Computer aid assessment of muscular imbalance for preventing overuse injuries in athletes

Weerasinghe, Maheshya; Dias, G. K. A.; Dharmaratne, Anuja; Sandaruwan, Damitha; Nisansala, Aruni; Keppitiyagama, Chamath; Kodikara, Nihal

doi:10.1145/3018009.3018023

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Due to the specific and differentiated demands of sports (e.g. handball, volleyball, basketball, tennis, badminton) limb asymmetries can be stimulated [30] an adaptation of the body following the movements that an athlete performs in regularly and unfortunately this can cause muscle imbalances that could affect performance or predispose the athlete to injury [31].…”

Section: Material-methodsmentioning

confidence: 99%

Prevention and outcome measurement strategies to reduce the future impact of back pain in handball

Cazac

2023

SAM

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From a physical therapist's perspective, one of the best ways to prevent back pain is to try to identify variables determined to be predisposing factors and to control certain somatic-related factors in junior players. To take steps in this direction, in close correlation with theoretical arguments, we proposed as prevention tools some specific outcome measurement strategies to control and verify over time certain factors that predispose to back pain episodes, such as low levels of the trunk muscular endurance and coordination or lateral muscular asymmetries. The existence of muscle asymmetries was identified by bioimpedance performed with the Tanita MC 780 MA analyzer and trunk muscle endurance and coordination levels were checked with the McGill trunk muscle endurance test battery. Junior players showed poor trunk muscle strength and an imbalance between the three main muscle groups and some lateral muscle mass asymmetries between dominant and non-dominant body parts. Approaching and presenting this strategic vision for the prevention of back pain in handball players can provide professionals in the field with information to guide, evaluate, verify and implement a future individualized prevention program.

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Section: Material-methodsmentioning

confidence: 99%

Prevention and outcome measurement strategies to reduce the future impact of back pain in handball

Cazac

2023

SAM

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“…To understand logistic regression, it is important to understand the sigmoid function. The sigmoid function can take any real input, whereas the output always takes values between 0 and 1 [68]. Given the parameter vector β = (β 0 , β 1 , ..., β m ) T and an input set of features…”

Section: Athlete Health Prediction Using Logistic Regressionmentioning

confidence: 99%

Athlete Health Prediction Using Machine Learning Methods

Sharif¹

2023

Preprint

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Due to an increase in sports activities, the prediction of athletes’ health (AH) has recently become an important research topic. However, it is a challenging task to predict AH because of the nature of the data and the limitations of predictive models. The main objective of this work is to develop appropriate models that can forecast AH using historical data. This work will enable sport organizations to monitor the well-being of their athletes. In this thesis, we explore the applicability of various machine learning (ML) methods for predicting AH. Traditional ML methods do not perform well for class-imbalanced data as these methods are biased towards the majority class. In this work, we propose to use ensemble-based methods which utilize downsampling, bootstrap sampling, and boosting techniques to improve the classification performance. Various metrics are used to evaluate and to compare the model performance. Our results show the superiority of ensemble-based methods over traditional approaches. The random forest and the RUSBoost classier models are in particular found to produce the best performance in handling imbalanced classes.

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“…Through human gait analysis, kinetic and kinematic parameters can be determined, and the gait phase can be detected. Gait analysis results have been used to evaluate athlete performance [1], [2], monitor patient progress in rehabilitation processes [3]- [6], and to diagnose gait pathologies [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Wearable Gait Monitoring System Using Wireless Inertial Sensors

et al. 2023

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This paper presents a wearable virtual reality system with a wireless network of inertial sensors for lower limb monitoring. The system comprises seven sensor nodes sending data wirelessly to a master node. The information is then collected, organized, and sent to a screening device via a serial interface. An application executed either on a smartphone or a personal computer features an avatar which represents the received data and mimics the sensed movements of the patient, providing online feedback during and after the execution of a therapy. The data resulting from the therapy execution can be uploaded to a web server to facilitate the assessment and decision-making by health professionals. A pendulum featuring a rotary optical encoder is used for sensor functional behavior validation. In addition, the orientation angles measured by the proposed system are compared with respect to measurements from the motion analysis software Kinovea. The delay between the patient's body movement and the avatar is 33 ms, which is acceptable for visual feedback. This system is portable, inexpensive and enables a patient to complete physical therapy sessions at home or anywhere, with the advantage of enabling visual feedback through an avatar during rehabilitation therapy and allowing the reproduction of a therapy session for further analysis.

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Computer aid assessment of muscular imbalance for preventing overuse injuries in athletes

Cited by 3 publications

References 2 publications

Prevention and outcome measurement strategies to reduce the future impact of back pain in handball

Prevention and outcome measurement strategies to reduce the future impact of back pain in handball

Athlete Health Prediction Using Machine Learning Methods

Smartphone-Based Wearable Gait Monitoring System Using Wireless Inertial Sensors

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