Physiological Tremor Estimation With Autoregressive (AR) Model and Kalman Filter for Robotics Applications

Tatinati, Sivanagaraja; Veluvolu, Kalyana C.; Hong, Sungjun; Latt, Win Tun; Ang, Wei Tech

doi:10.1109/jsen.2013.2271737

Cited by 34 publications

(30 citation statements)

References 28 publications

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“…Tremor data of five surgeons and five novice subjects with six trials per subject were considered for analysis in this paper. For more details about trials selection, see [5], [6], [15].…”

Section: Methodsmentioning

confidence: 99%

“…From (5), the corresponding Lagrangian multipliers and bias values are computed for the updated N data pairs. With the updated Lagrangian multipliers and bias, prediction is performed with (6).…”

Section: B Methodsmentioning

confidence: 99%

“…Recently, several signal processing algorithms have been developed to model and predict physiological tremor with the aid of robotic devices [4], [5], [14], [15], [21]. Comparative performance of all tremor prediction algorithms was provided in [5].…”

Section: Introductionmentioning

confidence: 99%

“…Comparative performance of all tremor prediction algorithms was provided in [5]. Band-limited multiple Fourier linear combiner (BMFLC) with Kalman filter (KF) (BMFLC-KF), and autoregressive model (AR) with KF (AR-KF) outperform other algorithms [5].…”

Section: Introductionmentioning

confidence: 99%

“…Although the methods provide a good prediction accuracy, the performance is adversely affected in the presence of phase delay. Tatinati et al [5] and Veluvolu et al [8] have documented the effect of phase delay on the tremor compensation in surgical robotics applications. As a solution to overcome the phase delay, multistep prediction of tremor with modifications to BMFLC-KF (MS-BMFLC-KF) and AR-KF (MS-AR-KF) have been proposed in [8].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multistep Prediction of Physiological Tremor Based on Machine Learning for Robotics Assisted Microsurgery

Tatinati

Veluvolu

Ang

2015

IEEE Trans. Cybern.

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For effective tremor compensation in robotics assisted hand-held device, accurate filtering of tremulous motion is necessary. The time-varying unknown phase delay that arises due to both software (filtering) and hardware (sensors) in these robotics instruments adversely affects the device performance. In this paper, moving window-based least squares support vector machines approach is formulated for multistep prediction of tremor to overcome the time-varying delay. This approach relies on the kernel-learning technique and does not require the knowledge of prediction horizon compared to the existing methods that require the delay to be known as a priori. The proposed method is evaluated through simulations and experiments with the tremor data recorded from surgeons and novice subjects. Comparison with the state-of-the-art techniques highlights the suitability and better performance of the proposed method.

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“…Tremor data of five surgeons and five novice subjects with six trials per subject were considered for analysis in this paper. For more details about trials selection, see [5], [6], [15].…”

Section: Methodsmentioning

confidence: 99%

Section: B Methodsmentioning

confidence: 99%