EVAL cane: Non-intrusive monitoring platform with a novel gait-based user identification scheme
| Autor: | Xing, Yuexiu, Wang, Ting, Zhou, Fen, Hu, Aiqun, Li, Guyue, Peng, Linning |
|---|---|
| Přispěvatelé: | Southeast University [Jiangsu], Institut Supérieur d'Electronique de Paris (ISEP), Université Gustave Eiffel, ESIEE Paris, Département Ingénierie des Systèmes., Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, ZHOU, FEN |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI]
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO] rough feature extraction EVAL cane [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR] [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing walking gait [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Index Terms-Monitoring system feature refinement [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR] user identi- fication/authentication |
| Zdroj: | IEEE Transactions on Instrumentation and Measurement IEEE Transactions on Instrumentation and Measurement, Institute of Electrical and Electronics Engineers, In press |
| ISSN: | 0018-9456 |
| Popis: | International audience; Considering the particularity of fragile people with reduced mobility, we design a non-intrusive platform called EVAL cane to assist and monitor the user's walking. On the one hand, it has important walking assistance functions, such as obstacle warning and fall detection. On the other hand, it collects user's long-term walking data in the background, which may be potential physical health assessment data. Compared with an ordinary cane, the above two functions are implemented without any burden on the user's life. In addition, we have considered the authentication security for that the walking data is private. To this end, we propose a novel user identification scheme leveraging the user walking gait data collected by EVAL cane. To the best of our knowledge, it is the first time that the gait information collected by cane is used for reinforcing monitoring system security. This scheme does not require the user to remember and enter any identity (such as a password), which is user-friendly for fragile people. In the scheme, a statistics-based rough gait feature extraction method is put forward at first. Then, in order to improve the identification precision, we design a performance-based feature deletion (PFD) algorithm to remove bad features. Finally, a minimum Mahalanobis distance classifier is used. Experimental results show that the user identification rate without the PFD algorithm can reach as high as 90.48%. In addition, the PFD algorithm further improves the performance by about 6%, reaching an excellent result of 96.43%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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