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Towards predicting frailty symptoms through a smart walking stick

Abstract : A warning sign of frailty is imbalance. Psycho-motor therapists run tests to evaluate the balance deterioration but not often enough to track the rapidly changing condition of the elderly. The proposed system collects fine-grained data from a smart cane and processes them with Machine Learning (ML) techniques. The originality of our proposition lies in its personalization by the elderly biomarkers in ML algorithms. Our experiments indicate that we can observe the orientation of locomotion through the cane as well as recognize characteristics of specific participants ambulation in the uncontrolled scenario.
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Submitted on : Friday, July 12, 2019 - 2:52:42 PM
Last modification on : Thursday, June 25, 2020 - 12:02:09 PM


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  • HAL Id : hal-02181941, version 1
  • OATAO : 22556


Oussama Kessentini, Rejane Dalce, Imen Megdiche Bousarsar, Rémi Bastide. Towards predicting frailty symptoms through a smart walking stick. IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN 2018), Sep 2018, Toulouse, France. pp.1-7. ⟨hal-02181941⟩



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