13 research outputs found
Multi-dimensional modeling of physiological tremor for active compensation in hand-held surgical robotics
Improvement in modelling of physiological tremor by inclusion of grip force in quaternion weighted Fourier linear combiner
Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications
Real-time Physiological Hand Tremor Estimation using Recursive Singular Spectrum Analysis
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\mum, 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