WAKE: Wavelet decomposition coupled with adaptive Kalman filtering for pathological tremor extraction
Open accessAbstract
Pathological Hand Tremor (PHT) is among common symptoms of several neurological movement disorders, which can significantly degrade quality of life of affected individuals. Beside pharmaceutical and surgical therapies, mechatronic technologies have been utilized to control PHTs. Most of these technologies function based on estimation, extraction, and characterization of tremor movement signals. Real-time extraction of tremor signal is of paramount importance because of its application in assistive and rehabilitative devices. In this paper, we propose a novel on-line adaptive method which can adjust the hyper-parameters of the filter to the variable characteristics of the tremor. The proposed technique (i.e., WAKE) is composed of a new adaptive Kalman filter and a wavelet transform core to provide indirect prediction of the tremor, one sample ahead of time, to be used for its suppression. In this paper, the design, implementation and evaluation of WAKE are given. The performance is evaluated on two different datasets. One dataset is recorded from patients with PHTs and the other one is a synthetic dataset, developed in this work, that simulates hand tremor under ten different conditions. The results demonstrate a significant improvement in the estimation accuracy in comparison with two well regarded techniques in literature.
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There is an unmet need for a reliable, adaptable, and generalizable processing framework that can be directly translated to clinical settings for estimation, extraction, and prediction of action tremor with high spectrotemporal resolution.
“Despite the crucial need for PHT elimination techniques and the numerous research advancements on this topic51–55, including our previous work13,14, there is an unmet need for a reliable, adaptable, and generalizable processing framework that can be directly translated to clinical settings for estimation, extraction, and prediction of action tremor with high spectrotemporal resolution.”
From PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models (2020) · cites this paper as [14]
The Introduction (page unavailable) says variable tremor challenges accurate real-time extraction and motivates methods combining temporal and spectral features. WAKE is itself an adaptive extraction and prediction framework, so the citation supports identifying it as prior work. The paper does not establish the broader claim that a generalizable, directly clinically translatable framework remains unavailable. (LLM, conf=0.82)
Experimental — accuracy not yet measuredResearch on using the KF and its derivative algorithms has shown improved accuracy in tremor estimation compared to gradient descent methods.
“Furthermore, research on using the KF and its derivative algorithms [21], [31], [32] has shown improved accuracy in tremor estimation compared to gradient descent methods.”
From Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device (2022) · cites this paper as [31]
The Abstract and Section 5 describe WAKE as an adaptive Kalman-filtering method that improves tremor-estimation accuracy over its counterparts; Section 4 compares it with BMFLC and E-BMFLC. Our copy does not identify those comparators as gradient-descent methods, so it does not establish the specific comparison claimed. (LLM, conf=0.78)
Experimental — accuracy not yet measuredExisting methods including EBMFLC and WAKE assume that the spectral contents of voluntary and involuntary components are distinct and that one could be derived by removing the other from the measurement signal.
“Existing methods including EBMFLC and WAKE, share a similar characteristic by assuming that the spectral contents of voluntary and involuntary components are distinct and one could be derived by removing the other from the measurement signal.”
From PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models (2020) · cites this paper as [14]
FLC-based methods, e.g., BMFLC and EBMFLC, focus on modeling the spectral contents of the measurement signal with a linear combination of spectral components, while the WAKE method employs spectrotemporal techniques and Kalman filtering to decompose the measurement signal into the two components of motion.
“While the FLC-based methods, e.g., BMFLC and EBMFLC, are focused on modeling the spectral contents of the measurement signal with a linear combination of spectral components, the WAKE method employs spectrotemporal techniques and Kalman filtering to decompose the measurement signal into the two components of motion.”
From PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models (2020) · cites this paper as [14]
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- Authors
- Shahtalebi, S., Atashzar, S. F., Patel, R. V., & Mohammadi, A.
- Affiliations
- Concordia University (CA)
- Western University (CA)
- Corresponding author
- Mohammadi, Arash
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 48
- Pages
- 179-188
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W2769629983
- Semantic Scholar
- 113a0568532c86e37fbbe4a8d7d738bbb6221484
- Cited by
- 29
- References
- 31
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 1 · 2023: 6 · 2022: 4 · 2021: 5 · 2020: 4 · 2019: 2
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Biomedical Signal Processing and Control (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- FRQNT (2018-NC-206591)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Physical Sciences › Engineering › Computational Mechanics › Advanced Adaptive Filtering Techniques
- Keywords
- wavelet decomposition, adaptive Kalman filter, tremor suppression, movement disorders
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