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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 35–43 ()

Full text

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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