Deep Reinforcement Learning based Haptic Enhancement for Tele-Diagnosis

ABSTRACT

Tele-Diagnosis is beneficial for medical care in areas with inadequate resources, which helps control the spread of Covid-19 in the current pandemic. Most teleoperated diagnostics are dependent on humans, possibly leading to cognitive issue caused by distanced communication. In this paper, we propose a local haptic enhancement framework to facilitate the remote palpation. The deep deterministic policy gradient (DDPG) algorithm is exploited to compensate for signal transmission due to latency, allowing human to operate without the sense of delay. With the help of weighted recursive least squares (WRLS) method, the interactive force can be estimated on the patient's side despite the lack of force sensors. Fuzzy inference is used to diagnose and classify the extent of disease based on the estimated force and motion state on the remote side, thereby leveraging the remote sensory information to conduct autonomous reasoning. Finally, the final diagnosis is derived by performing minimum risk Bayesian decision based on local and remote inference results. Comparative simulation results have validated the superior performances of the proposed scheme. © 2022 IEEE.