Inclusion detection with haptic-palpation system for medical telediagnosis

Multi-silicone bilateral soft physical twin as an alternative to traditional user interfaces for remote palpation: a comparative study

Teleoperated medical technologies are a fundamental part of the healthcare system. From telemedicine to remote surgery, they allow remote diagnosis and treatment. However, the absence of any interface able to effectively reproduce the sense of touch and interaction with the patient prevents the implementation of teleoperated systems for primary care examinations, such as palpation. In this paper, we propose the first reported case of a soft robotic bilateral physical twin for remote palpation. By creating an entirely soft interface that can be used both to control the robot and receive feedback, the proposed device allows the user to achieve remote palpation by simply palpating the soft physical twin. This is achieved through a compact design showcasing 9 pneumatic chambers and exploiting multi-silicone casting to minimize cross-noise and allow teleoperation. A comparative study has been run against a traditional setup, and both the control and feedback of the physical twin are carefully analyzed. Despite distributed tactile feedback not achieving the same performance as the visual map, the soft control and visual feedback combination showcases a 5.1% higher accuracy. Moreover, the bilateral soft physical twin results always in a less invasive procedure, with 41% lower mechanical work exchanged with the remote phantom.

Face mediated human–robot interaction for remote medical examination

Realtime visual feedback from consequences of actions is useful for future safety-critical human–robot interaction applications such as remote physical examination of patients. Given multiple formats to present visual feedback, using face as feedback for mediating human–robot interaction in remote examination remains understudied. Here we describe a face mediated human–robot interaction approach for remote palpation. It builds upon a robodoctor–robopatient platform where user can palpate on the robopatient to remotely control the robodoctor to diagnose a patient. A tactile sensor array mounted on the end effector of the robodoctor measures the haptic response of the patient under diagnosis and transfers it to the robopatient to render pain facial expressions in response to palpation forces. We compare this approach against a direct presentation of tactile sensor data in a visual tactile map. As feedback, the former has the advantage of recruiting advanced human capabilities to decode expressions on a human face whereas the later has the advantage of being able to present details such as intensity and spatial information of palpation. In a user study, we compare these two approaches in a teleoperated palpation task to find the hard nodule embedded in the remote abdominal phantom. We show that the face mediated human–robot interaction approach leads to statistically significant improvements in localizing the hard nodule without compromising the nodule position estimation time. We highlight the inherent power of facial expressions as communicative signals to enhance the utility and effectiveness of human–robot interaction in remote medical examinations.