BoostSole: Design and Realization of a Smart Insole for Automatic Human Gait Classification

• Autorzy: Djamaa Badis , Bessa Med Messaoud , Diaf Badreddine , Rouigueb Abdenebi , Yachir Ali

Identyfikatory

DOI

10.15439/2020F92

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

This paper presents BoostSole; a smart insole based system for automatic human gait recognition. It consists of a smart instrumented insole connected to the cloud via the patient's smartphone using low-power wireless communication. First, the design of BoostSole is introduced with discussions of sensors choice, placement, calibration, and data communication. Next, an adaptive multi-boost classification algorithm is deployed to accurately identify different gait patterns. The algorithm is fast and lightweight and can be implemented in ordinary smartphones with a small footprint in terms of computational requirements, energy consumption, and communication usage. Raw and on-device classified data can be securely uploaded to a distant cloud server for continuous monitoring and analysis. Indeed, they can be visualized and exploited by doctors to identify/correct walking habits and assess the risks of chronic pain associated with an abnormal walk. The system has been evaluated on a dataset containing three gait patterns, namely: shuffle walk; toe walking; and normal gait. Obtained results are promising with more than 97\\% classification accuracy accompanied by low response time and computational demands.

Słowa kluczowe

EN

smart insole; human gait analysis; force sensing resistors; multiboost classification; Internet of things

PL

inteligentna wkładka; analiza chodu człowieka; rezystory wykrywające siłę; klasyfikacja multiboost; Internet przedmiotów

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 35--43
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Djamaa Badis

