Browse > Article
http://dx.doi.org/10.21289/KSIC.2019.22.4.457

Gate Data Gathering in WiFi-embedded Smart Shoes with Gyro and Acceleration Sensor  

Jeong, KiMin (Dept. of Control & Instrumentation Engineering, Pukyong National University)
Lee, Kyung-chang (Dept. of Control & Instrumentation Engineering, Pukyong National University)
Publication Information
Journal of the Korean Society of Industry Convergence / v.22, no.4, 2019 , pp. 459-465 More about this Journal
Abstract
There is an increasing interest in health and research on methods for measuring human body information. The importance of continuously observing information such as the step change and the walking speed is increasing. At a person's gait, information about the disease and the currently weakened area can be known. In this paper, gait is measured using wearable walking module built in shoes. We want to make continuous measurement possible by simplifying gait measurement method. This module is designed to receive information of gyro sensor and acceleration sensor. The designed module is capable of WiFi communication and the collected walking information is stored in the server. The information stored in the server is corrected by integrating the acceleration sensor and the gyro sensor value. A band-pass filter was used to reduce the error. This data is categorized by the Gait Finder into walking and waiting states. When walking, each step is divided and stored separately for analysis.
Keywords
Gait Recognition; Multi-sensor Fusion; Wearable Sensor; Gait Identification; Inertial Sensor;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. Guo, D. Wu, G. Liu, and G. Zhao, B. Huang, and L. Wang, "A Low-Cost Body Inertial-Sensing Network for Practical Gait Discrimination of Hemiplegia Patients", Telemedicine and e-Health, vol. 18, no. 10, pp. 748-754, (2012).   DOI
2 J. Taborri, E. Palemo, S. Rossi, and P. Cappa, "Gait Partitioning Methods: A Systematic Review", Sensors, vol. 16, no. 1, pp. 1-20, (2016).   DOI
3 Y. Han, F. Yi, C. Jiang, K. Dai, Y. Xu, X. Wang, and Z. You, "Self-Powered Gait Pattern-Based Identity Recognition by a Soft and Stretchable Triboelectric Band", Nano Energy, vol. 56, pp. 516-523, (2019).   DOI
4 J. Qi, P. Yang, D. Fan, and Z. Deng, "A Survey of Physical Activity Monitoring and Assessment using Internet of Things Technology," 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 2353-2358, (2015).
5 S. K. Dash, S. Mohapatra, and P. K, Pattnaik, "A Survey on Applications of Wireless Sensor Network using Cloud Computing", International Journal of Computer Science & Emerging Technologies, vol. 1, no. 4, pp. 50-55, (2010).
6 D. Rosenbaum, "Foot Loading Patterns Can be Changed by Deliberately Walking with In-Toeing or Out-Toeing Gait Modifications", Gait & Posture, vol. 38, no. 4, pp. 1067-1069, (2013).   DOI
7 G. Cola, M. Avvenuti, A. Vecchio, G. Z. Yang, and B. Lo, "An On-Node Processing Approach for Anomaly Detection in Gait", IEEE Sensors Journal, vol. 15, no. 11, pp. 6640-6649, (2015).   DOI
8 S. Chung, J. Lim, K. J. Noh, G. Kim, and H. Jeong, "Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning", Sensors, vol. 19, no. 7, pp. 1-20, (2019).   DOI
9 K. M. Jeong, H. H. Kim, K. C. Lee, "Implementation of Gait Pattern Monitoring System Using WiFi-Embedded Smart Shoes", 2019 PRESM International Symposium on Precision Engineering and Sustainable Manufacturing, pp. 108, (2019).