Convolutional LSTM: a deep learning approach to predict shoulder joint reaction forces

Mubarrat, Syed T.; Chowdhury, S.

doi:10.1080/10255842.2022.2045974

Cited by 7 publications

(8 citation statements)

References 29 publications

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“…They evaluated linear models and neural networks and reported a minimum of 23% of normalized (based on the MSM predictions) RMSE for different joint reaction forces. Mubarrat and Chowdhury (2023) developed a convolutional long short-term memory (LSTM) model to predict shoulder joint reaction forces based on motion data for eight participants. They reported a mean of 18.6% normalized RMSE for medial-lateral, 19.2% for inferior-superior, and 21.3% for anterior-posterior force for their best model.…”

Section: Discussionmentioning

confidence: 99%

“…For the second study (Sharma et al, 2022), the input was motion data for the Reach-to-Grasp task in the forward direction executed at a self-selected pace. In the third study (Mubarrat and Chowdhury, 2023), the input consisted of 3D shoulder kinematics data collected across 30 different shoulder activities. However, in our case, the input was MSM results for three shoulder abductions.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, their input was recorded data for every patient, while the MSM we used scaled recorded data for one subject based on patientspecific parameters (Sarshari et al, 2021a,b). The models of De Vries et al ( 2016) and Mubarrat and Chowdhury (2023) predicted the time course of the glenohumeral joint force, and the model of Sharma et al (2022) predicted the time course of different MSM outcomes (e.g., joint angles and forces, muscle forces, and activations). The prediction of our model was the glenohumeral joint force for a specific elevation angle, but although the model was only trained on some elevation angles, it could make predictions for any elevation angle (Fi.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Glenohumeral Joint Force Prediction with Deep Learning

Eghbali,

Becce,

Goetti

et al. 2023

Preprint

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Glenohumeral Joint Force Prediction with Deep Learning

Eghbali,

Becce,

Goetti

et al. 2023

Preprint

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“…The lower-limb musculoskeletal model was developed using the biomechanical simulation software AnyBody version 7.4 (AnyBody Technology, Aalborg, Denmark), which has 3D motion capture dynamics. This software has undergone multiple experimental validations and demonstrates high reliability and accuracy 37 , 38 .…”

Section: Methodsmentioning

confidence: 99%

Impact of specialized fatigue and backhand smash on the ankle biomechanics of female badminton players

He,

Liu,

Zhang

et al. 2024

Sci Rep

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During fatigued conditions, badminton players may experience adverse effects on their ankle joints during smash landings. In addition, the risk of ankle injury may vary with different landing strategies. This study aimed to investigate the influence of sport-specific fatigue factors and two backhand smash actions on ankle biomechanical indices. Thirteen female badminton players (age: 21.2 ± 1.9 years; height: 167.1 ± 4.1 cm; weight: 57.3 ± 5.1 kg; BMI: 20.54 ± 1.57 kg/m2) participated in this study. An 8-camera Vicon motion capture system and three Kistler force platforms were used to collect kinematic and kinetic data before and after fatigue for backhand rear-court jump smash (BRJS) and backhand lateral jump smash (BLJS). A 2 × 2 repeated measures analysis of variance was employed to analyze the effects of these smash landing actions and fatigue factors on ankle biomechanical parameters. Fatigue significantly affected the ankle-joint plantarflexion and inversion angles at the initial contact (IC) phase (p < 0.05), with both angles increasing substantially post-fatigue. From a kinetic perspective, fatigue considerably influenced the peak plantarflexion and peak inversion moments at the ankle joint, which resulted in a decrease the former and an increase in the latter after fatigue. The two smash landing actions demonstrated different landing strategies, and significant main effects were observed on the ankle plantarflexion angle, inversion angle, peak dorsiflexion/plantarflexion moment, peak inversion/eversion moment, and peak internal rotation moment (p < 0.05). The BLJS landing had a much greater landing inversion angle, peak inversion moment, and peak internal rotation moment compared with BRJS landing. The interaction effects of fatigue and smash actions significantly affected the muscle force of the peroneus longus (PL), with a more pronounced decrease in the force of the PL muscle post-fatigue in the BLJS action(post-hoc < 0.05). This study demonstrated that fatigue and smash actions, specifically BRJS and BLJS, significantly affect ankle biomechanical parameters. After fatigue, both actions showed a notable increase in IC plantarflexion and inversion angles and peak inversion moments, which may elevate the risk of lateral ankle sprains. Compared with BRJS, BLJS poses a higher risk of lateral ankle sprains after fatigue.

