Identifying and Predicting Voluntary Peeling Force in Continuous Curvilinear Capsulorhexis Considering Species Differences

A handheld manipulator offers a feasible way to relieve the challenge of continuous curvilinear capsulorhexis (CCC) by controlling peeling force actively. The precise identification and prediction of voluntary peeling force (0–2 Hz) form the basis of force controller design. However, achieving this goal is limited by species differences (experimental validation: animal model and clinical surgery: in vivo human). This article proposes a framework to relieve this problem. The proposed framework consists of a wavelet-based process module and a prediction module based on long short-term memory (LSTM) and the Transformer’s encoder. Peeling forces measured from ex-vivo pig eyes (no treatment, treated with 0.1% and 0.5% sodium hypochlorite), in vivo rabbit eyes, and ex-vivo human lenses are utilized to validate the proposed framework. Experimental results show that the root-mean-square (rms) error of identification is 0.37 mN. The rms error of prediction is 0.41 mN (steady) and 0.87 mN (sharp, prediction time: 50 ms), respectively. The rms prediction error of ex-vivo human samples is 0.36 mN (steady) and 1.05 mN (sharp), even though all training data come from animal models.