Estimating Knee Joint Load Using Acoustic Emissions During Ambulation

Scherpereel, Keaton L.; Bolus, Nicholas B.; Jeong, Hyeon; Inan, Omer T.; Young, Aaron

doi:10.1007/s10439-020-02641-7

Cited by 18 publications

(6 citation statements)

References 46 publications

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“…Since direct ACL force measurement was not possible in this cohort (or almost any healthy cohort), computational modelling of the human neuromusculoskeletal system and internal tissue biomechanics is the best available method to quantify ACL load. Moreover, the neuromusculoskeletal modelling approach we have used has been independently recognised for its excellence in modelling internal biomechanics (see [ 61 ]), and the ACL model has been validated across a wide range (in magnitude and complexity) of loading configurations. Third, our cohort was comprised exclusively of females, and as such, we cannot say if the results extend to males.…”

Section: Discussionmentioning

confidence: 99%

Limiting the Use of Electromyography and Ground Reaction Force Data Changes the Magnitude and Ranking of Modelled Anterior Cruciate Ligament Forces

et al. 2023

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Neuromusculoskeletal models often require three-dimensional (3D) body motions, ground reaction forces (GRF), and electromyography (EMG) as input data. Acquiring these data in real-world settings is challenging, with barriers such as the cost of instruments, setup time, and operator skills to correctly acquire and interpret data. This study investigated the consequences of limiting EMG and GRF data on modelled anterior cruciate ligament (ACL) forces during a drop–land–jump task in late-/post-pubertal females. We compared ACL forces generated by a reference model (i.e., EMG-informed neural mode combined with 3D GRF) to those generated by an EMG-informed with only vertical GRF, static optimisation with 3D GRF, and static optimisation with only vertical GRF. Results indicated ACL force magnitude during landing (when ACL injury typically occurs) was significantly overestimated if only vertical GRF were used for either EMG-informed or static optimisation neural modes. If 3D GRF were used in combination with static optimisation, ACL force was marginally overestimated compared to the reference model. None of the alternative models maintained rank order of ACL loading magnitudes generated by the reference model. Finally, we observed substantial variability across the study sample in response to limiting EMG and GRF data, indicating need for methods incorporating subject-specific measures of muscle activation patterns and external loading when modelling ACL loading during dynamic motor tasks.

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Section: Discussionmentioning

confidence: 99%

Limiting the Use of Electromyography and Ground Reaction Force Data Changes the Magnitude and Ranking of Modelled Anterior Cruciate Ligament Forces

et al. 2023

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“…Four machine learning classification algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), were applied to determine fatigued state over a single bending/retraction cycle. As per the literature, these approaches are widely utilized and have demonstrated strong performance in the development of human-movementbased models [37,40,64]. Feature normalization through standardization was utilized in the implementation of the SVM and LR methods.…”

Section: Classification Algorithms and Model Developmentmentioning

confidence: 99%

“…In the context of predicting differences in physiological conditions, machine learning classification algorithms play a pivotal role [33,34]. These include (a) decision trees, which partition the feature space into distinct regions based on simple decision rules, are interpretable, and are suitable for tasks with categorical outcomes [35], (b) Support Vector Machines (SVM), which aim to find the hyperplane that best separates different classes while maximizing the margin between them, making them effective for binary classification tasks with high-dimensional feature spaces [36], and (c) ensemble methods such as Random Forests and Gradient Boosting, which combine multiple classifiers to improve predictive performance and robustness [37][38][39]. Previous studies have demonstrated the utility of machine learning techniques in detecting fatigue during tasks such as walking, thereby reducing the risk of injury due to overexertion.…”

Section: Introductionmentioning

confidence: 99%

Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach

Kuber,

Godbole,

Rashedi

2024

Applied Sciences

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Back-Support Industrial Exoskeletons (BSIEs) can be beneficial in reducing the risk of injury due to overexertion during trunk flexion tasks. Most real-world tasks include complex body movements, leading to mixed outcomes that necessitate field-based methods for detecting overall physical demands. Monitoring fatigue can be beneficial in this regard to ensure that benefits of BSIEs are translated to the real world. Our experiment included 14 participants, who performed 30 repetitions of 45° trunk-flexion while assisted by a BSIE, first without fatigue and then at medium-high back fatigue (7/10 in the Borg scale). We extracted 135 features from recorded muscle activity, trunk motion, and whole-body stability across bending, transition, and retraction portions of each trunk-flexion cycle. Four classification algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), were implemented to assess fatigue prediction. XGB (Accuracy: 86.1%, Recall: 86%, Specificity: 86.3%) was effective in classifying fatigue with data obtained from a single EMG sensor located on the lower back (erector spinae) muscle. Meanwhile, stability measures showed high predictability with both RF (92.9%, 91.9%, 94.1%) and XGB (93.5, 94.1%, 93.1%). Findings demonstrate the success of force plates, and when replaced by pressure insoles, they can facilitate real-world fatigue detection during BSIE-assisted trunk-flexion tasks.

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“…Decision trees incorporate a hierarchical tree form with structured nodes (root, decision, and leaf nodes) to categorize datapoints into subsets [12][13][14], while Support Vector Machines (SVM) aim to establish a boundary between predefined sets of datapoints [15][16][17]. Meanwhile, ensemble methods like Random Forests combine multiple decision trees to improve predictive performance and robustness [18][19][20]. Prior studies have implemented these algorithms for detecting fatigue.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors

Kuber,

Kulkarni,

Rashedi

2024

Applied Sciences

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Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent trunk-flexion task cycles consisting of static, sustained, and dynamic activities, until they reached medium-high fatigue levels, while wearing BSIEs. Three classification algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features from four wearable wireless Electromyography (EMG) sensors with integrated Inertial Measurement Units (IMUs). We examined the best grouping and sensor combinations by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IMU sensors) and 82% (single IMU sensor) accuracy, respectively. Tertiary classification for back and leg fatigue level prediction required four sensor setups with both EMG and IMU measures to perform at 79% and 67% accuracy, respectively. The efforts presented in our article demonstrate the feasibility of an accessible fatigue level detection system, which can be beneficial for objective fatigue assessment, design selection, and implementation of BSIEs in real-world scenarios.

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Estimating Knee Joint Load Using Acoustic Emissions During Ambulation

Cited by 18 publications

References 46 publications

Limiting the Use of Electromyography and Ground Reaction Force Data Changes the Magnitude and Ranking of Modelled Anterior Cruciate Ligament Forces

Limiting the Use of Electromyography and Ground Reaction Force Data Changes the Magnitude and Ranking of Modelled Anterior Cruciate Ligament Forces

Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach

Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors

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