An Intelligent Decision Support System for Stroke Rehabilitation Assessment

Abstract

Rehabilitation assessment is critical to determine an adequate intervention for a patient. However, the current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. In this paper, we identified the needs of therapists to assess patient's functional abilities (e.g. alternative perspective on assessment with quantitative information on patient's exercise motions). As a result, we developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning to assess the quality of motion and summarize patient specific analysis. We evaluated this system with seven therapists using the dataset from 15 patient performing three exercises. The evaluation demonstrates that our system is preferred over a traditional system without analysis while presenting more useful information and significantly increasing the agreement over therapists' evaluation from 0.6600 to 0.7108 F1-scores (p < 0.05). We discuss the importance of presenting contextually relevant and salient information and adaptation to develop a human and machine collaborative decision making system.

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

“…Jung et al [89] on the other hand used model trees [90] and found modest results with an F-measure of 79.29 percent and a ROC of 0.91. Lee, in [91], used a hybrid approach that combines a rulebased knowledge model and a predictive model for classifying the quality of motion as 0, 1, or 2. The findings indicated a good agreement level with the therapists' assessments.…”

Towards Personalized Interaction and Corrective Feedback of a Socially Assistive Robot for Post-Stroke Rehabilitation Therapy

“…For the evaluation, this paper utilizes the dataset of three task-oriented, upper-limb stroke rehabilitation exercises suggested by therapists: 'Bring a cup to the mouth', 'Switch a light on', and 'Move forward a cane' [11]. Fifteen stroke patients with different levels of functional abilities (37 ± 21 Fugl Meyer Scores) and 11 healthy subjects participated in data collection with a Kinect v2 sensor (Microsoft, Redmond, USA) that records the trajectory of body joints at 30 Hz.…”

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

“…A rule-based (RB) model requires the engagement of domain experts to derive a set of monitoring rules [14]. For example, Huang conducted an experiment on whether an authoring tool can support therapists to specify repetitions and joint angles for monitoring knee rehabilitation exercises [12].…”

“…Alternative approach is to utilize a machine learning algorithm with labeled sensor data [14]. Although this approach has the benefit of automatically learn a meaningful function (e.g.…”

“…This paper presents a hybrid model that integrates a data-driven, machine learning (ML) model with a rule-based (RB) model using a weighted average ensemble technique [2,14,16] to assess the quality of motion. For the development, we conducted a semistructured interview with therapists to elicit their knowledge of assessing rehabilitation exercises into 15 rules and collected the dataset of three upper-limb exercises from 15 post-stroke and 11 healthy subjects and the annotations of the dataset from therapists.…”