Journal Article

Reputation-aware data fusion for quantifying hand tremor severity form interaction with a smartphone

Abstract

In this paper, we present an approach to improve the accuracy of hand tremor severity in Parkinson’s patients in real-life unconstrained environments. The system leverages data achieved from daily interaction people with their smartphones and uses technologies for classifying and combining data. We describe the basic concept of data fusion and demonstrate how different combination techniques can improve the accuracy of tremor detection. The fusion enable to achieve the 23.5% improvement with respect to the average of individual classification models.

Attached files

Authors

Biloborodova, Tetiana
Skarga-Bandurova, Inna
Kotsiuba, Igor
Skarha-Bandurov, Illia

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2021
Date of RADAR deposit: 2022-07-01

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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