Physiological Tremor Filtering Without Phase Distortion for Robotic Microsurgery

All existing physiological tremor filtering algorithms, developed for robotic microsurgery, use nonlinear phase prefilters 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, which are 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
$10\mu \text{m}$
, yielding the estimation accuracy larger than 70%, at a time delay of 36 ms only. Both moving window versions of the proposed approach restricted the computational cost considerably while offering the same performance. It is the first time that the effective estimation of the physiological tremor is achieved, without any prefiltering 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 surgical performance. Yet, all the existing tremor filtering algorithms, implemented in hand-held surgical instruments, use nonlinear phase prefilters to separate the tremor signal. The inherent phase distortion caused by such prefilters 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 real-time processing, the proposed algorithm showed higher performance compared with the state-of-the-art algorithms. This validates not only its suitability for real-time implantation but also its potential to improve surgical performance, which has been limited by the distorted filtering. Nonetheless, we have presented a proof-of-principle framework for distortion-free filtering, but its full implementation in a real surgical instrument, such as Micron or ITrem, requires a substantial amount of experimental testing and verification. It can be also applicable in a wide range of areas, including health-care, digital manufacturing, smart automation and control, and various other robotic technologies where efficient filtering of advanced sensor data is highly desirable. In the future, we will develop the multidimensional model of the proposed framework to enable filtering of tremor in the
xyz
-axes simultaneously.