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.

Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review

“…More recently introduced deep learning (DL) models are specific ML applications whose complex algorithms and neural nets (consisting of many hierarchical layersi.e., deep-of non-linear processing units) train models, with little to no explicit human data input. These progressive developments make AI an incredible tool in various fields, including healthcare, where it has been deemed suitable for repetitive analytic tasks [2], complex calculations [3], and complex forecasts [4,5].…”