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Tactile Robotic Telemedicine

Tactile robotic telemedicine allows medical experts to examine patients from a distance.

Robot-Assisted Telediagnostics with Tactile Perception:

(Pulse diagnosis): This involves the physician using their fingers to palpate the patient's radial artery at various locations and depths to evaluate the condition of the patient's body. It is considered a key part of the diagnostic process in traditional Chinese medicine (TCM). The physician checks for various pulse qualities like the speed, rhythm, strength, and shape of the pulse to make a diagnosis.


(Palpation diagnosis): This refers to the examination of the body by touch and pressure. The doctor uses their hands to feel the body for areas of warmth, coolness, resistance, or discomfort. Palpation is often used to check the abdominal region to identify any organ enlargement or presence of mass or fluid.

In traditional Chinese medicine, these techniques are part of a larger diagnostic process that also includes inspection, listening/smelling, and interrogation. This holistic approach allows practitioners to understand the state of the patient's Qi (energy flow) and the balance of Yin and Yang within their body.

Tac-VGNN: A Voronoi Graph Neural Network for Pose-Based Tactile Servoing 

Tactile pose estimation and tactile servoing are fundamental capabilities of robot touch. Reliable and precise pose estimation can be provided by applying deep learning models to high-resolution optical tactile sensors. Given the recent successes of Graph Neural Network (GNN) and the effectiveness of Voronoi features, we developed a Tactile Voronoi Graph Neural Network (Tac-VGNN) to achieve reliable pose-based tactile servoing relying on a biomimetic optical tactile sensor (TacTip). The GNN is well suited to modeling the distribution relationship between shear motions of the tactile markers, while the Voronoi diagram supplements this with area-based tactile features related to contact depth. The experiment results showed that the Tac-VGNN model can help enhance data interpretability during graph generation and model training efficiency significantly than CNN-based methods. It also improved pose estimation accuracy along vertical depth by 28.57% over vanilla GNN without Voronoi features and achieved better performance on the real surface following tasks with smoother robot control trajectories.


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TIMS: A Tactile Internet-Based Micromanipulation System with Haptic Guidance for Surgical Training


Microsurgery involves the dexterous manipulation of delicate tissue or fragile structures such as small blood vessels, nerves, etc., under a microscope. To address the limitation of imprecise manipulation of human hands, robotic systems have been developed to assist surgeons in performing complex microsurgical tasks with greater precision and safety. However, the steep learning curve for robot-assisted microsurgery (RAMS) and the shortage of well-trained surgeons pose significant challenges to the widespread adoption of RAMS. Therefore, the development of a versatile training system for RAMS is necessary, which can bring tangible benefits to both surgeons and patients.

In this paper, we present a Tactile Internet-Based Micromanipulation System (TIMS) based on a ROS-Django web-based architecture for microsurgical training. This system can provide tactile feedback to operators via a wearable tactile display (WTD), while real-time data is transmitted through the internet via a ROS-Django framework. In addition, TIMS integrates haptic guidance to `guide' the trainees to follow a desired trajectory provided by expert surgeons. Learning from demonstration based on Gaussian Process Regression (GPR) was used to generate the desired trajectory. User studies were also conducted to verify the effectiveness of our proposed TIMS, comparing users' performance with and without tactile feedback and/or haptic guidance.

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