation of human interaction with autonomous vehicles and cognitive factors impacting human performance.