Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model

Abstract

Obesity and laziness are some of the common issues in the majority of the youth today. This has led to the development of a proposed exergaming solution where users can play first-person physical games. This research study not only proposes a solution for physical fitness in the form of a game using wearable sensors but also proposes a multi-purpose system that provides different applications when trained for the domain-specific dataset. Critical tasks of gesture recognition and depiction in virtual reality can be applied to many applications in the domains of crime detection, fitness, healthcare, online learning, and sports. In particular, the proposed system enables a user to perform, detect, and depict different gestures in the virtual reality game. First, the system pre-processes input data by applying a median filter to overcome the anomalies. Then, features are extracted through a convolutional neural network, power spectral density, skewness, and kurtosis methods. Further, the system optimizes different features by using the grey wolf optimization. Lastly, the feature set which is optimized is fed to a recurrent neural network for classification. When Compared to the traditional methods, the suggested system gives better results while being easier to use. The IMSporting behaviors (IMSB) dataset includes badminton and other physical activities, the WISDM dataset includes common locomotor motions, and the ERICA dataset which includes a variety of exercises, were used in the experimentation. According to experimental findings, the suggested approach outperformed current methods, which showed detection accuracies of 85.01%, 88.46%, and 93.18% over the IMSB, WISDM, and ERICA datasets, respectively.

A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

“…Table 7 compares the performance of different sports person health monitoring models based on various evaluation metrics such as accuracy, specificity, precision, and F1 score. Afsar [ 18 ] achieved an accuracy of 0.75, indicating the proportion of correctly classified instances out of the total. Patalas et al [ 19 ] attained a slightly improved accuracy of 0.8, suggesting a better classification performance.…”

“…Afsar [ 18 ] Achieved a specificity of 0.66, reflecting the ability to capture true negatives effectively. Patalas et al [ 19 ]: Improved the specificity to 0.73, suggesting a better performance in correctly identifying negatives.…”

“…Afsar [ 18 ] Achieved an F1 score of 0.8, the harmonic mean of precision and recall. Patalas et al [ 19 ] achieved an F1 score of 0.84, showcasing a balanced performance between precision and recall.…”

“…It then uses grey wolf optimization to optimize the feature set before feeding the optimized features to an RNN for classification. The system is evaluated using and performs well in a few cases [ 18 ].…”

The intelligent football players’ motion recognition system based on convolutional neural network and big data

“…The adoption of machine learning methodologies for motion recognition investigations has garnered substantial traction within the sports domain. Notably, algorithms have been effectively employed to accurately categorize commonplace activities like standing, walking, running, and reclining, thereby unveiling their distinctive patterns [ [7] , [8] , [9] ]. Similarly, deep learning has been applied to recognize human body movements within video sequences, detecting movement speed and direction through the amalgamation of electromyographic signals, acceleration signals, and video data.…”