Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby

Abstract

Robot-assisted rehabilitation systems have shown to be helpful in neuromotor rehabilitation because it is possible to deliver interactive and repeatable sensorimotor exercise and monitor the actual performance continuously. Note that it is also essential to distinguish if subject finds the rehabilitation task difficult or easy, since the difficulty level of a task can yield different emotional state, such as excited, bored, over-stressed, etc., at each subject. It is important to adjust the difficulty level of the task to encourage the non-motivated subjects during the therapy. The physiological measurements, which can be obtained from the biofeedback sensors, can be used to estimate the subject's emotional state during the execution of the rehabilitation task. Machine learning methods can be used to classify the emotional state using the features of the biofeedback sensory data. This is explored in this chapter.