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“…The musculoskeletal model of the lower limbs was built by the software AnyBody 7.2 (AnyBody Technology, Aalborg, Denmark), which processes 3D motion capture dynamics. The AnyBody 7.2 software has been validated by many experiments to operate with high reliability and accuracy ( Mubarrat & Chowdhury, 2023 ; Engelhardt et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Analysis of technical characteristics of typical lower limb balance movements in Tai Chi: a cross-sectional study based on AnyBody bone muscle modeling

Wang

et al. 2023

PeerJ

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Background Tai Chi is an excellent traditional Chinese physical fitness exercise, and its unique balancing movements are very important for improving human balance. In this study, the two most representative Tai Chi balance movements, “knee lift balance” and “leg stirrup balance”, were selected to establish the lower limb bone muscle model of Tai Chi balance movements by using computer simulation modeling technology, aiming to analyze the characteristics of the lower limb movement mechanisms of typical balance movements, to provide a quantitative theoretical basis for improving the scientific level of Tai Chi. Method A total of 16 subjects were recruited for this study. the BTS three-dimensional motion capture system and three-dimensional force platform were used for motion data acquisition, the physiological electromyographic signals were collected using BTS surface electromyography, and the lower limb bone muscle model of Tai Chi balance movements was established by AnyBody human simulation. Result In the knee lift balancing movement, the balance leg hip abduction/adduction angle, hip flexion/extension moment, and the strength of the rectus femoris muscle, biceps femoris short capitis, and iliacus muscle of the amateur group was significantly smaller than that of the professional group (P < 0.01). In the leg stirrup balance movement, the knee flexion/extension angle of the balancing leg in the amateur group was significantly greater than that in the professional group (P < 0.01), and the hip flexion/extension angle, hip inversion/abduction angle, knee flexion/extension moment, hip flexion/extension moment, the strength iliacus, gluteus maximus, and obturator internus were significantly smaller than those in the professional group (P < 0.01). The integral EMG of the biceps femoris of the support leg in the amateur group was significantly smaller than that of the professional group (P < 0.01). The integral EMG of the lateral femoral muscle of the balance leg was significantly smaller than that of the professional group (P < 0.01). Conclusion In this study, we found that the balancing leg of the balancing movement has a larger hip joint angle, the stirrup balancing knee joint angle is smaller, and the hip and knee joint moments are larger. This is related to joint activity and muscle activation, and amateurs should pay attention to increasing the range of motion of the hip joint and decreasing the range of motion of the knee joint when practicing to better stimulate exercise of the lower limb joints. In addition, the practice of balancing movements should strengthen the iliacus muscle, which plays an important role in maintaining the stable balance of the lower limbs, and strengthen the knee flexor and extensor muscles and hip adductor/abductor muscles of the balancing leg, thus promoting the stability of the balancing leg movements.

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Convolutional LSTM: a deep learning approach to predict shoulder joint reaction forces

Cited by 7 publications

References 29 publications

Glenohumeral Joint Force Prediction with Deep Learning

Glenohumeral Joint Force Prediction with Deep Learning

Impact of specialized fatigue and backhand smash on the ankle biomechanics of female badminton players

Analysis of technical characteristics of typical lower limb balance movements in Tai Chi: a cross-sectional study based on AnyBody bone muscle modeling

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