Smartphone Recommendation System to Prevent Potential Injuries in Young Athletes

Matos, Paulo; Rocha, João; Santos, Filipe; Marreiros, Goreti; Mota, Daniel; Fonseca, Nuno Gil; Martins, Constantino

doi:10.1007/978-3-030-31635-8_173

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…The AI module keeps track of the athlete or the trainee in general and extracts activities from the sensed data. With the help of a recommender model, we can tailor each training routine for each player to avoid injury [ 96 ]. We can also use cameras to track the athlete’s movements [ 63 ] so that we can correct mistakes in the exercises.…”

Section: Discussionmentioning

confidence: 99%

Digital Twin Coaching for Physical Activities: A Survey

Díaz

Ding

et al. 2020

Sensors

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Digital Twin technology has been rising in popularity thanks to the popularity of machine learning in the last decade. As the life expectancy of people around the world is increasing, so is the focus on physical activity to remain healthy especially in the current times where people are staying sedentary while in quarantine. This article aims to provide a survey on the field of Digital Twin technology focusing on machine learning and coaching techniques as they have not been explored yet. We also define what Digital Twin Coaching is and categorize the work done so far in terms of the objective of the physical activity. We also list common Digital Twin Coaching characteristics found in the articles reviewed in terms of concepts such as interactivity, privacy and security and also detail future perspectives in multimodal interaction and standardization, to name a few, that can guide researchers if they choose to work in this field. Finally, we provide a diagram for the Digital Twin Ecosystem showing the interaction between relevant entities and the information flow as well as an idealization of an ideal Digital Twin Ecosystem for team sports’ athlete tracking.

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Section: Discussionmentioning

confidence: 99%

Digital Twin Coaching for Physical Activities: A Survey

Díaz

Ding

et al. 2020

Sensors

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“…As reported in Emrich et al [43], content-based recommendation is also applied to support the recommendation of "health-aware" running or biking routes by matching a persons preferences (e.g., in termos of locations) but also the physical condition with route properties (e.g., "hilliness"). Similar to the recommendation of training sessions taking into account specific constraints such as current physical condition [105], constraint-based recommendation can also be used to avoid injuries caused, for example, by too intensive training sessions [87]. Finally, Yang et al [136] present an approach to depression prediction combined with a constraint-based recommendation of emotional improvement suggestions guiding a users behavior.…”

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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“…On the other hand, smartphone use can also improve athletic performance and general life. Smartphone apps are actively used to measure neuromuscular performance, assess vital signals, prevent injury, and improve skills ( Kidman et al, 2016 ; Driller et al, 2017 ; Perrotta et al, 2017 ; Matos et al, 2019 ). Using these apps may improve the competition process and the quality of training in athletes.…”

Section: Introductionmentioning

confidence: 99%

“… Perrotta et al (2017) estimated the immediate heart-rate variability in athletes using smartphone apps. Matos et al (2019) suggested that a smartphone recommendation system could prevent potential risks of injury for athletes. Kidman et al (2016) found that wearing a device with inertial-motion tracking and vibro-tactile feedback increased the accuracy of diving movements in athletes.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Smartphone App-Use Patterns on the Performance of Professional Golfers

et al. 2021

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Smartphone app-use patterns will predict professional golfers’ athletic performance, and the use time of serious apps would be associated with improved performance. This longitudinal 4-week observation of 79 professional golfers assessed golf handicaps and smartphone app-use patterns at the start of the Korean professional golf season and 2 and 4 weeks later. We classified use as social networking, entertainment, serious apps, and others. Use time of entertainment apps increased for non-improved golfers but did not change for improved golfers. Use time of serious apps increased for improved golfers and decreased for non-improved ones. Changes in golf handicaps were positively correlated with changes in entertainment app use time and negatively correlated with changes in serious app use time. Professional golfers’ sports performance was not associated with smartphone use time but was with the smartphone app type. The management of smartphone app-use patterns is important for professional golfers’ performance.

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Smartphone Recommendation System to Prevent Potential Injuries in Young Athletes

Cited by 5 publications

References 4 publications

Digital Twin Coaching for Physical Activities: A Survey

Digital Twin Coaching for Physical Activities: A Survey

Sports Recommender Systems: Overview and Research Issues

The Effect of Smartphone App-Use Patterns on the Performance of Professional Golfers

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