• Computer Science Department, Ecole Militaire Polytechnique, Algiers, Algeria

autor

Bessa Med Messaoud

• Computer Science Department, Ecole Militaire Polytechnique, Algiers, Algeria

autor

Diaf Badreddine

• Computer Science Department, Ecole Militaire Polytechnique, Algiers, Algeria

autor

Rouigueb Abdenebi

• Computer Science Department, Ecole Militaire Polytechnique, Algiers, Algeria

autor

Yachir Ali

• Computer Science Department, Ecole Militaire Polytechnique, Algiers, Algeria

Bibliografia

• 1. A. Muro-de-la Herran, B. Garcia-Zapirain, and A. Mendez-Zorrilla, “Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications,” Sensors (Basel, Switzerland), vol. 14, pp. 3362–3394, Feb. 2014.
• 2. W. Donkrajang, N. Watthanawisuth, J. P. Mensing, and T. Kerdcharoen, “Development of a wireless electronic shoe for walking abnormalities detection,” in The 5th 2012 Biomedical Engineering International Conference, pp. 1–5, Dec. 2012.
• 3. S. J. Morris, A shoe-integrated sensor system for wireless gait analysis and real-time therapeutic feedback. PhD thesis, Massachusetts Institute of Technology, 2004.
• 4. S. Bamberg, A. Benbasat, D. Scarborough, D. Krebs, and J. Paradiso, “Gait Analysis Using a Shoe-Integrated Wireless Sensor System,” IEEE Transactions on Information Technology in Biomedicine, vol. 12, pp. 413–423, July 2008.
• 5. Hyejeong Nam, Jin-Hyun Kim, and Jee-In Kim, “Smart Belt : A wearable device for managing abdominal obesity,” in 2016 International Conference on Big Data and Smart Computing (BigComp), (Hong Kong, China), pp. 430–434, IEEE, Jan. 2016.
• 6. A. De Santis, E. Gambi, L. Montanini, L. Raffaeli, S. Spinsante, and G. Rascioni, “A simple object for elderly vitality monitoring: The smart insole,” in 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA), (Senigallia, Italy), pp. 1–6, IEEE, Sept. 2014.
• 7. W. Donkrajang, N. Watthanawisuth, J. P. Mensing, and T. Kerdcharoen, “A wireless networked smart-shoe system for monitoring human locomotion,” in The 4th 2011 Biomedical Engineering International Conference, (Chiang Mai, Thailand), pp. 54–58, IEEE, Jan. 2012.
• 8. J. Bae and M. Tomizuka, “Gait Phase Analysis based on a Hidden Markov Model,” IFAC Proceedings Volumes, vol. 43, no. 18, pp. 746–751, 2010.
• 9. G.-M. Jeong, P. Truong, and S.-I. Choi, “Activity classification of three types of walking regarding stairs using plantar pressure sensors,” IEEE Sensors Journal, vol. PP, pp. 1–1, 03 2017.
• 10. T. Nilpanapan and T. Kerdcharoen, “Social data shoes for gait monitoring of elderly people in smart home,” in 2016 9th Biomedical Engineering International Conference (BMEiCON), (Laung Prabang, Laos), pp. 1–5, IEEE, Dec. 2016.
• 11. S. Marquez J, R. Atri, M. R. Siddiquee, C. Leung, and O. Bai, “A Mobile, Smart Gait Assessment System for Asymmetry Detection Using Machine Learning-Based Classification,” Journal of Biomedical Engineering and Medical Devices, vol. 03, no. 02, 2018.
• 12. S.-S. Lee, S. T. Choi, and S.-I. Choi, “Classification of Gait Type Based on Deep Learning Using Various Sensors with Smart Insole,” Sensors, vol. 19, p. 1757, Apr. 2019.
• 13. S.-I. Choi, S.-S. Lee, H.-C. Park, and H. Kim, “Gait Type Classification Using Smart Insole Sensors,” in TENCON 2018 - 2018 IEEE Region 10 Conference, (Jeju, Korea (South)), pp. 1903–1906, IEEE, Oct. 2018.
• 14. W.-k. Tam, A. Wang, B. Wang, and Z. Yang, “Lower-body posture estimation with a wireless smart insole,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (Berlin, Germany), pp. 3348–3351, IEEE, July 2019.
• 15. D. Chen, Y. Cai, X. Qian, R. Ansari, W. Xu, K.-C. Chu, and M.-C. Huang, “Bring Gait Lab to Everyday Life: Gait Analysis in Terms of Activities of Daily Living,” IEEE Internet of Things Journal, vol. 7, pp. 1298–1312, Feb. 2020.
• 16. N. Carbonaro, F. Lorussi, and A. Tognetti, “Assessment of a Smart Sensing Shoe for Gait Phase Detection in Level Walking,” Electronics, vol. 5, p. 78, Nov. 2016.
• 17. R. Das and N. Kumar, “Investigations on postural stability and spatiotemporal parameters of human gait using developed wearable smart insole,” Journal of Medical Engineering & Technology, vol. 39, pp. 75–78, Jan. 2015.
• 18. G. Saggio, F. Riillo, L. Sbernini, and L. R. Quitadamo, “Resistive flex sensors: a survey,” Smart Materials and Structures, vol. 25, no. 1, p. 013001, 2015.
• 19. J. Pineda-Gutierrez, L. Miro-Amarante, M. Hernandez-Velazquez, F. Sivianes-Castillo, and M. Dominguez-Morales, “Designing a Wearable Device for Step Analyzing,” in 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), (Cordoba, Spain), pp. 259–262, IEEE, June 2019.
• 20. A. Jor, S. Das, A. S. Bappy, and A. Rahman, “Foot Plantar Pressure Measurement Using Low Cost Force Sensitive Resistor (FSR): Feasibility Study,” Journal of Scientific Research, vol. 11, pp. 311–319, Sept. 2019.
• 21. A. Jović, K. Brkić, and N. Bogunović, “Decision Tree Ensembles in Biomedical Time-Series Classification,” in Pattern Recognition, vol. 7476, pp. 408–417, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. Series Title: Lecture Notes in Computer Science.
• 22. G. I. Webb, “Multiboosting: A technique for combining boosting and wagging,” Machine Learning, vol. 40, no. 2, pp. 159–196, 2000.

Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).