Posture Estimation in Agriculture: Employing Inertial Measurement Units and Unscented Kalman Filtering for Trunk, Shoulder, and Elbow Analysis

Farmworkers are often at risk of musculoskeletal health problems, with low back pain being the most common, accounting for approximately over half of the population worldwide. Moreover, these musculoskeletal disorders (MSD) are prevalent in other body regions including shoulders, elbows, wrists or h...

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Vydané v:	IEEE access Ročník 12; s. 97332 - 97345
Hlavní autori:	Zougali, Amine, Thamsuwan, Ornwipa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accelerometers Agriculture Algorithms Elbow Elbow (anatomy) Estimation Farming Gyroscopes Human factors human posture Inertial platforms inertial sensor Kalman filters Magnetic disturbances Magnetometers Musculoskeletal system Pain Pitch (inclination) Posture Risk analysis Sensor fusion sensor fusion algorithms Shoulder Task analysis unscented Kalman filter
ISSN:	2169-3536, 2169-3536
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Abstract	Farmworkers are often at risk of musculoskeletal health problems, with low back pain being the most common, accounting for approximately over half of the population worldwide. Moreover, these musculoskeletal disorders (MSD) are prevalent in other body regions including shoulders, elbows, wrists or hands. As these issues were found related to inappropriate working postures, it is beneficial to quantify these postures in order to prevent work-related MSD. The primary focus of this study was to improve the estimation of the postures and the exposure to non-neutral postures among agricultural workers. Three inertial measurement units (IMU) were attached to the upper back, upper arm, and forearm of nine workers while they were performing their regular work activities. A posture characterization algorithm was developed to rely on the data from only accelerometers and gyroscopes while excluding magnetometer readings due to high magnetic disturbance. Despite these challenges, a specialized unscented Kalman filter (UKF) was developed to achieve a more precise posture estimation. The UKF effectively expanded the range of pitch angles from ±90 degrees to ±180 degrees, resulting in a substantial improvement in the assessment of the back inclination as well as shoulder and elbow angles. The study was carried out among workers in a large-scale plant nursery, revealing instances of extreme postures in the back, upper arms, and elbows during their work activities. The quantitative findings highlighted the high exposure to ergonomic risks faced by the workers. This emphasized the urgent need for implementing appropriate measures to mitigate these risks.
AbstractList	Farmworkers are often at risk of musculoskeletal health problems, with low back pain being the most common, accounting for approximately over half of the population worldwide. Moreover, these musculoskeletal disorders (MSD) are prevalent in other body regions including shoulders, elbows, wrists or hands. As these issues were found related to inappropriate working postures, it is beneficial to quantify these postures in order to prevent work-related MSD. The primary focus of this study was to improve the estimation of the postures and the exposure to non-neutral postures among agricultural workers. Three inertial measurement units (IMU) were attached to the upper back, upper arm, and forearm of nine workers while they were performing their regular work activities. A posture characterization algorithm was developed to rely on the data from only accelerometers and gyroscopes while excluding magnetometer readings due to high magnetic disturbance. Despite these challenges, a specialized unscented Kalman filter (UKF) was developed to achieve a more precise posture estimation. The UKF effectively expanded the range of pitch angles from ±90 degrees to ±180 degrees, resulting in a substantial improvement in the assessment of the back inclination as well as shoulder and elbow angles. The study was carried out among workers in a large-scale plant nursery, revealing instances of extreme postures in the back, upper arms, and elbows during their work activities. The quantitative findings highlighted the high exposure to ergonomic risks faced by the workers. This emphasized the urgent need for implementing appropriate measures to mitigate these risks.
Author	Zougali, Amine Thamsuwan, Ornwipa
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SubjectTerms	Accelerometers Agriculture Algorithms Elbow Elbow (anatomy) Estimation Farming Gyroscopes Human factors human posture Inertial platforms inertial sensor Kalman filters Magnetic disturbances Magnetometers Musculoskeletal system Pain Pitch (inclination) Posture Risk analysis Sensor fusion sensor fusion algorithms Shoulder Task analysis unscented Kalman filter
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Title	Posture Estimation in Agriculture: Employing Inertial Measurement Units and Unscented Kalman Filtering for Trunk, Shoulder, and Elbow Analysis
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