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Found 8 papers, 0 papers with code
An Approach for Self-Training Audio Event Detectors Using Web Data
The audio event detectors are trained on the labeled audio and ran on the unlabeled audio downloaded from YouTube.
Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment
Rehabilitation assessment is critical to determine an adequate intervention for a patient.
Designing Personalized Interaction of a Socially Assistive Robot for Stroke Rehabilitation Therapy
The research of a socially assistive robot has a potential to augment and assist physical therapy sessions for patients with neurological and musculoskeletal problems (e. g. stroke).
Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-Stroke Survivors
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction.
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
In this work, we conducted a set of seven design workshops with 35 stakeholders who have been impacted by the child welfare system or who work in it to understand their beliefs and concerns around PRMs, and to engage them in imagining new uses of data and technologies in the child welfare system.
Exploring a Gradient-based Explainable AI Technique for Time-Series Data: A Case Study of Assessing Stroke Rehabilitation Exercises
Explainable artificial intelligence (AI) techniques are increasingly being explored to provide insights into why AI and machine learning (ML) models provide a certain outcome in various applications.
Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises
In this paper, we present our work of iteratively engaging therapists and post-stroke survivors to design, develop, and evaluate a social robot exercise coaching system for personalized rehabilitation.
Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making
Our results showed that the AI model with both salient features and counterfactual explanations assisted therapists and laypersons to improve their performance and agreement level on the task when `right' AI outputs are presented.
