Laboratory of Robotics and Engineering Systems

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Publications

Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment

Publication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, Sergi

Clinical decision support systems have the potential to improve work flows of experts in practice (e.g. therapist’s evidence-based rehabilitation assessment). However, the adoption of these systems is challenging, and the gains of these systems have not fully demonstrated yet. In this paper, we identified the needs of therapists to assess patient’s functional abilities (e.g. alternative perspectives with quantitative information on patient’s exercise motions). As a result, we co-designed and developed an intelligent decision support system that automatically identifies salient features of assessment using reinforcement learning to assess the quality of motion and generate patient-specific analysis. We evaluated this system with seven therapists using the dataset from 15 patients performing three exercises. The results show that therapists have higher usage intent on our system than a traditional system without patient-specific analysis (𝑝 < 0.05). While presenting richer information (𝑝 < 0.10), our system significantly reduces therapists’ effort on assessment (𝑝 < 0.10) and improves their agreement on assessment from 0.66 to 0.71 F1-scores (𝑝 < 0.01). This work discusses the importance of human centered design and development of a machine learning-based decision support system that presents contextually relevant information and salient explanations on its prediction for better adoption in practice.

2020Conference object

Open access

A human-ai collaborative approach for clinical decision making on rehabilitation assessment

Publication . Lee, Min Hun; Siewiorek, Daniel P. P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, Sergi

Advances in artificial intelligence (AI) have made it increasingly applicable to supplement expert’s decision-making in the form of a decision support system on various tasks. For instance, an AI-based system can provide therapists quantitative analysis on patient’s status to improve practices of rehabilitation assessment. However, there is limited knowledge on the potential of these systems. In this paper, we present the development and evaluation of 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 evalu ations with therapists, we found that our system supports thera pists significantly higher agreement on assessment (0.71 average F1-score) than a traditional system without analysis (0.66 average F1-score, p < 0.05). After tuning with therapist’s feedback, our sys tem significantly improves its performance from 0.8377 to 0.9116 average F1-scores (p < 0.01). This work discusses the potential of a human-AI collaborative system to support more accurate decision making while learning from each other’s strengths

2021Conference object

Open access

Evaluation of a low-cost virtual reality surround-screen projection system

Publication . Gonçalves, Afonso; Borrego, Adrián; Latorre, Jorge; Llorens, Roberto; Bermúdez i Badia, Sergi

Two of the most popular mediums for virtual reality are head-mounted displays and surround-screen projection systems, such as CAVE Automatic Virtual Environments. In recent years, HMDs suffered a significant reduction in cost and have become widespread consumer products. In contrast, CAVEs are still expensive and remain accessible to a limited number of researchers. This study aims to evaluate both objective and subjective characteristics of a CAVE-like monoscopic low-cost virtual reality surround-screen projection system compared to advanced setups and HMDs. For objective results, we measured the head position estimation accuracy and precision of a low-cost active infrared (IR) based tracking system, used in the proposed low-cost CAVE, relatively to an infrared marker-based tracking system, used in a laboratory-grade CAVE system. For subjective characteristics, we investigated the sense of presence and cybersickness elicited in users during a visual search task outside personal space, beyond arms reach, where the importance of stereo vision is diminished. Thirty participants rated their sense of presence and cybersickness after performing the VR search task with our CAVE-like system and a modern HMD. The tracking showed an accuracy error of 1.66 cm and .4 mm of precision jitter. The system was reported to elicit presence but at a lower level than the HMD, while causing significant lower cybersickness. Our results were compared to a previous study performed with a laboratory-grade CAVE and support that a VR system implemented with low-cost devices could be a viable alternative to laboratory-grade CAVEs for visual search tasks outside the user’s personal space.

2021Journal article

Open access

Towards personalized interaction and corrective feedback of a socially assistive robot for post-stroke rehabilitation therapy

Publication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, Sergi

A robotic exercise coaching system requires the capability of automatically assessing a patient’s exercise to in teract with a patient and generate corrective feedback. However, even if patients have various physical conditions, most prior work on robotic exercise coaching systems has utilized generic, pre-defined feedback. This paper presents an interactive approach that combines machine learning and rule-based models to automatically assess a patient’s rehabilitation exercise and tunes with patient’s data to generate personalized corrective feedback. To generate feedback when an erroneous motion occurs, our approach applies an ensemble voting method that leverages predictions from multiple frames for frame-level assessment. According to the evaluation with the dataset of three stroke rehabilitation exercises from 15 post-stroke subjects, our interactive approach with an ensemble voting method supports more accurate frame level assessment (p < 0.01), but also can be tuned with held-out user’s unaffected motions to significantly improve the perfor mance of assessment from 0.7447 to 0.8235 average F1-scores over all exercises (p < 0.01). This paper discusses the value of an interactive approach with an ensemble voting method for personalized interaction of a robotic exercise coaching system.

2020Conference object

Open access

Finding the optimal time window for increased classification accuracy during motor imagery

Publication . Blanco-Mora, D. A.; Aldridge, A.; Jorge, C.; Vourvopoulos, A.; Figueiredo, P.; Bermúdez i Badia, S.

Motor imagery classification using electroencephalography is based on feature extraction over a length of time, and different configurations of settings can alter the performance of a classifier. Nevertheless, there is a lack of standardized settings for motor imagery classification. This work analyzes the effect of age on motor imagery training performance for two common spatial pattern-based classifier pipelines and various configurations of timing parameters, such as epochs, windows, and offsets. Results showed significant (p ≤ 0.01) inverse correlations between performance and feature quantity, as well as between performance and epoch/window ratio.

2021Conference object

Open access