Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

Abstract

The healthcare benefits associated with regular physical activity recognition and monitoring have been considered in several research studies. Regular recognition and monitoring of health status can potentially assist in managing and reducing the risk of many diseases such as cardiovascular disease, diabetes, and obesity. Using healthcare equipment in hospitals, people can conduct regular physical examinations to check their health status. However, most of the time, it is difficult to reach a specific medical environment and use special medical equipment. In this paper, a deep learning framework based on the bidirectional gated recurrent unit for health status recognition is implemented to improve the accuracy by making full use of the information provided by smartphone acceleration sensors. A model based on a bidirectional gated recurrent unit is constructed to describe the relationship between input acceleration signals and output information through a gating approach. Therefore, it can automatically detect the health status of the sportsman as healthy, subhealthy, and unhealthy. Finally, the practical data collected from an athlete have been used to evaluate the recognition performance of the system. Results show that the proposed methodology can predicate the sports health status accurately.

Detection and recognition based on machine learning and deep learning for Morse signal in wide-band wireless spectrum

“…Table 1 Gated recurrent unit (GRU) is a variant of the LSTM network. GRU merges the LSTM input gate and the forget gate into the update gate and merges the data unit state and the hidden layer state [31], which realizes the simplification of the network structure. Like the LSTM network, GRU can capture the preceding information quickly, process variable-length sequences and significantly suppress diffusion and explosion of gradients.…”

Retracted: Health Recognition Algorithm for Sports Training Based on Bi‐GRU Neural Networks

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ].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:…”