Human-AI Collaborative Decision Making
for Rehabilitation Assessment

Rehabilitation monitoring and assessment with sensors and artificial intelligence (AI) provide an opportunity to improve current rehabilitation practices. However, the adoption of such a technology in practice still remains a challenge. This work presents an interactive approach that supports collaboration between a therapist and an AI based system for rehabilitation assessment. An AI-based system can automatically identify salient features of assessment to generate patient-specific analysis for a therapist. After reviewing patient-specific analysis, a therapist can provide feedback to tune an imperfect system.

In two evaluations with therapists, we found that the patient-specific analysis supports significantly higher agreement of therapists' assessment than a traditional system without any analysis. In addition, therapists can provide feature-based feedback to tune a system, which significantly improves its performance from 0.8377 to 0.9116 average F1-scores on three exercises. This work discusses the potential of a human and AI collaborative system that supports more accurate decision making while learning from each other's strength.

2022

Towards Efficient Annotations for a Human-AI Collaborative, Clinical Decision Support System

Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia
ACM International Conference on Intelligent User Interfaces (IUI) 2022

2021

Interactive Hybrid Intelligence Systems for Human-AI/Robot Collaboration

PhD thesis.

A Human-AI Collaborative Approach for Clinical Decision Making on Rehabilitation Assessment

Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia
ACM Conference on Human Factors in Computing Systems (CHI) 2021

2020

Co-Design and Evaluation of an Intelligent Decision Support System for Stroke Rehabilitation Assessment

Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia
ACM on Human-Computer Interaction. 4 (CSCW2):156 2020

Interactive Hybrid Approach to Combine Machine and Human Intelligence for Personalized Rehabilitation Assessment

Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia
ACM Conference on Health, Inference, and Learning (CHIL) 2020

An Exploratory Study on Techniques for Quantitative Assessment of Stroke Rehabilitation Exercises

Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia
ACM Conference on User Modeling, Adaptation and Personalization (UMAP) 2020

2019

Learning to Assess the Quality of Stroke Rehabilitation Exercises

Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia
ACM International Conference on Intelligent User Interfaces (IUI) 2019

An Intelligent Decision Support System for Stroke Rehabilitation Assessment

ACM SIGACCESS Conference on Computers and Accessibility (ASSETS Student Research Competition) 2019

Intelligent Agent for Assessing and Guiding Rehabilitation Exercises

International Joint Conference on Artificial Intelligence (IJCAI Doctoral Consortium) 2019

2018

A Technology for Computer-Assisted Stroke Rehabilitation

ACM International Conference on Intelligent User Interfaces (IUI Student Consortium) 2018