Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review

doi:10.31661/jbpe.v0i0.2305-1621

Cited by 4 publications

(3 citation statements)

References 57 publications

(107 reference statements)

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“…Among the wide set of classification models explored, the SVM Fine Gaussian model showed the best accuracy for all the features. This is in line with the current literature, considering the SVM approach as a proper solution to dealing with high dimensionality problems with a high discriminative classification power in sport applications [ 33 , 34 ]. In terms of kinematic features, the mean kinematics in the first 0-20% of the stance phase showed the best accuracy in model training and testing.…”

Section: Discussionsupporting

confidence: 87%

Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I

Benjaminse,

Nijmeijer,

Gokeler

et al. 2024

Sensors

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Laboratory studies have limitations in screening for anterior cruciate ligament (ACL) injury risk due to their lack of ecological validity. Machine learning (ML) methods coupled with wearable sensors are state-of-art approaches for joint load estimation outside the laboratory in athletic tasks. The aim of this study was to investigate ML approaches in predicting knee joint loading during sport-specific agility tasks. We explored the possibility of predicting high and low knee abduction moments (KAMs) from kinematic data collected in a laboratory setting through wearable sensors and of predicting the actual KAM from kinematics. Xsens MVN Analyze and Vicon motion analysis, together with Bertec force plates, were used. Talented female football (soccer) players (n = 32, age 14.8 ± 1.0 y, height 167.9 ± 5.1 cm, mass 57.5 ± 8.0 kg) performed unanticipated sidestep cutting movements (number of trials analyzed = 1105). According to the findings of this technical note, classification models that aim to identify the players exhibiting high or low KAM are preferable to the ones that aim to predict the actual peak KAM magnitude. The possibility of classifying high versus low KAMs during agility with good approximation (AUC 0.81–0.85) represents a step towards testing in an ecologically valid environment.

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Section: Discussionsupporting

confidence: 87%

Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I

Benjaminse,

Nijmeijer,

Gokeler

et al. 2024

Sensors

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“…Consequently, models based on CP cannot be universally implemented across diverse sports ( Jones and Vanhatalo, 2017 ). To address the limitation of CP-based models, the integration of AI holds promise, as AI-powered algorithms can excel in analyzing extensive datasets, offering a more comprehensive and refined evaluation of athletes’ profiles for the application for the identification of doping suspicions in sports ( Chmait and Westerbeek, 2021 ; Molavian et al, 2023 ). Through the detection of irregular patterns, trends, and anomalies, AI systems can support stakeholders in pinpointing athletes engaged in prohibited practices, thus serving as an independent and valuable criterion for selecting individuals for doping tests.…”

Section: Discussionmentioning

confidence: 99%

Identification of doping suspicions through artificial intelligence-powered analysis on athlete’s performance passport in female weightlifting

Ryoo,

Cho,

et al. 2024

Front. Physiol.

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IntroductionDoping remains a persistent concern in sports, compromising fair competition. The Athlete Biological Passport (ABP) has been a standard anti-doping measure, but confounding factors challenge its effectiveness. Our study introduces an artificial intelligence-driven approach for identifying potential doping suspicious, utilizing the Athlete’s Performance Passport (APP), which integrates both demographic profiles and performance data, among elite female weightlifters.MethodsAnalyzing publicly available performance data in female weightlifting from 1998 to 2020, along with demographic information, encompassing 17,058 entities, we categorized weightlifters by age, body weight (BW) class, and performance levels. Documented anti-doping rule violations (ADRVs) cases were also retained. We employed AI-powered algorithms, including XGBoost, Multilayer Perceptron (MLP), and an Ensemble model, which integrates XGBoost and MLP, to identify doping suspicions based on the dataset we obtained.ResultsOur findings suggest a potential doping inclination in female weightlifters in their mid-twenties, and the sanctioned prevalence was the highest in the top 1% performance level and then decreased thereafter. Performance profiles and sanction trends across age groups and BW classes reveal consistently superior performances in sanctioned cases. The Ensemble model showcased impressive predictive performance, achieving a 53.8% prediction rate among the weightlifters sanctioned in the 2008, 2012, and 2016 Olympics. This demonstrated the practical application of the Athlete’s Performance Passport (APP) in identifying potential doping suspicions.DiscussionOur study pioneers an AI-driven APP approach in anti-doping, offering a proactive and efficient methodology. The APP, coupled with advanced AI algorithms, holds promise in revolutionizing the efficiency and objectivity of doping tests, providing a novel avenue for enhancing anti-doping measures in elite female weightlifting and potentially extending to diverse sports. We also address the limitation of a constrained set of APPs, advocating for the development of a more accessible and enriched APP system for robust anti-doping practices.

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“…The first is interpreting the captured data. For example, with machine learning models, the contribution of various joints involved in a movement during its successive phases can be decomposed [82][83][84]. The second is the improved precision that can be achieved by coupling an AI algorithm with a markerless motion capture system [85,86].…”

Section: Role Of Artificial Intelligencementioning

confidence: 99%

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery

Berhouet,

Samargandi

2024

Diagnostics

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In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These “markerless” systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.

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Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review

Cited by 4 publications

References 57 publications

Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I

Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I

Identification of doping suspicions through artificial intelligence-powered analysis on athlete’s performance passport in female weightlifting

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery

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