Ordinary differential equation based neural network coupled with random forest in the quality assessment of hand hygiene processes

We describe a novel approach for quality assessment of hand hygiene process based on combination of ordinary differential equation (ODE) neural network and random forest. The continuous-time recurrent neural network (RNN) with ODE hidden nodes is utilized. Unlike traditional continuous-time RNNs, the ODE network in this scheme applies an input-dependent varying time-constant models referred as liquid time-constant (LTC) RNN. It expresses stable and bounded behavior and yields superior expressivity. The random forest is attractive for findings of multiple trees in classification. It is built from multiple LTC networks, each network is corresponding to each tree. The experimental results showed that the proposed approach attains the recognition accuracy of 98.9% for single handwashing step and 78% for the whole handwashing process. •A novel approach for quality assessment of hand hygiene process.•Use of ordinary differential equation (ODE) neural networks and random forests.•Utilizing the input-dependent varying time-constants model.•The performance is superior to other popular types of recurrent neural networks.