Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs

Abstract

To maintain and improve an amateur athlete’s fitness throughout training and to achieve peak performance in sports events, good nutrition and physical activity (general and training specifically) must be considered as important factors. In our context, the terminology “amateur athletes” represents those who want to practice sports to protect their health from sickness and diseases and improve their ability to join amateur athlete events (e.g., marathons). Unlike professional athletes with personal trainer support, amateur athletes mostly rely on their experience and feeling. Hence, amateur athletes need another way to be supported in monitoring and recommending more efficient execution of their activities. One of the solutions to (self-)coaching amateur athletes is collecting lifelog data (i.e., daily data captured from different sources around a person) to understand how daily nutrition and physical activities can impact their exercise outcomes. Unfortunately, not all factors of the lifelog data can contribute to understanding the mutual impact of nutrition, physical activities, and exercise frequency on improving endurance, stamina, and weight loss. Hence, there is no guarantee that analyzing all data collected from people can produce good insights towards having a good model to predict what the outcome will be. Besides, analyzing a rich and complicated dataset can consume vast resources (e.g., computational complexity, hardware, bandwidth), and this therefore does not suit deployment on IoT or personal devices. To meet this challenge, we propose a new method to (i) discover the optimal lifelog data that significantly reflect the relation between nutrition and physical activities and training performance and (ii) construct an adaptive model that can predict the performance for both large-scale and individual groups. Our suggested method produces positive results with low MAE and MSE metrics when tested on large-scale and individual datasets and also discovers exciting patterns and correlations among data factors.

A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization

“…However, conventional analyses of athlete characteristics often limit themselves to a narrow spectrum of dimensions. Such approaches frequently fall short of capturing the intricate and diverse characteristics of sports behavior [ [9] , [10] , [11] ]. Athletes exhibit an extensive array of characteristic data during competitions and training, including technical data, biomechanical parameters, and psychological responses, among others.…”

Acupuntura No Esporte

“…Para auxiliar na performance esportiva, algumas estratégias devem ser adotadas para que o atleta se mantenha saudável e apto a atingir seus objetivos, como por exemplo uma alimentação adequada, pois ela fornece os nutrientes necessários para que o corpo mantenha seu nível de energia e a função metabólica (25) , e dessa forma adaptações de treinamento e recuperação, incluindo sono, podem ser otimizadas por práticas de nutrição apropriadas, uma vez que existem inúmeros nutrientes que se mostram promissores em relação à promoção do sono e à recuperação do atleta (26) . Outra estratégia importante é a prática de exercícios de força, que ajudam a melhorar o equilíbrio muscular, aumentar a resistência e prevenir lesões (27) .…”