Evaluation of ground reaction forces and centers of pressure predicted by AnyBody Modeling System during load reaching/handling activities and effects of the prediction errors on model-estimated spinal loads

Daroudi, S.; Arjmand, N.; Mohseni, M.; El-Rich, M.; Parnianpour, M.

doi:10.1016/j.jbiomech.2024.111974

Journal of Biomechanics

2024

DOI: 10.1016/j.jbiomech.2024.111974

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Evaluation of ground reaction forces and centers of pressure predicted by AnyBody Modeling System during load reaching/handling activities and effects of the prediction errors on model-estimated spinal loads

S. Daroudi,

N. Arjmand,

M. Mohseni

et al.

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Cited by 3 publications

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Subject-Specific Parametric Identification of a Spine-Equivalent Beam Model for Lumbar Force Prediction in Sagittal Plane

Ding,

Lee,

Jiang

et al. 2024

IEEE Trans. Instrum. Meas.

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Subject-Specific Parametric Identification of a Spine-Equivalent Beam Model for Lumbar Force Prediction in Sagittal Plane

Ding,

Lee,

Jiang

et al. 2024

IEEE Trans. Instrum. Meas.

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BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment

Hulleck,

AlShehhi,

El Rich

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Evaluation of human gait through smartphonebased pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait. This study introduces a novel enhancement to the low-powered BlazePose algorithm by incorporating a Seq2seq autoencoder deep learning model. To ensure data accuracy and reliability, synchronized motion capture involving an RGB camera and ten Vicon cameras were employed across three distinct self-selected walking speeds. This investigation presents a groundbreaking avenue for remote gait assessment, harnessing the potential of Seq2seq architectures inspired by natural language processing (NLP) to enhance pose estimation accuracy. When comparing BlazePose alone to the combination of BlazePose and 1D convolution Long Short-term Memory Network (1D-LSTM), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the average mean absolute errors decreased from 13.4⁰ to 5.3⁰ for fast gait, from 16.3⁰ to 7.5⁰ for normal gait, and from 15.5⁰ to 7.5⁰ for slow gait at the left ankle joint angle respectively. The strategic utilization of synchronized data and rigorous testing methodologies further bolsters the robustness and credibility of these findings.

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Musculoskeletal simulation for patient transfer using hand load measurement device

Uchimura,

Fushitani,

Kitagawa

et al. 2024

Int J Res Orthop

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Background: Unsuitable posture in patient transfer motion causes lower back pain (LBP) among caregivers. The suitable postures to reducing lumbar loads during patient transfer are investigated by musculoskeletal simulation. However, existing musculoskeletal simulation cannot accurately predict lumbar loads because the existing musculoskeletal models are generated by only motion data. Thus, this study aimed to propose and evaluate an accurate musculoskeletal model using hand load data obtained from a hand load measurement device. Methods: Motion and hand load data for the musculoskeletal model were measured during patient transfer by an inertial measurement unit (IMU)-based motion capture system and hand load measurement device. The existing model without using hand load data and the proposed model using hand load data predicted the activity of the erector spinae muscles and the compressive force of L4-L5. The correlation of erector spinae muscle activity was compared between the predicted and ground truth (surface electromyography) values. Furthermore, predicted compressive forces of L4-L5 were compared with reference value reported by previous study related to in vivo intradiscal pressures measurement. Results: The proposed model could predict erector spinae muscle activity with a correlation that was significantly greater than that of the existing model (p<0.05). Furthermore, the proposed model could predict compressive forces of L4-L5 with approximate values close to in vivo intradiscal pressures measurement. Conclusions: Proposed musculoskeletal model may more accurately predict lumbar loads during patient transfer than the existing model. Proposed musculoskeletal model will be applied to explore suitable postures for preventing LBP.

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Evaluation of ground reaction forces and centers of pressure predicted by AnyBody Modeling System during load reaching/handling activities and effects of the prediction errors on model-estimated spinal loads

Cited by 3 publications

References 40 publications

Subject-Specific Parametric Identification of a Spine-Equivalent Beam Model for Lumbar Force Prediction in Sagittal Plane

Subject-Specific Parametric Identification of a Spine-Equivalent Beam Model for Lumbar Force Prediction in Sagittal Plane

BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment

Musculoskeletal simulation for patient transfer using hand load measurement device

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