Predicting Coordination Variability of Selected Lower Extremity Couplings during a Cutting Movement: An Investigation of Deep Neural Networks with the LSTM Structure

Shao, Enze; Mei, Qichang; Ye, Jingyi; Ugbolue, Ukadike C.; Chen, Chaoyi; Gu, Yaodong

doi:10.3390/bioengineering9090411

Cited by 4 publications

(3 citation statements)

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“…The adoption of a field-based clustering paradigm that takes into consideration the whole-body motion instead of a single joint movement pattern is deemed important in ACL injury prevention research. For example, recent studies are adopting vector coding technique to identify intra- and inter-joint coordination for trunk, hip, knee, and ankle, and link it to the risk of sustaining an ACL injury [ 38 , 40 , 41 , 42 ]. Data mining approaches based on waveform features such as the one adopted in the present study might facilitate this process, reducing the number of informative features while continuing to account for the task complexity [ 22 , 42 , 43 , 44 , 45 ].…”

Section: Discussionmentioning

confidence: 99%

Definition of High-Risk Motion Patterns for Female ACL Injury Based on Football-Specific Field Data: A Wearable Sensors Plus Data Mining Approach

Paolo

Nijmeijer

Bragonzoni

et al. 2023

Sensors

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The aim of the present study was to investigate if the presence of anterior cruciate ligament (ACL) injury risk factors depicted in the laboratory would reflect at-risk patterns in football-specific field data. Twenty-four female footballers (14.9 ± 0.9 year) performed unanticipated cutting maneuvers in a laboratory setting and on the football pitch during football-specific exercises (F-EX) and games (F-GAME). Knee joint moments were collected in the laboratory and grouped using hierarchical agglomerative clustering. The clusters were used to investigate the kinematics collected on field through wearable sensors. Three clusters emerged: Cluster 1 presented the lowest knee moments; Cluster 2 presented high knee extension but low knee abduction and rotation moments; Cluster 3 presented the highest knee abduction, extension, and external rotation moments. In F-EX, greater knee abduction angles were found in Cluster 2 and 3 compared to Cluster 1 (p = 0.007). Cluster 2 showed the lowest knee and hip flexion angles (p < 0.013). Cluster 3 showed the greatest hip external rotation angles (p = 0.006). In F-GAME, Cluster 3 presented the greatest knee external rotation and lowest knee flexion angles (p = 0.003). Clinically relevant differences towards ACL injury identified in the laboratory reflected at-risk patterns only in part when cutting on the field: in the field, low-risk players exhibited similar kinematic patterns as the high-risk players. Therefore, in-lab injury risk screening may lack ecological validity.

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Section: Discussionmentioning

confidence: 99%

Definition of High-Risk Motion Patterns for Female ACL Injury Based on Football-Specific Field Data: A Wearable Sensors Plus Data Mining Approach

Paolo

Nijmeijer

Bragonzoni

et al. 2023

Sensors

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“…Future study shall consider a well-designed experimental setup that match the real badminton court under training and match conditions. Second, only the discrete and key datapoints were applied to train and test the intelligent statistical models, without considering the time-varying features; thus, other machine learning algorithms, deep learning algorithms, and convolutional neural networks (such as long short-term memory, LSTM) may be utilized for the monitoring and prediction of loading accumulation ( Shao et al, 2022 ; Liew et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Intelligent prediction of lower extremity loadings during badminton lunge footwork in a lab-simulated court

Jiang

Mei

et al. 2023

Front. Bioeng. Biotechnol.

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Introduction: Playing badminton has been reported with extensive health benefits, while main injuries were documented in the lower extremity. This study was aimed to investigate and predict the knee- and ankle-joint loadings of athletes who play badminton, with “gold standard” facilities. The axial impact acceleration from wearables would be used to predict joint moments and contact forces during sub-maximal and maximal lunge footwork.Methods: A total of 25 badminton athletes participated in this study, following a previously established protocol of motion capture and musculoskeletal modelling techniques with the integration of a wearable inertial magnetic unit (IMU). We developed a principal component analysis (PCA) statistical model to extract features in the loading parameters and a multivariate partial least square regression (PLSR) machine learning model to correlate easily collected variables, such as the stance time, approaching velocity, and peak accelerations, with knee and ankle loading parameters (moments and contact forces).Results: The key variances of joint loadings were observed from statistical principal component analysis modelling. The promising accuracy of the partial least square regression model using input parameters was observed with a prediction accuracy of 94.52%, while further sensitivity analysis found a single variable from the ankle inertial magnetic unit that could predict an acceptable range (93%) of patterns and magnitudes of the knee and ankle loadings.Conclusion: The attachment of this single inertial magnetic unit sensor could be used to record and predict loading accumulation and distribution, and placement would exhibit less influence on the motions of the lower extremity. The intelligent prediction of loading patterns and accumulation could be integrated to design training and competition schemes in badminton or other court sports in a scientific manner, thus preventing fatigue, reducing loading-accumulation-related injury, and maximizing athletic performance.

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“…Long short-term memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to overcome the limitations of traditional RNNs in handling long-term dependencies in sequential data [ 36 ]. It has been used in a wide range of applications for time-series data classification and forecasting [ 37 , 38 , 39 , 40 ]. LSTMs are particularly useful in tasks that require modeling long-term dependencies in sequential data.…”

Section: Introductionmentioning

confidence: 99%

Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks

2023

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Activated channels of functional near-infrared spectroscopy are typically identified using the desired hemodynamic response function (dHRF) generated by a trial period. However, this approach is not possible for an unknown trial period. In this paper, an innovative method not using the dHRF is proposed, which extracts fluctuating signals during the resting state using maximal overlap discrete wavelet transform, identifies low-frequency wavelets corresponding to physiological noise, trains them using long-short term memory networks, and predicts/subtracts them during the task session. The motivation for prediction is to maintain the phase information of physiological noise at the start time of a task, which is possible because the signal is extended from the resting state to the task session. This technique decomposes the resting state data into nine wavelets and uses the fifth to ninth wavelets for learning and prediction. In the eighth wavelet, the prediction error difference between the with and without dHRF from the 15-s prediction window appeared to be the largest. Considering the difficulty in removing physiological noise when the activation period is near the physiological noise, the proposed method can be an alternative solution when the conventional method is not applicable. In passive brain-computer interfaces, estimating the brain signal starting time is necessary.

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Predicting Coordination Variability of Selected Lower Extremity Couplings during a Cutting Movement: An Investigation of Deep Neural Networks with the LSTM Structure

Cited by 4 publications

References 78 publications

Definition of High-Risk Motion Patterns for Female ACL Injury Based on Football-Specific Field Data: A Wearable Sensors Plus Data Mining Approach

Definition of High-Risk Motion Patterns for Female ACL Injury Based on Football-Specific Field Data: A Wearable Sensors Plus Data Mining Approach

Intelligent prediction of lower extremity loadings during badminton lunge footwork in a lab-simulated court

Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks

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