Modeling 3D tremor signals with a quaternion weighted Fourier Linear Combiner

IEEE Trans. Automat. Sci. Eng.

et al. 2022

Self Cite

All existing physiological tremor filtering algorithms, developed for robotic microsurgery, use non-linear phase pre-filters to isolate the tremor signal. Such filters cause phase distortion to the filtered tremor signal and limit the filtering accuracy. We revisited this long-standing problem to enable filtering of the physiological tremor without any phase distortion. We developed a combined estimation-prediction paradigm that offers zero-phase type filtering. The estimation is achieved with the mathematically modified recursive singular spectrum analysis algorithm and the prediction is delivered with the standard extreme learning machine. In addition, to limit the computational cost; we developed two moving window versions of this structure, appropriate for real-time implementation. The proposed paradigm preserved the natural phase of the filtered tremor. It achieved the key performance index of error limitation below 10µm, yielding the estimation accuracy larger than 70%, at a time delay of 36ms only. Both moving window versions of the proposed approach restricted the computational cost considerably; whilst offering the same performance. It is for the first time that effective estimation of the physiological tremor is achieved, without any pre-filtering and phase distortion. This proposed method is feasible for real-time implantation. Clinical translation of the proposed paradigm can significantly enhance the outcome in hand-held surgical robotics. Note to Practitioners-The imprecision caused by physiological hand tremor in microsurgeries has motivated researchers to innovate an efficient tremor compensating technique that can improve the surgical performance. Yet, all the existing tremor filtering algorithms, implemented in hand-held surgical instruments, use non-linear phase pre-filters to separate the tremor signal. The inherent phase distortion caused by such pre-filters restricts the filtering performance significantly and renders the existing methods inadequate for hand-held robotic surgery. Motivated by this, we proposed a novel estimator-predictor based framework, by adopting the modified recursive singular spectrum analysis estimator and the extreme learning machine predictor. The proposed framework filters the tremor signal accurately, without distorting it, but at a small fixed lag. In a set of rigorous testing performed by emulating a real-time processing, the proposed Manuscript received August 29, 2019.

Section: Discussionmentioning

confidence: 99%

Physiological Tremor Filtering Without Phase Distortion for Robotic Microsurgery

IEEE Trans. Automat. Sci. Eng.

et al. 2022

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“…The position was calulated by the reflected infra-red rays onto the detectors. For more details please refer to [10]- [12].…”

Section: E Experimental Setupmentioning

confidence: 99%

Real-time physiological tremor estimation using recursive singular spectrum analysis

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

et al. 2017

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Physiological hand tremor causes undesirable vibration of hand-held surgical instruments which results in imprecisions and poor surgical outcomes. Existing tremor cancellation algorithms are based on detection of the tremulous component from the whole motion; then adding an anti-phase tremor signal to the whole motion to cancel it out. These techniques are based on adaptive filtering algorithms which need a reference signal that is highly correlated with the actual tremor signal. Hence, such adaptive approaches use a non-linear phase filter to pre-filter the tremor signal either offline or in real-time. However, pre-filtering causes unnecessary delays and non-linear phase distortions as the filter has frequency selective delays. Consequently, the anti-phase tremor signal cannot be generated accurately which results in poor tremor cancellation. In this paper, we present a new technique based on singular spectrum analysis (SSA) and its recursive version, that is, recursive singular spectrum analysis (RSSA). These algorithms decompose the whole motion into dominant voluntary components corresponding to larger eigenvalues and oscillatory tremor components having smaller eigenvalues. By selecting a group of specific decomposed signals based on their eigenvalues and spectral range, both voluntary and tremor signals can be reconstructed accurately. We test the SSA and RSSA algorithms using recorded tremor data from five novice subjects. This new approach shows the tremor signal can be estimated from the whole motion with an accuracy of up to 85% offline. In real-time, tolerating a delay of ≈ 72ms, the tremor signal can be estimated with at least 70% accuracy. This delay is found to be one-tenth of the delay caused by a conventional linear-phase bandpass filter to achieve similar performance in real-time.

“…In [12] we have explained how we modelled 3 xyz tremor channels in 3-D quaternion form and showed the proposed new model improves the estimation performance. Extending our work from there, using same quaternion algebra we further extend our algorithm into 4-D as shown in Figure 2.…”

Section: Quaternion Weighted Fourier Linear Combinermentioning

confidence: 99%

“…Motivating by this, we extended the existing wFLC into three dimensions and called it QwFLC-3D which yielded 27% improvement in tremor modeling [12]. We further looked at the coherence between the grip force (force exerted by hand while holding a device) and three xyz tremor channels and found significant coherence between them.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improvement in modelling of physiological tremor by inclusion of grip force in quaternion weighted Fourier linear combiner

2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

et al. 2015

Physiological tremor is an involuntary oscillatory movement of body parts particularly exhibited in hands. The unintended vibrations due to tremor causes tip of the microsurgical tools to fluctuate unnecessarily causing unacceptable imprecisions in micro-surgeries. All the existing algorithms such as weighted Fourier Linear Combiner (wFLC), and its extensions, treat three x, y and z axes of tremor as three independent channels. However our correlation and coherence analysis showed that there is significant coupling between tremor channels. More importantly grip force by which a surgeon holds a surgical device also shows significant coupling with three tremor channels, which has never been investigated before. We first modelled the tremor in 3 dimensions (3-D) using quaternion algebra, and found 27% improvement which we have presented in our previous work. In this paper, we show modelling tremor in 4 dimensions by incorporating grip force and three tremor channels further exploits the cross-channel coupling information which is naturally inherent between force and xyz tremor channels. Using quaternion algebra we extract this coupling information to improve the tremor modelling performance. We show that estimation performance of tremor improves by 45% using 4-D model instead 3-D, yielding overall improvement of 65% from 1-D to 4-D.