Current sensor-based human activity recognition techniques that rely on a user-independent model struggle to generalize to new users and on to changes that a person may make over time to his or her way of carrying out activities. Incremental learning is a technique that allows to obtain personalized models which may improve the performance on the classifiers thanks to a continuous learning based on user data. Finally, deep learning techniques have been proven to be more effective with respect to traditional ones in the generation of user-independent models. The aim of our work is therefore to put together deep learning techniques with incremental learning in order to obtain personalized models that perform better with respect to user-independent model and personalized model obtained using traditional machine learning techniques. The experimentation was done by comparing the results obtained by a technique in the state of the art with those obtained by two neural networks (ResNet and a simplified CNN) on three datasets. The experimentation showed that neural networks adapt faster to a new user than the baseline.

Amrani, H., Micucci, D., Napoletano, P. (2021). Personalized Models in Human Activity Recognition Using Deep Learning. In Proceedings of the International Conference on Pattern Recognition (ICPR) (pp.9682-9688). IEEE [10.1109/ICPR48806.2021.9412140].

Personalized Models in Human Activity Recognition Using Deep Learning

Amrani Hamza;Micucci Daniela;Napoletano Paolo
2021

Abstract

Current sensor-based human activity recognition techniques that rely on a user-independent model struggle to generalize to new users and on to changes that a person may make over time to his or her way of carrying out activities. Incremental learning is a technique that allows to obtain personalized models which may improve the performance on the classifiers thanks to a continuous learning based on user data. Finally, deep learning techniques have been proven to be more effective with respect to traditional ones in the generation of user-independent models. The aim of our work is therefore to put together deep learning techniques with incremental learning in order to obtain personalized models that perform better with respect to user-independent model and personalized model obtained using traditional machine learning techniques. The experimentation was done by comparing the results obtained by a technique in the state of the art with those obtained by two neural networks (ResNet and a simplified CNN) on three datasets. The experimentation showed that neural networks adapt faster to a new user than the baseline.
poster
Deep learning, Human activity recognition, Personalization
English
International Conference on Pattern Recognition (ICPR)
2021
Proceedings of the International Conference on Pattern Recognition (ICPR)
978-1-7281-8808-9
2021
9682
9688
none
Amrani, H., Micucci, D., Napoletano, P. (2021). Personalized Models in Human Activity Recognition Using Deep Learning. In Proceedings of the International Conference on Pattern Recognition (ICPR) (pp.9682-9688). IEEE [10.1109/ICPR48806.2021.9412140].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/304212
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