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

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, clinical surgery: in-vivo human). This paper 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 force measured from ex-vivo pig eyes (no treatment, treat with 0.1% and 0.5% sodium hypochlorite), in-vivo rabbit eyes, and ex-vivo human lens 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 all training data comes from animal models.