Wearable Sport Activity Classification Based on Deep Convolutional Neural Network

This study presents a novel approach for sports activity detection using wearable devices without traditional sensors. Leveraging deep learning algorithms, we propose a non-invasive, sensor less methodology to classify various sports activities based solely on wearable data. Our model harnesses the power of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze raw motion data, extracting valuable features and patterns for accurate activity classification. Through extensive experimentation, we demonstrate the effectiveness of our approach, achieving high classification accuracy across a diverse range of sports activities. This sensorless, deep learning-based approach holds promise for enabling seamless and unobtrusive sports activity monitoring in real-world settings. Keywords: Sports activity dataset and deep learning algorithms