Min Lee

Talk Title: Human-AI Collaborative Decision Making on Rehabilitation Assessment

Talk Abstract: Rehabilitation monitoring systems with sensors and artificial intelligence (AI) provide an opportunity to improve current rehabilitation practices by automatically collecting quantitative data on patient’s status. However, the adoption of these systems still remains a challenge. This paper presents an interactive AI-based system that supports collaborative decision making with therapists for rehabilitation assessment. This system automatically identifies salient features of assessment to generate patient-specific analysis for therapists, and tunes with their feedback. In two evaluations with therapists, we found that our system supports therapists significantly higher agreement on assessment (0.71 average F1-score) than a traditional system without analysis (0.66 average F1-score, p < 0.05). In addition, after tuning with therapist’s feedback, our system significantly improves its performance (from 0.8377 to 0.9116 average F1-scores, p < 0.01). This work discusses the potential of a human and AI collaborative system that supports more accurate decision making while learning from each other’s strengths.

Bio: Min Lee is a PhD student at Carnegie Mellon University. His research interests lie at the intersection of human-computer interaction (HCI) and machine learning (ML), where he designs, develops, and evaluates human-centered ML systems to address societal problems. His thesis focuses on creating interactive hybrid intelligence systems to improve the practices of stroke rehabilitation (e.g. a decision support system for therapists and a robotic coaching system for post-stroke survivors).