Real-World Parkinson’s Hand Tremor Detection Using Ensemble Learning Techniques
Hand tremor is a major motor symptom of Parkinson’s disease (PD) that seriously affects the life quality of patients. Conventional tremor detection methods have mainly focused on resting state. However, resting tremor is not always sufficient to accurately distinguish PD tremor from other...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10854462/ |
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| Summary: | Hand tremor is a major motor symptom of Parkinson’s disease (PD) that seriously affects the life quality of patients. Conventional tremor detection methods have mainly focused on resting state. However, resting tremor is not always sufficient to accurately distinguish PD tremor from other tremor disorders such as essential tremor. Examining tremor during movement provides additional diagnostic value, but also has challenges. In this paper, we propose a novel approach to detect Parkinsonian hand tremor at walking state by exploiting the periodic synchronization between arm swings and leg steps that is observed in natural walking, specifically by using a wrist-worn sensor to capture the characteristic hand tremor that is induced during walking in Parkinson’s patients. Our method first applies a dynamic scanning mechanism to screen out valid walking fragments from whole walking sequence. We then exploit walking periodicity to combine time and frequency domain features, and design two new accelerometer-based features, Average Peak Interval Time (APIT) and Average Peak Frequency (APF), which capture the characteristic amplitude and frequency patterns of PD tremors during natural walking movements. Finally, unlike traditional approaches that use uniform feature weighting, our proposed variance adaptive AdaBoost (VA-AdaBoost) algorithm uniquely incorporates accelerometer signal variance to adaptively weight samples for feature training. The results of our five-fold cross-validation experimental evaluation using the PD-BioStampRC21 dataset show that our method achieved an accuracy of 86.67% and an AUC score of 95.56%, representing a significant improvement over the baseline model’s accuracy of 72.67% and surpassing other existing methods. Our method enables more accurate detection of subtle tremor patterns in real-world conditions compared to conventional methods. The proposed method has shown potential in the integration of wearable sensors and ensemble learning techniques for the early and accessible diagnosis of Parkinsonian hand tremor, ultimately contributing to improved patient care and treatment. |
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| ISSN: | 2169-3536 |