A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries

Feely, Ciara; Caulfield, Brian; Lawlor, Aonghus; Smyth, Barry

doi:10.1007/978-3-030-86957-1_6

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…(4) The prediction of medical health can help improve physical fitness and reduce the risk of injury. For example, Feely et al (2021) proposed a CBR method to predict the injury risk of athletes through various training data of previous marathon runners and provide a feasible explanation, which can help runners understand the risk and provide them with ways to reduce this risk.…”

Section: Predictionmentioning

confidence: 99%

A review of the development and future challenges of case-based reasoning

Yan

Cheng

2023

Preprint

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Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence. In this article, the development of CBR is mainly reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were formed in solving problems by CBR are summarized. Moreover, the application fields of CBR are sorted. Finally, according to the idea of deep learning and interpretable artificial intelligence, the main challenges for the future development of CBR are proposed.

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Section: Predictionmentioning

confidence: 99%

A review of the development and future challenges of case-based reasoning

Yan

Cheng

2023

Preprint

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“…Example 9 (Prediction of Injuries) Given a set of observations and training data, the duration of recovery and the probability of injuries could be predicted (see the example in Table 15) [48]. In this example, we are able to predict potential issues of the current runner.…”

Section: Measures For Injury/illness Avoidancementioning

confidence: 99%

“…Having such indications helps to better to generate corresponding explanations making the current situation transparent. In contrast to a low training level, also a too intensive training could lead to injuries -for details see [48,73]. Arciniega-Rocha et al [8] present an approach to the application of case-based recommendation helping to avoid injuries in the workouts of amateur athletes.…”

Section: Measures For Injury/illness Avoidancementioning

confidence: 99%

Sports Recommender Systems: Overview and Research Issues

Felfernig,

Wundara,

Tran

et al. 2023

Preprint

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Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sport. These systems support people in sports, for example, by the recommendation of healthy and performance-boosting food items, the recommendation of training practices, talent and team recommendation, and the recommendation of specific tactics in competitions. With applications in the virtual world, for example, the recommendation of maps or opponents in e-sports, these systems already transcend conventional sports scenarios where physical presence is needed. On the basis of different working examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss open research issues.

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“…In all of these cases, RS techniques are used to identify the type and magnitude of intervention needed. In fact, early research has looked at injury mitigation using RS in recreational marathon runners [10].…”

Section: Claim: ML Models Are Black Boxesmentioning

confidence: 99%

Comment on "Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care"

Berndsen¹,

McHugh²

2022

Preprint

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We read with interest the article by Bullock et. al. [1] on the applicability of Machine Learning (ML) approaches in sports medicine settings. Overall, we agree with the central thrust of this paper-without sufficient interpretability, ML models are not useful in the sports injury domain. As the authors correctly state, black box models can cause problems. If we cannot establish how a model is making its decisions, and the predictive power is low, there is no value to using these models.However, we feel there are areas of research within ML that have been overlooked which strongly temper the conclusions that the authors make. In the following, we look at how these are being addressed by the ML community.

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A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries

Cited by 12 publications

References 27 publications

A review of the development and future challenges of case-based reasoning

A review of the development and future challenges of case-based reasoning

Sports Recommender Systems: Overview and Research Issues

Comment on "Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care"

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