[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Abstract

This article analyzes the method of reading data from inertial sensors. We introduce how to create a 3D scene and a 3D human body model and use inertial sensors to drive the 3D human body model. We capture the movement of the lower limbs of the human body when a small number of inertial sensor nodes are used. This paper introduces the idea of residual error into the deep LSTM network to solve the problem of gradient disappearance and gradient explosion. The main problem to be solved by wearable inertial sensor continuous human motion recognition is the modeling of time series. This paper chooses the LSTM network which can handle time series as well as the main frame. In order to reduce the gradient disappearance and gradient explosion problems in the deep LSTM network, the structure of the deep LSTM network is adjusted based on the residual learning idea. In this paper, a data acquisition method using a single inertial sensor fixed on the bottom of a badminton racket is proposed, and a window segmentation method based on the combination of sliding window and action window in real-time motion data stream is proposed. We performed feature extraction on the intercepted motion data and performed dimensionality reduction. An improved Deep Residual LSTM model is designed to identify six common swing movements. The first-level recognition algorithm uses the C4.5 decision tree algorithm to recognize the athlete’s gripping style, and the second-level recognition algorithm uses the random forest algorithm to recognize the swing movement. Simulation experiments confirmed that the proposed improved Deep Residual LSTM algorithm has an accuracy of over 90.0% for the recognition of six common swing movements.

Dance somersault gesture recognition based on multi-scale depth feature fusion

“…Compared to other algorithms, this algorithm requires two measurement models, namely precision and memory. The precision rate is the accuracy of the segmentation of the video frame sequence and the retrieval rate is the percentage of all the latest levels on the manually defined level [19]. The effect of this algorithm is compared to other algorithms, as available in Table 2.…”

An Action Recognition Technology for Badminton Players Using Deep Learning

“…Action features are extracted from the skeleton motion trajectory and combined with other data to form an action recognition model to realize action recognition. In comparison to video-based action recognition methods, the human skeleton sequence data contains more accurate joint position information, making the action recognition approach based on human skeleton data more robust when dealing with perspective change, body proportion, and motion speed [11][12][13][14][15][16].…”

Retracted: Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

“…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:…”