Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration

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.Comment: Submitted to IEEE International Conference on Robotics and Automation (ICRA) 201

Spartan Daily February 2, 2011

Volume 136, Issue 3https://scholarworks.sjsu.edu/spartandaily/1110/thumbnail.jp

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

The brain is a prediction machine that cares about good and bad - Any implications for neuropragmatics?

Experimental pragmatics asks how people construct contextualized meaning in communication. So what does it mean for this field to add neuroas a prefix to its name? After analyzing the options for any subfield of cognitive science, I argue that neuropragmatics can and occasionally should go beyond the instrumental use of EEG or fMRI and beyond mapping classic theoretical distinctions onto Brodmann areas. In particular, if experimental pragmatics ‘goes neuro’, it should take into account that the brain evolved as a control system that helps its bearer negotiate a highly complex, rapidly changing and often not so friendly environment. In this context, the ability to predict current unknowns, and to rapidly tell good from bad, are essential ingredients of processing. Using insights from non-linguistic areas of cognitive neuroscience as well as from EEG research on utterance comprehension, I argue that for a balanced development of experimental pragmatics, these two characteristics of the brain cannot be ignored

Diagnosing serious infections in acutely ill children in ambulatory care (ERNIE 2 study protocol, part A): diagnostic accuracy of a clinical decision tree and added value of a point-of-care C-reactive protein test and oxygen saturation

Background: Acute illness is the most common presentation of children to ambulatory care. In contrast, serious infections are rare and often present at an early stage. To avoid complications or death, early recognition and adequate referral are essential. In a recent large study children were included prospectively to construct a symptom-based decision tree with a sensitivity and negative predictive value of nearly 100%. To reduce the number of false positives, point-of-care tests might be useful, providing an immediate result at bedside. The most probable candidate is C-reactive protein, as well as a pulse oximetry. Methods: This is a diagnostic accuracy study of signs, symptoms and point-of-care tests for serious infections. Acutely ill children presenting to a family physician or paediatrician will be included consecutively in Flanders, Belgium. Children testing positive on the decision tree will get a point-of-care C-reactive protein test. Children testing negative will randomly either receive a point-of-care C-reactive protein test or usual care. The outcome of interest is hospital admission more than 24 hours with a serious infection within 10 days. Aiming to include over 6500 children, we will report the diagnostic accuracy of the decision tree (+/- the point-of-care C-reactive protein test or pulse oximetry) in sensitivity, specificity, positive and negative likelihood ratios, and positive and negative predictive values. New diagnostic algorithms will be constructed through classification and regression tree and multiple logistic regression analysis. Discussion: We aim to improve detection of serious infections, and present a practical tool for diagnostic triage of acutely ill children in primary care. We also aim to reduce the number of investigations and admissions in children with non-serious infections