A computer vision based method for 3D posture estimation of symmetrical lifting

Mehrizi, Rahil; Peng, Xi; Xu, Xu; Zhang, Shaoting; Metaxas, Dimitris N.; Li, Kang

doi:10.1016/j.jbiomech.2018.01.012

Cited by 53 publications

(23 citation statements)

References 23 publications

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“…As such, they did not perform any ergonomics related biomechanics experiment but their tool has great potential. Mehrizi et al (Mehrizi et al 2018) proposed a model that could extract the joint angles of twelve healthy males performing symmetric lifting tasks. However, Mehrizi et al focused on the creation of an accurate measuring apparatus and did not examine any biomechanics regarding ergonomics either.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment

Menychtas

Glushkova

Manitsaris

2020

J Ambient Intell Human Comput

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During an eight-hour shift, an industrial worker will inevitably cycle through specific postures. Those postures can cause microtrauma on the musculoskeletal system that accumulates, which in turn can lead to chronic injury. To assess how problematic a posture is, the rapid upper limb assessment (RULA) scoring system is widely employed by the industry. Even though it is a very quick and efficient method of assessment, RULA is not a biomechanics-based measurement that is anchored in a physical parameter of the human body. As such RULA does not give a detailed description of the impact each posture has on the human joints but rather, an overarching, simplified assessment of a posture. To address this issue, this paper proposes the use of joint angles and torques as an alternative way of ergonomics evaluation. The cumulative motion and torque throughout a trial is compared with the average motions and torques for the same task. This allows the evaluation of each joint's kinematic and kinetic performance while still be able to assess a task"at-a-glance". To do this, an upper human body model was created and the mass of each segment were assigned. The joint torques and the RULA scores were calculated for simple range of motion (ROM) tasks, as well as actual tasks from a TV assembly line. The joint angles and torques series were integrated and then normalized to give the kinematic and kinetic contribution of each joint during a task as a percentage. This made possible to examine each joint's strain during each task as well as highlight joints that need to be more closely examined. Results show how the joint angles and torques can identify which joint is moving more and which one is under the most strain during a task. It was also possible to compare the performance of a task with the average performance and identify deviations that may imply improper execution. Even though the RULA is a very fast and concise assessment tool, it leaves little room for further analyses. However, the proposed work suggests a richer alternative without sacrificing the benefit of a quick evaluation. The biggest limitation of this work is that a pool of proper executions needs to be recorded for each task before individual comparisons can be done.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Introductionmentioning

confidence: 99%

Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment

Menychtas

Glushkova

Manitsaris

2020

J Ambient Intell Human Comput

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“…There are few studies which explored the field of computer vision and proposed marker-less methods for biomechanical and clinical applications. In particular, [9], [10] proposed a computer vision based method for estimation of 3D pose estimation and lower back loads in the symmetrical lifting tasks. In another study by [11], a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm was employed to estimate human motion through a markerless motion capture system.…”

Section: A Pose Estimation For Biomechanical Applicationmentioning

confidence: 99%

Toward Marker-Free 3D Pose Estimation in Lifting: A Deep Multi-View Solution

Mehrizi

Peng

Tang

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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Lifting is a common manual material handling task performed in the workplaces. It is considered as one of the main risk factors for Work-related Musculoskeletal Disorders. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks, which requires very accurate 3D pose. Existing approaches mainly utilize marker-based sensors to collect 3D information. However, these methods are usually expensive to setup, timeconsuming in process, and sensitive to the surrounding environment. In this study, we propose a multi-view based deep perceptron approach to address aforementioned limitations. Our approach consists of two modules: a "view-specific perceptron" network extracts rich information independently from the image of view, which includes both 2D shape and hierarchical texture information; while a "multi-view integration" network synthesizes information from all available views to predict accurate 3D pose. To fully evaluate our approach, we carried out comprehensive experiments to compare different variants of our design. The results prove that our approach achieves comparable performance with former marker-based methods, i.e. an average error of 14.72 ± 2.96 mm on the lifting dataset. The results are also compared with state-of-the-art methods on HumanEva-I dataset [1], which demonstrates the superior performance of our approach.

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“…Moreover, the authors in [19] proposed a deep neural network-(DNN-) based customer-specific detector that can efficiently thwart such cyber attacks. In recent years, the CNN has been applied to generate useful and discriminative features from raw data and has wide applications in different areas [20][21][22]. ese applications motivate the CNN applied for feature extraction from high-resolution smart meter data in electricity theft detection.…”

Section: Introductionmentioning

confidence: 99%

Electricity Theft Detection in Power Grids with Deep Learning and Random Forests

Shuan

Han

Yao

et al. 2019

Journal of Electrical and Computer Engineering

159

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As one of the major factors of the nontechnical losses (NTLs) in distribution networks, the electricity theft causes significant harm to power grids, which influences power supply quality and reduces operating profits. In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, a novel hybrid convolutional neural network-random forest (CNN-RF) model for automatic electricity theft detection is presented in this paper. In this model, a convolutional neural network (CNN) firstly is designed to learn the features between different hours of the day and different days from massive and varying smart meter data by the operations of convolution and downsampling. In addition, a dropout layer is added to retard the risk of overfitting, and the backpropagation algorithm is applied to update network parameters in the training phase. And then, the random forest (RF) is trained based on the obtained features to detect whether the consumer steals electricity. To build the RF in the hybrid model, the grid search algorithm is adopted to determine optimal parameters. Finally, experiments are conducted based on real energy consumption data, and the results show that the proposed detection model outperforms other methods in terms of accuracy and efficiency.

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A computer vision based method for 3D posture estimation of symmetrical lifting

Cited by 53 publications

References 23 publications

Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment

Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment

Toward Marker-Free 3D Pose Estimation in Lifting: A Deep Multi-View Solution

Electricity Theft Detection in Power Grids with Deep Learning and Random Forests

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