Prediction of slaughterhouse workers’ RULA scores and knife edge using low-cost inertial measurement sensor units and machine learning algorithms

Villalobos, A S Jorge; Vergara, Alejandro

doi:10.1016/j.apergo.2021.103556

Cited by 12 publications

(4 citation statements)

References 23 publications

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“…This methodology might, therefore, be tested on a larger population of participants to support current outcomes. Moreover, inspired by recent works [50,51], a machine learning-based system could be trained and tested on specific workers to automatically identify risky subtasks. Given the societal burden of MSDs affecting hospital workers, the use of advanced aiding systems and assistive devices would also be valuable.…”

Section: Discussionmentioning

confidence: 99%

Physical Ergonomic Assessment in Cleaning Hospital Operating Rooms Based on Inertial Measurement Units

Koskas,

Vignais

2024

Bioengineering

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Workers involved in hospital operating room cleaning face numerous constraints that may lead to musculoskeletal disorders. This study aimed to perform physical ergonomic assessments on hospital staff by combining a continuous assessment (RULA) based on inertial measurement units with video coding. Eight participants performed cleaning tasks while wearing IMUs and being video recorded. A subjective evaluation was performed through the Nordic questionnaire. Global RULA scores equaled 4.21 ± 1.15 and 4.19 ± 1.20 for the right and left sides, respectively, spending most of the time in the RULA range of 3–4 (right: 63.54 ± 31.59%; left: 64.33 ± 32.33%). Elbows and lower arms were the most exposed upper body areas with the highest percentages of time spent over a risky threshold (right: 86.69 ± 27.27%; left: 91.70 ± 29.07%). The subtask analysis identified ‘operating table moving’, ‘stretcher moving’, and ‘trolley moving’ as the riskiest subtasks. Thus, this method allowed an extensive ergonomic analysis, highlighting both risky anatomical areas and subtasks that need to be reconsidered.

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Section: Discussionmentioning

confidence: 99%

Physical Ergonomic Assessment in Cleaning Hospital Operating Rooms Based on Inertial Measurement Units

Koskas,

Vignais

2024

Bioengineering

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“…Pattern recognition and predictive models allow for calculating risk scores using different risk assessment methods. Examples of such usage are reported for risk assessment of lifting action using RULA [112], the Revised NIOSH Lifting Equation (RNLE) [286,287], and accurate tracking of human activity recognition [288][289][290]. Pattern recognition can also be used to identify subject-specific kinematic fatigue responses, such as identifying early signs of fatigue in manual handling operations and training the machine learning to learn the subject's normal movement patterns [291].…”

Section: Opportunities In Occupational Applications Of Wearable Techn...mentioning

confidence: 99%

Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics—An Overview of Current Applications, Challenges, and Future Opportunities

Lind

Abtahi

Forsman

2023

Sensors

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Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has a role in preventing WMSDs by contributing to improvements in exposure and risk assessment and potentially improved effectiveness in work technique training. Given the versatile potential for wearables, this article aims to provide an overview of their application related to the prevention of WMSDs of the trunk and upper limbs and discusses challenges for the technology to support prevention measures and future opportunities, including future research needs. The relevant literature was identified from a screening of recent systematic literature reviews and overviews, and more recent studies were identified by a literature search using the Web of Science platform. Wearable technology enables continuous measurements of multiple body segments of superior accuracy and precision compared to observational tools. The technology also enables real-time visualization of exposures, automatic analyses, and real-time feedback to the user. While miniaturization and improved usability and wearability can expand the use also to more occupational settings and increase use among occupational safety and health practitioners, several fundamental challenges remain to be resolved. The future opportunities of increased usage of wearable motion capture devices for the prevention of work-related musculoskeletal disorders may require more international collaborations for creating common standards for measurements, analyses, and exposure metrics, which can be related to epidemiologically based risk categories for work-related musculoskeletal disorders.

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“…In contrast to prior studies [21], [29], [30], [42], [43] that used IMUs to track human motion and joint angle data as inputs for automated risk assessment based on ergonomic instruments, mainly RULA, we conducted a posture evaluation in an automotive assembly line, giving the developed methods a real-world application. Moreover, we conducted an operator-level analysis which revealed relevant differences in operators' work methods that might be related to their anthropometry characteristics.…”

Section: ) Systems Differencesmentioning

confidence: 99%

“…These feature-based representations have been used to discover undefined and seemingly featureless patterns in data to characterize industrial processes. Examples of features are inertial and orientation data [20], [33]- [38], data-related statistical measurements [37], [39]- [41] and metrics derived from the application of risk assessment instruments [42], [43].…”

Section: Introductionmentioning

confidence: 99%

Posture Risk Assessment in an Automotive Assembly Line Using Inertial Sensors

et al. 2022

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Musculoskeletal disorders (MSD) are a highly prevalent work-related health problem. Biomechanical exposure to hazardous postures during work is a risk factor for the development of MSD. This study focused on developing an inertial sensor-based approach to evaluate posture in industrial contexts, particularly in automotive assembly lines. The analysis was divided into two stages: 1) a comparative study of joint angles calculated during movements of the upper body segments using the proposed motion tracking framework and the ones provided by a state-of-the-art inertial motion capture system and 2) a work-related posture risk evaluation of operators working in an automative assembly line. For the comparative study, we selected data collected in laboratory (N = 8 participants) and assembly line settings (N = 9 participants), while for the work-related posture risk evaluation, we only considered data acquired within the automotive assembly line. The results revealed that the proposed framework could be applied to track industrial tasks movements performed on the sagittal plane, and the posture evaluation uncovered posture risk differences among different operators that are not considered in traditional posture risk assessment instruments.

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Prediction of slaughterhouse workers’ RULA scores and knife edge using low-cost inertial measurement sensor units and machine learning algorithms

Cited by 12 publications

References 23 publications

Physical Ergonomic Assessment in Cleaning Hospital Operating Rooms Based on Inertial Measurement Units

Physical Ergonomic Assessment in Cleaning Hospital Operating Rooms Based on Inertial Measurement Units

Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics—An Overview of Current Applications, Challenges, and Future Opportunities

Posture Risk Assessment in an Automotive Assembly Line Using Inertial Sensors

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