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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 21

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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

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DOI: http://dx.doi.org/10.15439/2020F92

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 3543 ()

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Abstract. 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.


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