Design and validation of a high-order weighted-frequency fourier linear combiner-based Kalman filter for parkinsonian tremor estimation
Abstract
The design of a tremor estimator is an important part of designing mechanical tremor suppression orthoses. A number of tremor estimators have been developed and applied with the assumption that tremor is a mono-frequency signal. However, recent experimental studies have shown that Parkinsonian tremor consists of multiple frequencies, and that the second and third harmonics make a large contribution to the tremor. Thus, the current estimators may have limited performance on estimation of the tremor harmonics. In this paper, a high-order tremor estimation algorithm is proposed and compared with its lower-order counterpart and a widely used estimator, the Weighted-frequency Fourier Linear Combiner (WFLC), using 18 Parkinsonian tremor data sets. The results show that the proposed estimator has better performance than its lower-order counterpart and the WFLC. The percentage estimation accuracy of the proposed estimator is 85±2.9%, an average improvement of 13% over the lower-order counterpart. The proposed algorithm holds promise for use in wearable tremor suppression devices.
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Research 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 [21]
Cites
Adaptive cancelling of physiological tremor for improved precision in microsurgery
Riviere, C. N., Rader, R. S., & Thakor, N. V. (1998) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.686791 · Cited by 260 · see citing works
Real-Time Estimation of Pathological Tremor Parameters from Gyroscope Data
Gallego, J., Rocon, E., Roa, J. O., Moreno, J., & Pons, J. L. (2010) · Sensors · doi:10.3390/s100302129 · Cited by 107 · see citing works
Adaptive Fourier modeling for quantification of tremor
Riviere, C. N., Reich, S. G., & Thakor, N. V. (1997) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(97)02263-2 · Cited by 83 · see citing works
Loureiro, R. C. V., Belda-Lois, J. M., Lima, E. R., Pons, J. L., Sanchez-Lacuesta, J. J., & Harwin, W. S. (2005) · 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005. · doi:10.1109/icorr.2005.1501065 · Cited by 70 · see citing works
Pathological Tremor and Voluntary Motion Modeling and Online Estimation for Active Compensation
Bo, A. P. L., Poignet, P., & Geny, C. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2010.2091283 · Cited by 53 · see citing works
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A Review on Wearable Technologies for Tremor Suppression
Lora-Millan, J. S., Delgado-Oleas, G., Benito-León, J., & Rocon, E. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.700600 · Cited by 62 · see citing works
“They calculated offline the voluntary action by using a Kalman filter for parkinsonian tremor estimation (103) and used it as the input for their system.”
MethodZhou, Y., Naish, M. D., Jenkins, M. E., & Trejos, A. L. (2017) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.12.009 · Cited by 46 · see citing works
Self-citationCharacterization of Parkinsonian Hand Tremor and Validation of a High-Order Tremor Estimator
Zhou, Y., Jenkins, M. E., Naish, M. D., & Trejos, A. L. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2859793 · Cited by 41 · see citing works
Self-citationZhou, Y., Habibollahi, Z., Ibrahim, A., Jenkins, M. E., Naish, M. D., & Trejos, A. L. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3211450 · Cited by 8 · see citing works
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- Authors
- Zhou, Y., Jenkins, M. E., Naish, M. D., & Trejos, A. L.
- Affiliations
- Western University (CA)
- Venue
- 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2016-08
- Volume
- 2016
- Pages
- 5893-5896
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2534129474
- Cited by
- 16
- References
- 5
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 4 · 2020: 1 · 2018: 3 · 2017: 1
- FWCI
- 2.67
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Scholarship@Western (Western University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- parkinsonian tremor, tremor suppression
- MeSH terms
- Algorithms, Fourier Analysis, Humans, Monitoring, Physiologic, Parkinson Disease, Tremor
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