The Overtraining Syndrome

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doi:10.1007/978-3-030-22357-1_21

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…However, one can fall into overtraining syndrome, which is experienced when a person trains disproportionally and ends up training in an inappropriate way compared to their capabilities and what their body can manage [6]. Additionally, such syndrome can occur when the equilibrium between the training load and the resting time is not respected and can lead to critical situations, both related to physical and mental health [7]. Amateur athletes are also more prone to develop such syndrome, as they do not benefit from coach feedback on the way they train [6].…”

Section: Introductionmentioning

confidence: 99%

Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

2020

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In a world where the data is a central piece, we provide a novel technique to design training plans for road cyclists. This study exposes an in-depth review of a virtual coach based on state-of-the-art artificial intelligence techniques to schedule road cycling training sessions. Together with a dozen of road cycling participants’ training data, we were able to create and verify an e-coach dedicated to any level of road cyclists. The system can provide near-human coaching advice on the training of cycling athletes based on their past capabilities. In this case study, we extend the tests of our empirical research project and analyze the results provided by experts. Results of the conducted experiments show that the computational intelligence of our system can compete with human coaches at training planification. In this case study, we evaluate the system we previously developed and provide new insights and paths of amelioration for systems based on artificial intelligence for athletes. We observe that our system performs equal or better than the control training plans in 14 and 24 week training periods where it was evaluated as better in 4 of our 5 test components. We also report a higher statistical difference in the results of the experts’ evaluations between the control and virtual coach training plan (24 weeks; training load: X2 = 4.751; resting time quantity: X2 = 3.040; resting time distance: X2 = 2.550; efficiency: X2 = 2.142).

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Section: Introductionmentioning

confidence: 99%

Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

2020

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“…Between the functions that intelligent systems must have are: Intelligence, Systematization, Objective, Sensory capacity, Conceptualization, Rules of action, Memory and Learning. These functions have allowed them to be adapted to several types of application, among which can be found: movement control in robots, character recognition systems, portfolio problem solving, optimization of computer networks, etcetera [8][9][10].…”

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Methodology to Implement a Fuzzy Controller through an Embedded Device

Ariza¹,

Jacinto²,

Martinez³

2018

IJET

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This paper makes a general description of the characteristics and properties of intelligent controllers, focusing on the design and implementation of a fuzzy controller for the control of a didactic level and flow plant (AMATROL T5552). The document shows the step by step development of the supported controller on the structuring and association of premises, in addition the structure and block diagram of the proposed solution is analyzed in detail, the analysis of the fuzzy rules used for the value control and the comparison of experimental results that validate the correct functioning of the proposed solution. Keyword-Fuzzy Controller, PSOC, Embedded Systems, Inference Method. I. INTRODUCTION The controllers allow to maintain or stabilize a system around a given value. This is achieved through various strategies, since there are different types of control, among which are: intelligent, conventional, modern, hybrids, among others. Between these trends, intelligent type controllers stand out, because they compensate the system in an adaptive manner and have the ability to control another system without the need for prior modeling [1-3]. The coupling between systems to be controlled and controllers carried out by means of techniques involving the development of models of the systems to be controlled, increase the steady-state error (difference between the expected value and the current value) when estimating an output value, due to the fact that designing the controller does not take into account all the physical interactions of the system to be controlled with its environment. As mentioned before, intelligent controllers partially solve this limitation by means of adaptive strategies that do not require prior knowledge of the system to be controlled. However, this issue is still under exploration because this type of controller is versatile enough to fit a totally unknown system [1-3]. The problem of controlling unknown systems has been addressed in different ways, including through the development of optimization algorithms, random search, bio-inspired, among others. In particular, through the use of fuzzy logic, controllers are designed based on the representation of the behavior of the different states of the system to be controlled. This technique gives controllers the ability to reason to solve problems, since they are constructed using sets of rules that regulate the behavior of the system to be treated [4-5]. These characteristics give fuzzy controllers an advantage over other intelligent controllers, because they emulate behavior similar to human reasoning. When inheriting this characteristic, the controller deduces and generates a logical action from a previous knowledge, which allows it to control one or more variables of the system of simultaneous way. However, one limitation of this technique is the large number of parameters that it may have, due to this they are usually implemented in centralized controllers that are mostly computers [5]. However, the development of technologies for the manufacture o...

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The Overtraining Syndrome

Cited by 2 publications

References 18 publications

Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study

Methodology to Implement a Fuzzy Controller through an Embedded Device

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