Browsing by Author "Smailagic, Asim"
Now showing 1 - 7 of 7
Results Per Page
Sort Options
- An exploratory study on techniques for quantitative assessment of stroke rehabilitation exercisesPublication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, SergiTechnology-assisted systems to monitor and assess rehabilitation exercises have an opportunity of enhancing rehabilitation practices by automatically collecting patient’s quantitative performance data. However, even if a complex algorithm (e.g. Neural Network) is applied, it is still challenging to develop such a system due to pa tients with various physical conditions. The system with a complex algorithm is limited to be a black-box system that cannot provide explanations on its predictions. To address these challenges, this paper presents a hybrid model that integrates a machine learn ing (ML) model with a rule-based (RB) model as an explainable artificial intelligence (AI) technique for quantitative assessment of stroke rehabilitation exercises. For evaluation, we collected thera pist’s knowledge on assessment as 15 rules from interviews with therapists and the dataset of three upper-limb stroke rehabilitation exercises from 15 post-stroke and 11 healthy subjects using a Kinect sensor. Experimental results show that a hybrid model can achieve comparable performance with a ML model using Neural Network, but also provide explanations on a model prediction with a RB model. The results indicate the potential of a hybrid model as an explainable AI technique to support the interpretation of a model and fine-tune a model with user-specific rules for personalization.
- Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessmentPublication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, SergiClinical 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.
- Coaching or gaming? Implications of strategy choice for home based stroke rehabilitationPublication . Cameirão, Mónica S.; Smailagic, Asim; Miao, Guangyao; Siewiorek, Dan P.Background: The enduring aging of the world population and prospective increase of age-related chronic diseases urge the implementation of new models for healthcare delivery. One strategy relies on ICT (Information and Communications Technology) home-based solutions allowing clients to pursue their treatments without institutionalization. Stroke survivors are a particular population that could strongly benefit from such solutions, but is not yet clear what the best approach is for bringing forth an adequate and sustainable usage of home-based rehabilitation systems. Here we explore two possible approaches: coaching and gaming. Methods: We performed trials with 20 healthy participants and 5 chronic stroke survivors to study and compare execution of an elbow flexion and extension task when performed within a coaching mode that provides encouragement or within a gaming mode. For each mode we analyzed compliance, arm movement kinematics and task scores. In addition, we assessed the usability and acceptance of the proposed modes through a customized self-report questionnaire. Results: In the healthy participants sample, 13/20 preferred the gaming mode and rated it as being significantly more fun (p < .05), but the feedback delivered by the coaching mode was subjectively perceived as being more useful (p < .01). In addition, the activity level (number of repetitions and total movement of the end effector) was significantly higher (p <.001) during coaching. However, the quality of movements was superior in gaming with a trend towards shorter movement duration (p=.074), significantly shorter travel distance (p <.001), higher movement efficiency (p <.001) and higher performance scores (p <.001). Stroke survivors also showed a trend towards higher activity levels in coaching, but with more movement quality during gaming. Finally, both training modes showed overall high acceptance. Conclusions: Gaming led to higher enjoyment and increased quality in movement execution in healthy participants. However, we observed that game mechanics strongly determined user behavior and limited activity levels. In contrast, coaching generated higher activity levels. Hence, the purpose of treatment and profile of end-users has to be considered when deciding on the most adequate approach for home based stroke rehabilitation.
- A human-ai collaborative approach for clinical decision making on rehabilitation assessmentPublication . Lee, Min Hun; Siewiorek, Daniel P. P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, SergiAdvances 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
- Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessmentPublication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, SergiAutomated assessment of rehabilitation exercises using machine learning has a potential to improve current rehabilitation practices. However, it is challenging to completely replicate therapist’s deci sion making on the assessment of patients with various physical conditions. This paper describes an interactive machine learning approach that iteratively integrates a data-driven model with ex pert’s knowledge to assess the quality of rehabilitation exercises. Among a large set of kinematic features of the exercise motions, our approach identifies the most salient features for assessment using reinforcement learning and generates a user-specific analysis to elicit feature relevance from a therapist for personalized rehabilita tion assessment. While accommodating therapist’s feedback on fea ture relevance, our approach can tune a generic assessment model into a personalized model. Specifically, our approach improves performance to predict assessment from 0.8279 to 0.9116 average F1-scores of three upper-limb rehabilitation exercises (𝑝 < 0.01). Our work demonstrates that machine learning models with feature selection can generate kinematic feature-based analysis as expla nations on predictions of a model to elicit expert’s knowledge of assessment, and how machine learning models can augment with expert’s knowledge for personalized rehabilitation assessment.
- The efficacy of a multicomponent functional fitness program based on exergaming on cognitive functioning of healthy older adults: a randomized controlled trialPublication . Gouveia, Élvio R.; Smailagic, Asim; Ihle, Andreas; Marques, Adilson; Gouveia, Bruna R.; Cameirão, Mónica; Sousa, Honoratoé Santos Correia de; Kliegel, Matthias; Siewiorek, DanielBackground and Objectives: Regular physical exercise can attenuate age-related cognitive decline. This study aimed to investigate the effect of a physical exercise multicomponent training based on exergames on cognitive functioning (CF) in older adults. Research Design and Methods: This randomized controlled trial included older adults aged 61–78. Participants were randomly allocated to an intervention group (IG; n = 15) or active control group (CG; n = 16). The IG was exposed to a combined training with traditional exercise and exergaming, twice a week over a period of 12 weeks. The CG performed only traditional sessions. CF was assessed by the Cognitive Telephone Screening Instrument. The time points for assessment were at zero (pretest), 12 (posttest), and 17 weeks (follow-up). Results: Active CG and IG increased from pretest to posttest in short-term memory (STM), long-term memory (LTM), and Cognitive Telephone Screening Instrument total score 1.98 > Z < 3.00, ps < .005, with moderately large positive effects (.36 > r < .54). A significant increase was seen from posttest to follow-up in STM, Z = 2.74, p = .006, and LTM, Z = 2.31, p < .021, only in IG. Across the two time periods posttest to follow-up, there were significant interaction effects between program type and time for STM (p = .022, η2 p = .17) and LTM (p = .004, η2 p = .25), demonstrating a more beneficial effect of the exergames intervention compared to the CG. Discussion and Implications: The integration of exergaming in a multicomponent functional fitness exercise might have the potential to maintain and improve CF (in particular, STM and LTM) in older adults.
- Towards personalized interaction and corrective feedback of a socially assistive robot for post-stroke rehabilitation therapyPublication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, SergiA 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.