Hybrid CNN-GRU Model for Exercise Classification Using IMU Time-Series Data
Jing Zhang, Meng Cheng Lau, Ziping Zhu · Journal of Machine Intelligence and Data Science · 2024
We introduce a hybrid CNN-GRU model in this study to classify exercises using IMU time-series data, with a focus on jumping jacks, lunges, and squats.By combining Convolutional Neural Networks with Gated Recurrent Units, our model effectively manages the high dimensionality and variable sampling rates of IMU data.We employed data normalisation and augmentation techniques to refine the dataset.Our model showed high accuracy in classifying types of exercises, highlighting its potential in motion classification and fitnesstracking applications.These results emphasise the value of hybrid deep learning methods in analysing complex time-series data and make a significant contribution to the understanding of human exercise movement patterns.