EVAL Cane: Nonintrusive Monitoring Platform With a Novel Gait-Based User-Identification Scheme

• Published in: IEEE transactions on instrumentation and measurement 2021, Vol.70, p.1-15
• Main Authors: Xing, Yuexiu, Wang, Ting, Zhou, Fen, Hu, Aiqun, Li, Guyue, Peng, Linning
• Format: Article
• Language: English
• Subjects: Accelerometers; Algorithms; Authentication; Automatic; Computer Science; Cryptography and Security; Embedded Systems; Engineering Sciences; EVAL cane; Fall detection; Feature extraction; feature refinement; Gait; Gait recognition; Legged locomotion; Monitoring; monitoring system; Networking and Internet Architecture; Performance enhancement; Phase frequency detector; rough feature extraction; Security; user identification/authentication; walking gait
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2020.3009338

Considering the particularity of fragile people with reduced mobility, we design a nonintrusive 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 the user's long-term walking data in the background, which may be the 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 are 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 the 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%.