Development of an Algorithm for Automatically Assessing Lifting Risk Factors Using Inertial Measurement Units

Lu, Ming‐Lun; Feng, Shuo; Hughes, Grant; Barim, Menekse Salar; Hayden, Marie; Werren, Dwight

doi:10.1177/1071181319631252

Cited by 10 publications

(10 citation statements)

References 21 publications

(15 reference statements)

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“…Moreover, machine learning (ML) algorithms are gaining popularity in the ergonomic field for biomechanical risk assessment by means of data acquired by wearable inertial systems. Several publications have appeared in recent years documenting several strategies [ 27 , 28 , 29 ]. IMU systems, which incorporate machine learning into their data analysis pathways, have been found effective in automated exercise detection and in classifying movement quality across a range of lower limb exercises, including lifting, despite studies in this field having so far involved few samples [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

Donisi

Cesarelli

Coccia

et al. 2021

Sensors

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Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity.

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Section: Introductionmentioning

confidence: 99%

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

Donisi

Cesarelli

Coccia

et al. 2021

Sensors

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“…Of course, the cases where the lift detection algorithm mislabeled a start or end have very poor parameter estimation results. To counter this, it could be possible to use additional features, such as inertial coupling between the hands [24], as a redundant check to make sure that a lift is actually occurring between the detected start and end times. If one is not, the algorithm can strategically search for better times.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…The biggest challenge in developing the algorithms for real-time would be identifying the start and end of a lift amidst a continuous stream of data. Lu et al demonstrated one plausible method for accomplishing this using a sliding pattern recognition window [24]. Once that problem is solved, the analysis could be done the same way it is done offline.…”

Section: Conversion To Real-timementioning

confidence: 99%

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Characterizing Human Box-Lifting Behavior Using Wearable Inertial Motion Sensors

Hlucny

Novak

2020

Sensors

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Although several studies have used wearable sensors to analyze human lifting, this has generally only been done in a limited manner. In this proof-of-concept study, we investigate multiple aspects of offline lift characterization using wearable inertial measurement sensors: detecting the start and end of the lift and classifying the vertical movement of the object, the posture used, the weight of the object, and the asymmetry involved. In addition, the lift duration, horizontal distance from the lifter to the object, the vertical displacement of the object, and the asymmetric angle are computed as lift parameters. Twenty-four healthy participants performed two repetitions of 30 different main lifts each while wearing a commercial inertial measurement system. The data from these trials were used to develop, train, and evaluate the lift characterization algorithms presented. The lift detection algorithm had a start time error of 0.10 s ± 0.21 s and an end time error of 0.36 s ± 0.27 s across all 1489 lift trials with no missed lifts. For posture, asymmetry, vertical movement, and weight, our classifiers achieved accuracies of 96.8%, 98.3%, 97.3%, and 64.2%, respectively, for automatically detected lifts. The vertical height and displacement estimates were, on average, within 25 cm of the reference values. The horizontal distances measured for some lifts were quite different than expected (up to 14.5 cm), but were very consistent. Estimated asymmetry angles were similarly precise. In the future, these proof-of-concept offline algorithms can be expanded and improved to work in real-time. This would enable their use in applications such as real-time health monitoring and feedback for assistive devices.

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“…The equations below were used for estimating the V and H variables. The development of the algorithm is described in detail in a separate proceeding paper (Lu et al, 2019).…”

Section: Algorithm For Processing Imu Datamentioning

confidence: 99%

Accuracy of An Algorithm Using Motion Data Of Five Wearable IMU Sensors For Estimating Lifting Duration And Lifting Risk Factors

Barim

Feng

et al. 2019

Proceedings of the Human Factors and Ergonomics Society Annual

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The objective of this study was to assess the accuracy of an algorithm for processing data from five inertial measurement unit (IMU) sensors for measuring the vertical distance (V) and horizontal distance (H) of two handed lifting, trunk flexion angle (T) and lifting duration (LD). The sensors were placed on five body segments including the left wrist, right wrist, upper arm of the dominant hand, upper back, and thigh of the dominant leg. A laboratory-grade optical motion capture system was used as the ground truth for the assessment. Data were collected on ten subjects that performed 12 two-handed lifting tasks varying in height of the hands and horizontal distance between the body and the lifted object. Results showed that the algorithm performed well for determining the LD (~1 sec error) and T (~2° error). The average errors for V and H were about 33 and 6.5 cm, respectively.

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Development of an Algorithm for Automatically Assessing Lifting Risk Factors Using Inertial Measurement Units

Cited by 10 publications

References 21 publications

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

Characterizing Human Box-Lifting Behavior Using Wearable Inertial Motion Sensors

Accuracy of An Algorithm Using Motion Data Of Five Wearable IMU Sensors For Estimating Lifting Duration And Lifting Risk Factors

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