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Development of a real‐time work‐related postural risk assessment system of farm workers using a sensor‐based artificial intelligence approach.

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Title: Development of a real‐time work‐related postural risk assessment system of farm workers using a sensor‐based artificial intelligence approach.
Authors: Singh, Lakhwinder Pal1 (AUTHOR) singhl@nitj.ac.in, Kumar, Praveen1 (AUTHOR), Lohan, Shiv Kumar2 (AUTHOR)
Source: Journal of Field Robotics. Oct2024, Vol. 41 Issue 7, p2100-2113. 14p.
Subjects: Kinect (Motion sensor), Farm mechanization, Agriculture, Musculoskeletal system diseases, Posture
Abstract: In recent years, the promotion of farm mechanization has been directed toward reducing the human discomfort and fatigue associated with various agricultural work‐related activities. During these activities, many factors (like force, awkward posture, vibration, repetition, etc.) play a significant role in causing musculoskeletal disorders. Second, ergonomic risk assessment of physical work is conventionally conducted through observation and direct/indirect physiological measurements. However, these methods are time‐consuming and require human subjects to perform the motion to obtain detailed body movement data. In the present study, a semiautomatic rapid entire body assessment (REBA) evaluation tool is developed for real‐time assessment of agricultural work‐related musculoskeletal disorders risk of farm workers using Kinect V2 sensor‐based artificial intelligence approach. It allows the investigator speedy detect of awkward postures leading to critical conditions and to reduce subjective bias. It is useful to analyze online as well as offline posture analysis, it detects the critical areas of the body posture, which may lead to the musculoskeletal disorders of agricultural workers, and suggest aptly to correct the posture. The Kinect V2 REBA assessment score was found with a factual significant match with the reference expert evaluation as reflected by the Landis and Koch scale k = 0.673 (p < 0.001), 95% confidence interval (CI) for the left side, and k = 0.644 (p < 0.001), 95% CI for the right side of the body respectively. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: In recent years, the promotion of farm mechanization has been directed toward reducing the human discomfort and fatigue associated with various agricultural work‐related activities. During these activities, many factors (like force, awkward posture, vibration, repetition, etc.) play a significant role in causing musculoskeletal disorders. Second, ergonomic risk assessment of physical work is conventionally conducted through observation and direct/indirect physiological measurements. However, these methods are time‐consuming and require human subjects to perform the motion to obtain detailed body movement data. In the present study, a semiautomatic rapid entire body assessment (REBA) evaluation tool is developed for real‐time assessment of agricultural work‐related musculoskeletal disorders risk of farm workers using Kinect V2 sensor‐based artificial intelligence approach. It allows the investigator speedy detect of awkward postures leading to critical conditions and to reduce subjective bias. It is useful to analyze online as well as offline posture analysis, it detects the critical areas of the body posture, which may lead to the musculoskeletal disorders of agricultural workers, and suggest aptly to correct the posture. The Kinect V2 REBA assessment score was found with a factual significant match with the reference expert evaluation as reflected by the Landis and Koch scale k = 0.673 (p &lt; 0.001), 95% confidence interval (CI) for the left side, and k = 0.644 (p &lt; 0.001), 95% CI for the right side of the body respectively. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: &lt;i&gt;Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.1002/rob.22215
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 2100
    Subjects:
      – SubjectFull: Kinect (Motion sensor)
        Type: general
      – SubjectFull: Farm mechanization
        Type: general
      – SubjectFull: Agriculture
        Type: general
      – SubjectFull: Musculoskeletal system diseases
        Type: general
      – SubjectFull: Posture
        Type: general
    Titles:
      – TitleFull: Development of a real‐time work‐related postural risk assessment system of farm workers using a sensor‐based artificial intelligence approach.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Singh, Lakhwinder Pal
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          Name:
            NameFull: Kumar, Praveen
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            NameFull: Lohan, Shiv Kumar
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          Dates:
            – D: 01
              M: 10
              Text: Oct2024
              Type: published
              Y: 2024
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              Value: 41
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            – TitleFull: Journal of Field Robotics
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