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.
Communication delay represents a fundamental challenge in telerobotics: on one hand, it compromises the stability of teleoperated robots, on the other hand, it decreases the user’s awareness of the designated task. In scientific literature, such a problem has been addressed both with statistical models and neural networks (NN) to perform sensor prediction, while keeping the user in full control of the robot’s motion. We propose shared control as a tool to compensate and mitigate the effects of communication delay. Shared control has been proven to enhance precision and speed in reaching and manipulation tasks, especially in the medical and surgical fields. We analyse the effects of added delay and propose a unilateral teleoperated leader-follower architecture that both implements a predictive system and shared control, in a 1-dimensional reaching and recognition task with haptic sensing. We propose four different control modalities of increasing autonomy: non-predictive human control (HC), predictive human control (PHC), (shared) predictive human-robot control (PHRC), and predictive robot control (PRC). When analyzing how the added delay affects the subjects’ performance, the results show that the HC is very sensitive to the delay: users are not able to stop at the desired position and trajectories exhibit wide oscillations. The degree of autonomy introduced is shown to be effective in decreasing the total time requested to accomplish the task. Furthermore, we provide a deep analysis of environmental interaction forces and performed trajectories. Overall, the shared control modality, PHRC, represents a good trade-off, having peak performance in accuracy and task time, a good reaching speed, and a moderate contact with the object of interest.
The mechanical properties of a sensor strongly affect its tactile sensing capabilities. The role of morphology and stiffness on the quality of the tactile data has already been the subject of several studies, which focus mainly on static sensor designs and design methodologies. However, static designs always come with trade-offs: considering stiffness, soft compliant sensors ensure a better contact, but at the price of mechanically filtering and altering the detected signal. Conversely, online adaptable filters can tune their characteristics, becoming softer or stiffer when needed. We propose a magneto-active elastomer filter which, when placed on top of the tactile unit, allows the sensor to change its stiffness on demand. We showcase the advantages provided by online stiffening adaptation in terms of information gained and data structure. Moreover, we illustrate how adaptive stiffening influences classification, using 9 standard machine learning algorithms, and how adaptive stiffening can increase the classification accuracy up to 34% with respect to static stiffness control.
Palpation is a method use by physicians to physically examine patients using fingers or hands to diagnose any disease or illness. Vocal pain expressions of the patient during palpation are considered as important feedback to assess the conditions. Although recent technological advances has enabled development of medical simulators for physician to train the palpation procedures, incorporating vocal pain expressions to these simulators has been understudied. In this paper, we present a vocal pain expression augmentation for a robopatient to be used in abdominal palpation training. Our virtual robopatient builds upon a virtual abdomen and a face which can render facial pain expressions together with vocal pain expressions. In a user study (N=26), we test the vocal pain augmented virtual robopatient against a system without vocal pain expressions in a palpation task to estimate the maximum pain point within the virtual abdomen. We demonstrate that the vocal pain augmented virtual robopatient leads to statistically significant improvements in localizing the maximum pain without compromising the position estimation time.CONFIDENTIAL. Limited circulation. For review only.
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