An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems

• URL: http://arxiv.org/abs/2209.13043v1
• Date: Mon, 26 Sep 2022 21:53:01 GMT
• Title: An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems
• Authors: Fabio Aurelio D'Asaro, Luca Raggioli, Salim Malek, Marco Grazioso, Silvia Rossi
• Abstract summary: We present a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child's current level of cognitive and behavioral performance.
• Score: 1.7761842246724584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child's current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) and take decisions accordingly. These decisions are typically aimed at improving the child's performance by triggering appropriate re-engagement stimuli when their attention is low, by changing the game or making it more difficult when the child is losing interest in the task as it is too easy. Alongside state-of-the-art techniques for emotion recognition and head pose estimation, we use a runtime variant of a probabilistic and epistemic logic programming dialect of the Event Calculus, known as the Epistemic Probabilistic Event Calculus. In particular, the probabilistic component of this symbolic framework allows for a natural interface with the machine learning techniques. We overview the architecture and its components, and show some of its characteristics through a discussion of a running example and experiments. Under consideration for publication in Theory and Practice of Logic Programming (TPLP).

Related papers

• On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms [9.071347361654931]
  We assess the assurance of end-to-end fully differentiable neurosymbolic systems that are an emerging method to create data-efficient models. We find end-to-end neurosymbolic methods present unique opportunities for assurance beyond their data efficiency.
  arXiv  Detail & Related papers  (2025-02-13T03:29:42Z)
• Auto Detecting Cognitive Events Using Machine Learning on Pupillary Data [0.0]
  Pupil size is a valuable indicator of cognitive workload, reflecting changes in attention and arousal governed by the autonomic nervous system. This study explores the potential of using machine learning to automatically detect cognitive events experienced using individuals.
  arXiv  Detail & Related papers  (2024-10-18T04:54:46Z)
• IID Relaxation by Logical Expressivity: A Research Agenda for Fitting Logics to Neurosymbolic Requirements [50.57072342894621]
  We discuss the benefits of exploiting known data dependencies and distribution constraints for Neurosymbolic use cases. This opens a new research agenda with general questions about Neurosymbolic background knowledge and the expressivity required of its logic.
  arXiv  Detail & Related papers  (2024-04-30T12:09:53Z)
• TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning [8.129782272731397]
  We propose a novel encoding framework that relies on self-supervised learning with momentum contrast to learn representations from various physiological domains without needing labels. We show that our self-supervised learning approach can indeed learn discriminative features which can be exploited in downstream classification tasks.
  arXiv  Detail & Related papers  (2023-06-10T21:17:42Z)
• NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants [73.85768093666582]
  We propose an explainable geometric deep network dubbed NeuroExplainer. NeuroExplainer is used to uncover altered infant cortical development patterns associated with preterm birth.
  arXiv  Detail & Related papers  (2023-01-01T12:48:12Z)
• Vision-Based Activity Recognition in Children with Autism-Related Behaviors [15.915410623440874]
  We demonstrate the effect of a region-based computer vision system to help clinicians and parents analyze a child's behavior. The data is pre-processed by detecting the target child in the video to reduce the impact of background noise. Motivated by the effectiveness of temporal convolutional models, we propose both light-weight and conventional models capable of extracting action features from video frames.
  arXiv  Detail & Related papers  (2022-08-08T15:12:27Z)
• Cross Your Body: A Cognitive Assessment System for Children [5.279475826661643]
  We created a system called Cross-Your-Body and recorded data, which is imprecise in several aspects. The videos capture real-world usage, as they record children performing tasks during real-world assessment by psychologists. It is our goal that this system will be useful in advancing research in cognitive assessment of kids.
  arXiv  Detail & Related papers  (2021-11-24T22:38:07Z)
• Online Learning Probabilistic Event Calculus Theories in Answer Set Programming [70.06301658267125]
  Event Recognition (CER) systems detect occurrences in streaming time-stamped datasets using predefined event patterns. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of rules weighted in the Event Calculus. Our results demonstrate the superiority of our novel approach, both terms efficiency and predictive.
  arXiv  Detail & Related papers  (2021-03-31T23:16:29Z)
• Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution [97.50813120600026]
  Spatial-temporal reasoning is a challenging task in Artificial Intelligence (AI) Recent works have focused on an abstract reasoning task of this kind -- Raven's Progressive Matrices ( RPM) We propose a neuro-symbolic Probabilistic Abduction and Execution learner (PrAE) learner.
  arXiv  Detail & Related papers  (2021-03-26T02:42:18Z)
• A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits [91.3755431537592]
  Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neuro-robotics.
  arXiv  Detail & Related papers  (2020-03-23T14:55:00Z)
• Relational Neural Machines [19.569025323453257]
  This paper presents a novel framework allowing jointly train the parameters of the learners and of a First-Order Logic based reasoner. A Neural Machine is able recover both classical learning results in case of pure sub-symbolic learning, and Markov Logic Networks. Proper algorithmic solutions are devised to make learning and inference tractable in large-scale problems.
  arXiv  Detail & Related papers  (2020-02-06T10:53:57Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.