TY - GEN
T1 - Dynamic estimation strategy for E-BMFLC filters in analyzing pathological hand tremors
AU - Ghassab, Vahid Khorasani
AU - Mohammadi, Arash
AU - Atashzar, Seyed Farokh
AU - Patel, Rajni V.
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/3/7
Y1 - 2018/3/7
N2 - This paper proposes an adaptive filtering framework for real-Time estimation of pathological hand tremors such as those caused by Parkinson's disease (PD) and Essential Tremor (ET). The proposed framework is designed based on Enhanced Band-limited Multiple Fourier Linear Combiner (E-BMFLC), a recently proposed state-of-The-Art tremor estimation algorithm. Conventional BMFLC-based filter (and the E-BMFLC technique) are developed based on application of recursive Least Mean Square (LMS) algorithm. There exist variations of BMFLC-based filters that replace LMS approach by the Kalman Filter (KF) to enhance the performance. However, despite the improved performance, the KF increases the computational overhead challenging real-Time implementation which is essential in several application such as robotics-Assisted tremor suppression. The proposed framework, referred to as the Reduced-order Kalman-based E-BMFLC (RKE-BMFLC), addresses this gap. In particular, we propose a two-step development. First an extension of the E-BMFLC is proposed where classical KF is incorporated in place of the LMS algorithm; then we propose a specifically-designed reduced-order KF implementation to address the computational overhead. Evaluated through experimental pathological tremor data, the proposed RKE-BMFLC technique significantly reduces computational complexity of the conventional KF (for E-BMFLC filter) while is capable of providing improved accuracy level in comparison to the recently-developed LMS-based E-BMFLC technique.
AB - This paper proposes an adaptive filtering framework for real-Time estimation of pathological hand tremors such as those caused by Parkinson's disease (PD) and Essential Tremor (ET). The proposed framework is designed based on Enhanced Band-limited Multiple Fourier Linear Combiner (E-BMFLC), a recently proposed state-of-The-Art tremor estimation algorithm. Conventional BMFLC-based filter (and the E-BMFLC technique) are developed based on application of recursive Least Mean Square (LMS) algorithm. There exist variations of BMFLC-based filters that replace LMS approach by the Kalman Filter (KF) to enhance the performance. However, despite the improved performance, the KF increases the computational overhead challenging real-Time implementation which is essential in several application such as robotics-Assisted tremor suppression. The proposed framework, referred to as the Reduced-order Kalman-based E-BMFLC (RKE-BMFLC), addresses this gap. In particular, we propose a two-step development. First an extension of the E-BMFLC is proposed where classical KF is incorporated in place of the LMS algorithm; then we propose a specifically-designed reduced-order KF implementation to address the computational overhead. Evaluated through experimental pathological tremor data, the proposed RKE-BMFLC technique significantly reduces computational complexity of the conventional KF (for E-BMFLC filter) while is capable of providing improved accuracy level in comparison to the recently-developed LMS-based E-BMFLC technique.
UR - http://www.scopus.com/inward/record.url?scp=85048198748&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048198748&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2017.8308681
DO - 10.1109/GlobalSIP.2017.8308681
M3 - Conference contribution
AN - SCOPUS:85048198748
T3 - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
SP - 442
EP - 446
BT - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
Y2 - 14 November 2017 through 16 November 2017
ER -