Personalized and motion-based human activity recognition with transfer learning and compressed deep learning models

Human activity recognition (HAR) enables the recognition of the activities of daily living using signals from motion sensors integrated into mobile and wearable devices. One of the challenges is the uniqueness of each individual with his/her different characteristics. A general model trained without user data may perform poorly on specific users. Another challenge is running deep learning (DL) models on mobile and wearable devices due to their limited resources. In this paper, to cope with these two challenges, we use transfer learning to build personalized models and model compression for running DL algorithms. We examine the impact of different DL architectures, the number of layers to be fine-tuned, the amount of user training data, and the transfer to new datasets on the performance of HAR. We compare the performance of the transferred models with general and user-specific models in terms of F1 score, training and inference time.