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JRM Vol.32 No.5 pp. 947-957
doi: 10.20965/jrm.2020.p0947
(2020)

Paper:

Study on Human Behavior Classification by Using High-Performance Shoes Equipped with Pneumatic Actuators

Yasuhiro Hayakawa*, Yuta Kimata**, and Keisuke Kida**

*Department of Control Engineering, National Institute of Technology, Nara College
22 Yata-cho, Yamatokoriyama, Nara 639-1058, Japan

**Advanced Mechanical Engineering Course, National Institute of Technology, Nara College
22 Yata-cho, Yamatokoriyama, Nara 639-1058, Japan

Received:
March 30, 2020
Accepted:
June 29, 2020
Published:
October 20, 2020
Keywords:
pneumatics, supervised machine-learning classifiers, rehabilitation, walking training, shoes
Abstract
The shoes and dedicated insole elements

The shoes and dedicated insole elements

In Japan, accidents involving the falling of elderly people are increasingly becoming a problem. To solve this problem, walking training is effective for preventing falls of elderly people. In this study, a walking training system was developed in which high-performance shoes are used to improve the efficiency of walking training. The high-performance shoes have three functions: 1) measurement of plantar pressure using changes in the inner pressure of the insole, 2) leg movement measurement using a six-axis motion sensor, and 3) applying stimulus to the sole of the foot by changing the shape of the insole. A unique rubber element was developed for these functions. Furthermore, a system to predict the behavior of patients during walking training was developed. Based on experimental results, four types of behavior of patients during walking training were predicted. Moreover, leave-one-person-out cross validation was performed by the random forest (RF) machine-learning algorithm, and the F-measure was calculated. As a result, the four types of behavior were classified with an F-measure of 78.6%.

Cite this article as:
Y. Hayakawa, Y. Kimata, and K. Kida, “Study on Human Behavior Classification by Using High-Performance Shoes Equipped with Pneumatic Actuators,” J. Robot. Mechatron., Vol.32 No.5, pp. 947-957, 2020.
Data files:
References
  1. [1] Cabinet Office, “Situation of the Ageing Population,” Annual Report on the Ageing Society, pp. 3-9, 2019.
  2. [2] Tokyo Fire Department, “Accidents by type,” Actual Situation of Daily Life Accidents From Emergency Transport Data, pp. 21-37, 2019.
  3. [3] M. F. Antwi-Afari, H. Li, J. Seo, and A. Y. L. Wong, “Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers,” Automation in Construction, Vol.96, pp. 189-199, 2018.
  4. [4] S. Lee, S. T. Cho, and S. Choi, “Classification of Gait Type Based on Deep Learning Using Various Sensors with Smart Insole,” Sensors, Vol.19, No.8, 1757, 2019.
  5. [5] A. B. Putti, G. P. Arnold, L. Cochrane, and R. J. Abboud, “The Pedar® in-shoe system: Repeatability and normal pressure values,” Gait & Posture, Vol.25, pp. 401-405, 2007.
  6. [6] S. W. Park, P. S. Das, and J. Y. Park, “Development of wearable and flexible insole type capacitive pressure sensor for continuous gait signal analysis,” Organic Electronics, Vol.53, pp. 213-220, 2018.
  7. [7] Q. Zhang, Y. L. Wang, Y. Xia, X. Wu, T. V. Kirk, and X. D. Chen, “A low-cost and highly integrated sensing insole for plantar pressure measurement,” Sensing and Bio-Sensing Research, Vol.26, 100298, 2019.
  8. [8] I. P. I. Pappas, M. R. Popovic, T. Keller, V. Dietz, and M. Morari, “A Reliable Gait Phase Detection System,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol.9, pp. 113-125, 2001.
  9. [9] P. H. Truong, J. Lee, A. Kwon, and G. Jeong, “Stride Counting in Human Walking and Walking Distance Estimation Using Insole Sensors,” Sensors, Vol.16, No.6, 823, 2016.
  10. [10] M. F. Antwi-Afari, H. Li, Y. Yu, and L. Kong, “Automated detection and classification of construction workers’ loss of balance events using wearable insole pressure sensors,” Automation in Construction, Vol.96, pp. 189-199, 2018.
  11. [11] Y. Hayakawa, S. Kawanaka, K. Kanezaki, K. Minami, and S. Doi, “Study on Presentation System for Walking Training using High-Performance Shoes,” J. Robot. Mechatron, Vol.27, No.6, pp. 706-713, 2015.
  12. [12] T. Mori, M. Hamatani, H. Noguchi, M. Oe, and H. Sanada, “Insole-Type Simultaneous Measurement System of Plantar Pressure and Shear Force During Gait for Diabetic Patients,” J. Robot. Mechatron, Vol.24, No.5, pp. 766-772, 2012.
  13. [13] P. Jacqueline, “Gait Analysis: Normal and Pathological Function (2nd Ed.),” Slack Inc., pp. 2-47, 2007.
  14. [14] K. Ueda, M. Tamaki, and K. Yasumoto, “A System for Daily Living Activities Recognition Based on Multiple Sensing Data in a Smart Home,” Proc. of Multimedia, Distributed, Cooperative, and Mobile Symp. (DICOMO2014), pp. 1884-1891, 2014 (in Japanese).
  15. [15] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al., “Scikit-learn: Machine Learning in Python,” J. of Machine Learning Re-Search, Vol.12, pp. 2825-2830, 2011.
  16. [16] J. Grus, “Data Science from Scratch,” O’Reilly, 2017.

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Last updated on Jun. 05, 2023