PD disease state assessment in naturalistic environments using deep learning

Hammerla, Nils Yannick; Fisher, James; Andras, Peter; Rochester, Lynn; Walker, Richard; Pl�tz, Thomas

Abstract

Management of Parkinson's Disease (PD) could be improved significantly if reliable, objective information about fluctuations in disease severity can be obtained in ecologically valid surroundings such as the private home. Although automatic assessment in PD has been studied extensively, so far no approach has been devised that is useful for clinical practice. Analysis approaches common for the field lack the capability of exploiting data from realistic environments, which represents a major barrier towards practical assessment systems. The very unreliable and infrequent labelling of ambiguous, low resolution movement data collected in such environments represents a very challenging analysis setting, where advances would have significant societal impact in our ageing population. In this work we propose an assessment system that abides practical usability constraints and applies deep learning to differentiate disease state in data collected in naturalistic settings. Based on a large data-set collected from 34 people with PD we illustrate that deep learning outperforms other approaches in generalisation performance, despite the unreliable labelling characteristic for this problem setting, and how such systems could improve current clinical practice.

Citation

Hammerla, N. Y., Fisher, J., Andras, P., Rochester, L., Walker, R., & Plötz, T. (2015, January). PD disease state assessment in naturalistic environments using deep learning. Presented at Twenty-Ninth AAAI conference on artificial intelligence, Austin, Texas

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Twenty-Ninth AAAI conference on artificial intelligence
• Start Date: Jan 25, 2015
• End Date: Jan 30, 2015
• Online Publication Date: Jan 25, 2015
• Publication Date: 2015
• Deposit Date: Nov 9, 2021
• Publisher: Association for Computing Machinery (ACM)
• Pages: 1742-1748
• Book Title: AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
• ISBN: 0262511290
• Public URL: http://researchrepository.napier.ac.uk/Output/2809247