Identity Recognition by Walking Outdoors Using Multimodal Sensor Insoles

IEEE Access

Omisore Olatunji Mumini¹  Zhanyong Mei²  Ludwig Lubich³  Martin Penev³  Sau Van Nguyen Van⁴  Yan Yan⁵  Lei Wang⁵  Kamen Ivanov⁵ 

[1] CAS Key Laboratory for Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;College of Cyber Security, Chengdu University of Technology, Chengdu, China;Faculty of Telecommunications, Technical University of Sofia, Sofia, Bulgaria;Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China;Shenzhen Key Laboratory for Low-cost Healthcare, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;

关键词: Gait recognition;    smart insole;    plantar pressure;    wearable sensors;    sensor fusion;   

DOI  :  10.1109/ACCESS.2020.3016970

来源: DOAJ

【 摘 要 】

Recently, gait attracts attention as a practical biometric for devices that naturally possess walking pattern sensing. In the present study, we explored the feasibility of using a multimodal smart insole for identity recognition. We used sensor insoles designed and implemented by us to collect kinetic and kinematic data from 59 participants that walked outdoors. Then, we evaluated the performance of four neural network architectures, which are a baseline convolutional neural network (CNN), a CNN with a multi-stage feature extractor, a CNN with an extreme learning machine classifier using sensor-level fusion and CNN with extreme learning machine classifier using feature-level fusion. The networks were trained with segmented insole data using 0%, 50%, and 70% segmentation overlap, respectively. For 70% segmentation overlap and both-side data, we obtained mean accuracies of 72.8% ±0.038, 80.9% ±0.036, 80.1% ±0.021 and 93.3% ±0.009, for the four networks, respectively. The results suggest that multimodal sensor-enabled footwear could serve biometric purposes in the next generation of body sensor networks.

【 授权许可】

Unknown