Comparison of Deep Learning Techniques on Human Activity Recognition using Ankle Inertial Signals
| Autor: | Nazari, Farhad, Nahavandi, Darius, Mohajer, Navid, Khosravi, Abbas |
|---|---|
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/SMC53654.2022.9945472 |
| Popis: | Human Activity Recognition (HAR) is one of the fundamental building blocks of human assistive devices like orthoses and exoskeletons. There are different approaches to HAR depending on the application. Numerous studies have been focused on improving them by optimising input data or classification algorithms. However, most of these studies have been focused on applications like security and monitoring, smart devices, the internet of things, etc. On the other hand, HAR can help adjust and control wearable assistive devices, yet there has not been enough research facilitating its implementation. In this study, we propose several models to predict four activities from inertial sensors located in the ankle area of a lower-leg assistive device user. This choice is because they do not need to be attached to the user's skin and can be directly implemented inside the control unit of the device. The proposed models are based on Artificial Neural Networks and could achieve up to 92.8% average classification accuracy Comment: This is the accepted version of an article published in the proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
| Databáze: | arXiv |
| Externí odkaz: |
|