AIMC Topic: Athletic Injuries
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Explaining the differences of gait patterns between high and low-mileage runners with machine learning.
Scientific reports
Feb 22, 2022
Running gait patterns have implications for revealing the causes of injuries between higher-mileage runners and low-mileage runners. However, there is limited research on the possible relationships between running gait patterns and weekly running mil...
Long-Term Assessment of Rehabilitation Treatment of Sports through Artificial Intelligence Research.
Computational and mathematical methods in medicine
Dec 22, 2021
BACKGROUND: Artificial intelligence (AI) technology has been incorporated into all walks of life, especially the integration of machine learning and health management has achieved very significant progress and results. It is very necessary to analyze...
Application of CT Medical Imaging Combined with Deep Learning 3D Reconstruction in the Diagnosis and Rehabilitation of Anterior Cruciate Ligament Injury in Table Tennis Players.
Journal of healthcare engineering
Dec 17, 2021
Because of the intense competition, table tennis requires players to bear a strong physiological load, which increases the risk of sports injury. Anterior cruciate ligament (ACL) is an important structure of the knee joint to maintain forward stabili...
Validation of a Machine Learning Brain Electrical Activity-Based Index to Aid in Diagnosing Concussion Among Athletes.
JAMA network open
Feb 1, 2021
IMPORTANCE: An objective, reliable indicator of the presence and severity of concussive brain injury and of the readiness for the return to activity has the potential to reduce concussion-related disability.
The accuracy and predictability of micro Doppler radar signature projection algorithm measuring functional movement in NCAA athletes.
Gait & posture
Jan 26, 2021
BACKGROUND: Development of accessible cost-effective technology to objectively, reliably, and accurately predict musculoskeletal injury risk could aid the effort to prevent chronic pain and disability. Recent work on micro-Doppler radar suggests it m...
Can an inertial measurement unit (IMU) in combination with machine learning measure fast bowling speed and perceived intensity in cricket?
Journal of sports sciences
Jan 22, 2021
This study examined whether an inertial measurement unit (IMU), in combination with machine learning, could accurately predict two indirect measures of bowling intensity through ball release speed (BRS) and perceived intensity zone (PIZ). One IMU was...
A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes.
Sensors (Basel, Switzerland)
Nov 9, 2020
BACKGROUND: The running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running.
New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes.
International journal of sports medicine
Sep 13, 2020
The purpose of this article is to present how predictive machine learning methods can be utilized for detecting sport injury risk factors in a data-driven manner. The approach can be used for finding new hypotheses for risk factors and confirming the...
Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players.
Sensors (Basel, Switzerland)
May 27, 2020
Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still...
Using machine learning to improve our understanding of injury risk and prediction in elite male youth football players.
Journal of science and medicine in sport
May 18, 2020
OBJECTIVES: The purpose of this study was to examine whether the use of machine learning improved the ability of a neuromuscular screen to identify injury risk factors in elite male youth football players.
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