Design of the Exercise Load Data Monitoring System for Exercise Training Based on the Neural Network

Abstract

In order to monitor the sports load data of athletes in sports training, this paper studies the methods and systems of sports load monitoring and fatigue warning based on neural network technology. In this paper, the neural network parallel optimization algorithm based on big data is used to accurately estimate the motion load and intensity according to the determined motion mode and acceleration data, so as to realize the real-time monitoring of the exercise training. The results show that the value of η is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, 176 samples were extracted from the monitoring data collected by the “National Tennis Team Information Platform,” 160 of which were selected as training samples and the other 16 as test samples. Ant colony size M = 20. The minimum value Wmin of the weight interval is −2, and the maximum value Wmax is 2. The maximum number of iterations is set to 200. σ = 1; that is, only one optimal solution is retained. The domain is divided into 60 parts evenly; that is, r = 60. Generally, η can be taken as any number [28] between [10-3, 10], but the value is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, the value is 0.003. In the early warning stage of exercise fatigue, reasonable measurement units of exercise fatigue time were divided according to the characteristics of different exercise items. It is proved that the Bayesian classification algorithm can effectively avoid the sports injury caused by overtraining by warning the fatigue and preventing the sports injury caused by overtraining.

Entwicklung einer KI-gestützten Bewegungstherapie bei onkologischen Palliativpatienten

“…An diesem Punkt kann der Einsatz von KI eine Verbesserung der aktuellen Versorgung bedeuten. Eine genaue Klassifizierung der sportlichen Leistung und der daraus resultierenden Erschöpfung und Verletzungsanfälligkeit kann durch den Einsatz KI vorausgesagt und besser gesteuert werden 14 15 16 17 . Die verbesserte Evaluierung der Trainingsleistung wurde jedoch nur bei professionellen Athleten dargestellt.…”

“…Jedoch gilt es, insbesondere bei Palliativpatienten, eine Überlastung zu vermeiden. Der Einsatz von KI kann hierbei hilfreich sein 14 . Das Ziel dieser Pilotstudie war daher die Analyse der Machbarkeit einer Unterstützung der Trainingssteuerung durch KI bei Palliativpatienten.…”

Retracted: Design of the Exercise Load Data Monitoring System for Exercise Training Based on the Neural Network

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”