Validation of the reasoning of an entry-level cyber-physical stroke rehabilitation system equipped with engagement enhancing capabilities. (November 2016)

• Record Type: Journal Article
• Authors: Li, Chong; Rusák, Zoltán; Horváth, Imre; Ji, Linhong
• Is Part Of: Engineering applications of artificial intelligence. Volume 56(2016:Aug.)
• Page Start: 185
• Page End: 199
• Publication Date: 2016-11
• Subjects: Cyber-physical stroke rehabilitation system -- Engagement enhancing -- Reasoning in context -- Stimulation strategy -- Learning mechanism -- Accuracy of suggestions; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.09.006
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Maintaining and enhancing engagement of patients during stroke rehabilitation exercises are in the focus of current research. In the preceding phase of our research, an entry-level cyber-physical stroke rehabilitation system (CP-SRS) has been developed, with the aim of enhancing patients' overall engagement during rehabilitation exercises. As a follow up on the evaluation of the proposed engagement enhancing method and the smart learning mechanism based on the simulated data, this paper presents the validation results of the proposed CP-SRS system based on real-life data. Validation included two aspects: (i) validation of the effectiveness of the applied stimulation strategies (SSs), and (ii) validation of the accuracy of the suggestions of the smart learning mechanism. Methodologically, a within-subject experiment was designed and completed. Eighteen subjects were recruited to participate in the experiments, based on convenience sampling. During the completed game exercises SSs were applied individually as well as in combination. The engagement levels of the participants were evaluated and recorded after applying the SSs individually and combined. The results were processed by within-subject ANOVA in order to test if there was a significant difference between the influences of the different SSs and combinations. In addition, training and testing of the smart learning mechanism (SLM) was also executed in MATLAB. The results indicated that several SSs significantly increased the engagement of the subjects, and that both neural network-based SLM and the Naive Bayes-based SLM were able to learn and discriminate the effects of the various SSs. Our conclusion is that they both can be used to assist making decision on effective application of SSs. However, applying neural network-based SLM is more appropriate in the context of increasing engagement.