Predicting 3-D Lower Back Joint Load in Lifting: A Deep Pose Estimation Approach

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

doi:10.1109/thms.2018.2884811

Cited by 32 publications

(13 citation statements)

References 36 publications

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“…It can be considered an explorative study assessing the potential of such an approach to bring substantial methodological benefit towards the field of sports science and sports medicine. Similar studies on the accuracy of monocular 3D pose estimation have been conducted in lab conditions, for instance, for analyzing forces and load during weightlifting [33]. However, when interpreting the current findings, one should have the following study/method limitations in mind.…”

Section: Discussionmentioning

confidence: 84%

Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing

Ostrek

Rhodin

Fua

et al. 2019

Sensors

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In this study, we compared a monocular computer vision (MCV)-based approach with the golden standard for collecting kinematic data on ski tracks (i.e., video-based stereophotogrammetry) and assessed its deployment readiness for answering applied research questions in the context of alpine skiing. The investigated MCV-based approach predicted the three-dimensional human pose and ski orientation based on the image data from a single camera. The data set used for training and testing the underlying deep nets originated from a field experiment with six competitive alpine skiers. The normalized mean per joint position error of the MVC-based approach was found to be 0.08 ± 0.01 m. Knee flexion showed an accuracy and precision (in parenthesis) of 0.4 ± 7.1° (7.2 ± 1.5°) for the outside leg, and −0.2 ± 5.0° (6.7 ± 1.1°) for the inside leg. For hip flexion, the corresponding values were −0.4 ± 6.1° (4.4° ± 1.5°) and −0.7 ± 4.7° (3.7 ± 1.0°), respectively. The accuracy and precision of skiing-related metrics were revealed to be 0.03 ± 0.01 m (0.01 ± 0.00 m) for relative center of mass position, −0.1 ± 3.8° (3.4 ± 0.9) for lean angle, 0.01 ± 0.03 m (0.02 ± 0.01 m) for center of mass to outside ankle distance, 0.01 ± 0.05 m (0.03 ± 0.01 m) for fore/aft position, and 0.00 ± 0.01 m2 (0.01 ± 0.00 m2) for drag area. Such magnitudes can be considered acceptable for detecting relevant differences in the context of alpine skiing.

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Section: Discussionmentioning

confidence: 84%

Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing

Ostrek

Rhodin

Fua

et al. 2019

Sensors

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show abstract

“…For the performance measures to compare and validate different regressions, root mean squared error (RMSE), Pearson's correlation coefficient (R), and intraclass correlation coefficients (ICC) were computed [46]. For the ICC values, less than 0.40 was poor; between 0.40 and 0.75 was good, and greater than 0.75 was considered excellent [47].…”

Section: Plos Onementioning

confidence: 99%

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches

Hwang

Lee

2021

PLoS ONE

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The objective of this study was to accurately predict the grip strength using a deep learning-based method (e.g., multi-layer perceptron [MLP] regression). The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collected. The data set was divided into a training set (90% of data) and a test set (10% of data). Different combinations of variables including demographic and anthropometric information of individual participants and postures was tested and compared to find the most predictive model. The MLP regression and 3 different polynomial regressions (linear, quadratic, and cubic) were conducted and the performance of regression was compared. The results showed that including all variables showed better performance than other combinations of variables. In general, MLP regression showed higher performance than polynomial regressions. Especially, MLP regression considering all variables achieved the highest performance of grip strength prediction (RMSE = 69.01N, R = 0.88, ICC = 0.92). This deep learning-based regression (MLP) would be useful to predict on-site- and individual-specific grip strength in the workspace to reduce the risk of musculoskeletal disorders in the upper extremity.

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“…However, 3-D motion capture systems are unsuitable for personal fitness because they require large, complex, and expensive measurement environments comprising multiple motion tracking cameras and markers affixed to the bodies of subjects. The other method is an image-processing approach that employs deep convolutional neural networks to learn the image features for activity recognition [6,7] and human pose estimation [8][9][10]. Because the video image processing needs high computation power, it is not proper for self-coaching system in home.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Squat Posture Using Inertial Sensors: Deep Learning Approach

Lee

Joo

Lee

et al. 2020

Sensors

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Without expert coaching, inexperienced exercisers performing core exercises, such as squats, are subject to an increased risk of spinal or knee injuries. Although it is theoretically possible to measure the kinematics of body segments and classify exercise forms with wearable sensors and algorithms, the current implementations are not sufficiently accurate. In this study, the squat posture classification performance of deep learning was compared to that of conventional machine learning. Additionally, the location for the optimal placement of sensors was determined. Accelerometer and gyroscope data were collected from 39 healthy participants using five inertial measurement units (IMUs) attached to the left thigh, right thigh, left calf, right calf, and lumbar region. Each participant performed six repetitions of an acceptable squat and five incorrect forms of squats that are typically observed in inexperienced exercisers. The accuracies of squat posture classification obtained using conventional machine learning and deep learning were compared. Each result was obtained using one IMU or a combination of two or five IMUs. When employing five IMUs, the accuracy of squat posture classification using conventional machine learning was 75.4%, whereas the accuracy using deep learning was 91.7%. When employing two IMUs, the highest accuracy (88.7%) was obtained using deep learning for a combination of IMUs on the right thigh and right calf. The single IMU yielded the best results on the right thigh, with an accuracy of 58.7% for conventional machine learning and 80.9% for deep learning. Overall, the results obtained using deep learning were superior to those obtained using conventional machine learning for both single and multiple IMUs. With regard to the convenience of use in self-fitness, the most feasible strategy was to utilize a single IMU on the right thigh.

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Predicting 3-D Lower Back Joint Load in Lifting: A Deep Pose Estimation Approach

Cited by 32 publications

References 36 publications

Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing

Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches

Automatic Classification of Squat Posture Using Inertial Sensors: Deep Learning Approach

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