TY - JOUR
T1 - Development of engagement evaluation method and learning mechanism in an engagement enhancing rehabilitation system
AU - Li, Chong
AU - Rusak, Zoltan
AU - Horvath, Imre
AU - Ji, Linhong
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Cyber-physical stroke rehabilitation system
KW - Multi-aspect engagement level
KW - Naive Bayes
KW - Smart learning mechanism
UR - http://www.scopus.com/inward/record.url?scp=84956866887&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2016.01.021
DO - 10.1016/j.engappai.2016.01.021
M3 - Article
SN - 0952-1976
VL - 51
SP - 182
EP - 190
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
ER -