Wearable-Sensor-Based Weakly Supervised Parkinson's Disease Assessment with Data Augmentation.
Sensors (Basel)
; 24(4)2024 Feb 12.
Article
in En
| MEDLINE
| ID: mdl-38400357
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Parkinson Disease
/
Wearable Electronic Devices
Limits:
Humans
Language:
En
Journal:
Sensors (Basel)
Year:
2024
Type:
Article
Affiliation country:
United kingdom