Monitoring surgeon workload during robot-assisted surgery can guide allocation of task demands, adapt system interfaces, and assess the robotic system's usability. Current practices for measuring cognitive load primarily rely on questionnaires that are subjective and disrupt surgical workflow. To address this limitation, a computational framework is demonstrated to predict user workload during telerobotic surgery. This framework leverages wireless sensors to monitor surgeons’ cognitive load and predict their cognitive states. Continuous data across multiple physiological modalities (e.g., heart rate variability, electrodermal, and electroencephalogram activity) were simultaneously recorded for twelve surgeons performing surgical skills tasks on the validated da Vinci Skills Simulator. These surgical tasks varied in difficulty levels, e.g., requiring varying visual processing demand and degree of fine motor control. Collected multimodal physiological signals were fused using independent component analysis, and the predicted results were compared to the ground-truth workload level. Results compared performance of different classifiers, sensor fusion schemes, and physiological modality (i.e., prediction with single vs. multiple modalities). It was found that our multisensor approach outperformed individual signals and can correctly predict cognitive workload levels 83.2% of the time during basic and complex surgical skills tasks.

In Robot-Assisted Surgery, predicting surgeon attributes such as task workload, operation performance, and expertise levels is important in providing tailored assistance. This paper proposes Joint Inference for Surgeon Attributes Prediction (JISAP), a computational framework to jointly infer surgeon attributes (i.e., task workload, operation performance, and expertise level) from multimodal physiological signals (heart rate variability, wrist motion, electrodermal, electromyography, and electroencephalogram activity). JISAP was evaluated with a dataset of twelve surgeons operating on the da Vinci Skills Simulator. It was found that JISAP can simultaneously predict surgeon attributes with a percentage error of 11.05%. Additionally, joint inference was found to outperform isolated inference with a boost of 10%.

Individuals who are blind use exploration procedures (EPs) to navigate and understand digital images. The ability to model and detect these EPs can help the assistive technologies' community build efficient and accessible interfaces for the blind and overall enhance human-machine interaction. In this paper, we propose a framework to classify various EPs using spike-timing neural networks (SNNs). While users interact with a digital image using a haptic device, rotation and translation-invariant features are computed directly from exploration trajectories acquired from the haptic control. These features are further encoded as model strings through trained SNNs. A classification scheme is then proposed to distinguish these model strings to identify the EPs. The framework adapted a modified Dynamic Time Wrapping (DTW) for spatial-temporal matching with Dempster-Shafer Theory (DST) for multimodal fusion. Experimental results (87.05% as EPs' detection accuracy) indicate the effectiveness of the proposed framework and its potential application in human-machine interfaces.

Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40 % of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.

This research presents an exploratory study among touch-based and touchless interfaces selected to teleoperate a highly dexterous surgical robot. The possibility of incorporating touchless interfaces into the surgical arena may provide surgeons with the ability to engage in telerobotic surgery similarly as if they were operating with their bare hands. On the other hand, precision and sensibility may be lost. To explore the advantages and drawbacks of these modalities, five interfaces were selected to send navigational commands to the Taurus robot in the system: Omega, Hydra, and a keyboard. The first represented touch-based, while Leap Motion and Kinect were selected as touchless interfaces. Three experimental designs were selected to test the system, based on standardized surgically related tasks and clinically relevant performance metrics measured to evaluate the user's performance, learning rates, control stability, and interaction naturalness. The current work provides a benchmark and validation framework for the comparison of these two groups of interfaces and discusses their potential for current and future adoption in the surgical setting.

This paper presents a comprehensive evaluation among touchless, vision-based hand tracking interfaces (Kinect and Leap Motion) and the feasibility of their adoption into the surgical theater compared to traditional interfaces.