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Discovering the typing behaviour of Parkinson's patients using topic models

Discovering the typing behaviour of Parkinson's patients using topic models
Discovering the typing behaviour of Parkinson's patients using topic models
Sensing health-related behaviours in an unobtrusive, ubiquitous and cost-effective manner carries significant benefits to healthcare and patient management. In this paper, we focus on detecting typing behaviour that is characteristic of patients suffering from Parkinson’s disease. We consider typing data obtained from subjects with and without Parkinson’s, and we present a framework based on topic models that determines the differing behaviours between these two groups based on the key hold time. By learning a topic model on each group separately and measuring the dissimilarity between topic distributions, we are able to identify particular topics that emerge in Parkinson’s patients and have low probability for the control group, demonstrating a clear shift in terms of key stroke duration. Our results further support the utilisation of key stroke logs for the early onset detection of Parkinson’s disease, while the method presented is straightforwardly generalisable to similar applications.
89-97
Springer
Milne, Antony
4b472700-8bf5-408a-a867-6480dc4138e9
Nicolaou, Mihalis
9a10dc98-d381-4bc3-a864-1e4c106e6f8c
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Ciampaglia, G.
Mashhadi, A.
Yasseri, T.
Milne, Antony
4b472700-8bf5-408a-a867-6480dc4138e9
Nicolaou, Mihalis
9a10dc98-d381-4bc3-a864-1e4c106e6f8c
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Ciampaglia, G.
Mashhadi, A.
Yasseri, T.

Milne, Antony, Nicolaou, Mihalis and Farrahi, Katayoun (2017) Discovering the typing behaviour of Parkinson's patients using topic models. Ciampaglia, G., Mashhadi, A. and Yasseri, T. (eds.) In Social Informatics: SocInfo 2017. vol. 10540, Springer. pp. 89-97 .

Record type: Conference or Workshop Item (Paper)

Abstract

Sensing health-related behaviours in an unobtrusive, ubiquitous and cost-effective manner carries significant benefits to healthcare and patient management. In this paper, we focus on detecting typing behaviour that is characteristic of patients suffering from Parkinson’s disease. We consider typing data obtained from subjects with and without Parkinson’s, and we present a framework based on topic models that determines the differing behaviours between these two groups based on the key hold time. By learning a topic model on each group separately and measuring the dissimilarity between topic distributions, we are able to identify particular topics that emerge in Parkinson’s patients and have low probability for the control group, demonstrating a clear shift in terms of key stroke duration. Our results further support the utilisation of key stroke logs for the early onset detection of Parkinson’s disease, while the method presented is straightforwardly generalisable to similar applications.

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Milne-socinfo17 - Accepted Manuscript
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Published date: September 2017

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Local EPrints ID: 419356
URI: http://eprints.soton.ac.uk/id/eprint/419356
PURE UUID: 0aa61ce5-9f2c-4865-aa5f-c8dd57d40f02

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Date deposited: 11 Apr 2018 16:30
Last modified: 03 Jun 2020 04:01

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