Stack LSTM-Based User Identification Using Smart Shoes with Accelerometer Data

Sensors

Gu-Min Jeong¹  Do-Yun Kim¹  Seung-Hyeon Lee¹ 

[1] School of Electrical Engineering, Kookmin University, 77 Jeongnung-ro, Seongbuk-gu, Seoul 136-702, Korea;

关键词: stack LSTM;    user identification;    smart shoes;   

DOI  :  10.3390/s21238129

来源: DOAJ

【 摘 要 】

In this study, we propose a long short-term memory (LSTM)-based user identification method using accelerometer data from smart shoes. In general, for the user identification with human walking data, we require a pre-processing stage in order to divide human walking data into individual steps. Next, user identification can be made with divided step data. In these approaches, when there exist partial data that cannot complete a single step, it is difficult to apply those data to the classification. Considering these facts, in this study, we present a stack LSTM-based user identification method for smart-shoes data. Rather than using a complicated analysis method, we designed an LSTM network for user identification with accelerometer data of smart shoes. In order to learn partial data, the LSTM network was trained using walking data with random sizes and random locations. Then, the identification can be made without any additional analysis such as step division. In the experiments, user walking data with 10 m were used. The experimental results show that the average recognition rate was about 93.41%, 97.19%, and 98.26% by using walking data of 2.6, 3.9, and 5.2 s, respectively. With the experimental results, we show that the proposed method can classify users effectively.

【 授权许可】

Unknown