Wearable-Sensor-Based Weakly Supervised Parkinson’s Disease Assessment with Data Augmentation

Author:

Yue Peng12,Li Ziheng3,Zhou Menghui1,Wang Xulong1,Yang Po1

Affiliation:

1. Department of Computer Science, University of Sheffield, Sheffield S10 2TN, UK

2. AntData Ltd., Liverpool L16 2AE, UK

3. Department of Software, Yunnan University, Kunming 650106, China

Abstract

Parkinson’s disease (PD) is the second most prevalent dementia in the world. Wearable technology has been useful in the computer-aided diagnosis and long-term monitoring of PD in recent years. The fundamental issue remains how to assess the severity of PD using wearable devices in an efficient and accurate manner. However, in the real-world free-living environment, there are two difficult issues, poor annotation and class imbalance, both of which could potentially impede the automatic assessment of PD. To address these challenges, we propose a novel framework for assessing the severity of PD patient’s in a free-living environment. Specifically, we use clustering methods to learn latent categories from the same activities, while latent Dirichlet allocation (LDA) topic models are utilized to capture latent features from multiple activities. Then, to mitigate the impact of data imbalance, we augment bag-level data while retaining key instance prototypes. To comprehensively demonstrate the efficacy of our proposed framework, we collected a dataset containing wearable-sensor signals from 83 individuals in real-life free-living conditions. The experimental results show that our framework achieves an astounding 73.48% accuracy in the fine-grained (normal, mild, moderate, severe) classification of PD severity based on hand movements. Overall, this study contributes to more accurate PD self-diagnosis in the wild, allowing doctors to provide remote drug intervention guidance.

Publisher

MDPI AG

Reference41 articles.

1. Symptoms and possible causes cures for parkinsons disease;McHenry;Brain Matters,2021

2. Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030;Dorsey;Neurology,2007

3. Diagnostic criteria for Parkinson disease;Gelb;Arch. Neurol.,1999

4. Nass, R., and Przedborski, S. (2011). Parkinson’s Disease: Molecular and Therapeutic Insights from Model Systems, Elsevier.

5. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results;Goetz;Mov. Disord. Off. J. Mov. Disord. Soc.,2008

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