Gym Exercise Recognition Using Deep Convolutional and LSTM Neural Network Based on IMU Sensor Data
Sakorn Mekruksavanich, Datchakorn Tancharoen, Anuchit Jitpattanakul · 2024
This study delves into the utilization of wearable devices equipped with inertial measurement units (IMUs) to gather data for human activity recognition. It explores the application of deep neural networks for automatically identifying gym workouts using IMU sensors. Our methodology involves developing a framework that integrates convolutional neural networks (CNNs) to extract features from raw sensor data, followed by using long short-term memory (LSTM) recurrent neural networks to classify sequences. IMU data from accelerometers and gyroscopes are collected from 10 individuals performing 30 standard gym routines. The CNN-LSTM pipeline is supervised on a comprehensive dataset comprising multiple sensors and subjects to distinguish between different workouts accurately. During the evaluation, the CNN-LSTM model achieved an accuracy of 93.81 % in categorizing 30 workout categories based solely on accelerometer data. Through augmentation, this accuracy is further improved to 95.75 %. This solution outperforms independently utilized CNN and LSTM models and traditional machine learning approaches. Detailed assessments offer valuable insights into the benefits of combining diverse sensor types and model architectures for robust exercise classification. This research raises a precise and dependable wearable system for identifying gym exercises using deep neural networks. The findings suggest promising avenues for future exploration in human activity recognition, mainly focusing on utilizing on-body sensors and deep learning to analyze more complex human movements.