Development of engagement evaluation method and learning mechanism in an engagement enhancing rehabilitation system. (May 2016)

• Record Type: Journal Article
• Authors: Li, Chong; Rusák, Zoltán; Horváth, Imre; Ji, Linhong
• Is Part Of: Engineering applications of artificial intelligence. Volume 51(2016:Mar.)
• Page Start: 182
• Page End: 190
• Publication Date: 2016-05
• Subjects: Cyber-physical stroke rehabilitation system -- Multi-aspect engagement level -- Smart learning mechanism -- Artificial neural network -- Naive Bayes; 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.2016.01.021
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Maintaining and enhancing engagement of patients during stroke rehabilitation exercises are in the focus of current research. There have been various methods and computer supported tools developed for this purpose, which try to avoid mundane exercising that is prone to become a routine or even boring for the patients and leads to ineffective training. This paper proposes a strategy bundle-based smart learning mechanism (SLM) to increase the efficiency of rehabilitation exercises. The underpinning strategy considers motor, perceptive, cognitive and emotional aspects of engagement. Part of a cyber-physical stroke rehabilitation system (CP-SRS), the proposed SLM is able to learn the relationship between the actual engagement levels and applied stimulations. From a computational point of view, the SLM is based on multiplexed signal processing and a machine learning agent. The paper presents the mathematical concepts of signal processing, the reasoning algorithms, and the overall embedding of the SLM in the CP-SRS. Regression and classification are two possible solutions for this learning mechanism. Computer simulation is conducted to investigate the limitations of the proposed learning mechanism and compare the results of different machine learning methods. We simulate regression with artificial neural network (ANN), and classification with ANN and Naive Bayes (NB). Results show that classification with NB is more promising in practice since it is less sensitive to the deviations in the inputs than the applied version of ANN.