Literature DB >> 28411721

Ergonomic analysis of construction worker's body postures using wearable mobile sensors.

Nipun D Nath1, Reza Akhavian2, Amir H Behzadan3.   

Abstract

Construction jobs are more labor-intensive compared to other industries. As such, construction workers are often required to exceed their natural physical capability to cope with the increasing complexity and challenges in this industry. Over long periods of time, this sustained physical labor causes bodily injuries to the workers which in turn, conveys huge losses to the industry in terms of money, time, and productivity. Various safety and health organizations have established rules and regulations that limit the amount and intensity of workers' physical movements to mitigate work-related bodily injuries. A precursor to enforcing and implementing such regulations and improving the ergonomics conditions on the jobsite is to identify physical risks associated with a particular task. Manually assessing a field activity to identify the ergonomic risks is not trivial and often requires extra effort which may render it to be challenging if not impossible. In this paper, a low-cost ubiquitous approach is presented and validated which deploys built-in smartphone sensors to unobtrusively monitor workers' bodily postures and autonomously identify potential work-related ergonomic risks. Results indicates that measurements of trunk and shoulder flexions of a worker by smartphone sensory data are very close to corresponding measurements by observation. The proposed method is applicable for workers in various occupations who are exposed to WMSDs due to awkward postures. Examples include, but are not limited to industry laborers, carpenters, welders, farmers, health assistants, teachers, and office workers.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Construction safety; Ergonomics; Posture analysis; Risk assessment; Smartphone sensor; Wearable technology

Mesh:

Year:  2017        PMID: 28411721     DOI: 10.1016/j.apergo.2017.02.007

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  16 in total

Review 1.  On-Body Placement of Wearable Safety Promotion Devices Based on Wireless Communication for Construction Workers-on-Foot: State-of-the-Art Review.

Authors:  Neeraj Yadav; Neda Sadeghi; Julian Kang
Journal:  Sensors (Basel)       Date:  2022-04-20       Impact factor: 3.847

Review 2.  Evolution and Applications of Recent Sensing Technology for Occupational Risk Assessment: A Rapid Review of the Literature.

Authors:  Giacomo Fanti; Andrea Spinazzè; Francesca Borghi; Sabrina Rovelli; Davide Campagnolo; Marta Keller; Andrea Borghi; Andrea Cattaneo; Emanuele Cauda; Domenico Maria Cavallo
Journal:  Sensors (Basel)       Date:  2022-06-27       Impact factor: 3.847

3.  Classification of body postures using smart workwear.

Authors:  Christian Lins; Andreas Hein
Journal:  BMC Musculoskelet Disord       Date:  2022-10-18       Impact factor: 2.562

4.  Statistical prediction of load carriage mode and magnitude from inertial sensor derived gait kinematics.

Authors:  Sol Lim; Clive D'Souza
Journal:  Appl Ergon       Date:  2018-11-29       Impact factor: 3.661

5.  Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals.

Authors:  Mark C Schall; Richard F Sesek; Lora A Cavuoto
Journal:  Hum Factors       Date:  2018-01-10       Impact factor: 3.598

6.  Occupational Risk Prevention through Smartwatches: Precision and Uncertainty Effects of the Built-In Accelerometer.

Authors:  Luis Sigcha; Ignacio Pavón; Pedro Arezes; Nélson Costa; Guillermo De Arcas; Juan Manuel López
Journal:  Sensors (Basel)       Date:  2018-11-06       Impact factor: 3.576

7.  The role of wearables in spinal posture analysis: a systematic review.

Authors:  Lauren Simpson; Monish M Maharaj; Ralph J Mobbs
Journal:  BMC Musculoskelet Disord       Date:  2019-02-08       Impact factor: 2.362

8.  Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach.

Authors:  Ilaria Conforti; Ilaria Mileti; Zaccaria Del Prete; Eduardo Palermo
Journal:  Sensors (Basel)       Date:  2020-03-11       Impact factor: 3.576

9.  Psychosocial and Ergonomic Conditions at Work: Influence on the Probability of a Workplace Accident.

Authors:  J R López-García; S García-Herrero; J M Gutiérrez; M A Mariscal
Journal:  Biomed Res Int       Date:  2019-11-11       Impact factor: 3.411

10.  An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders.

Authors:  Ze Li; Ruiqiu Zhang; Ching-Hung Lee; Yu-Chi Lee
Journal:  Sensors (Basel)       Date:  2020-08-07       Impact factor: 3.576

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