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

Sevda Özge Bursa, Özlem Durmaz Incel, Gülfem Isiklar Alptekin

Abstract: Highlights•We use transfer learning (TL) to build personalized models for Human Activity Recognition (HAR).•We compress DL models using quantization to be deployed on mobile devices with limited resources.•We examine the impact of different architectures and layers to fine-tune on the performance of TL.•TL is compared with a general and user-specific model.•Using TL results in higher F1 scores and less running time.•Even if new labels are introduced, TL performs better, with similar positions and classes.•When the models are compressed, the inference time reduces with no compromise in accuracy.