title: Auto-adaptive robot-aided therapy using machine learning techniques.
abstract: This paper presents an application of a classification method to adaptively and dynamically modify the therapy and real-time displays of a virtual reality system in accordance with the specific state of each patient using his/her physiological reactions. First, a theoretical background about several machine learning techniques for classification is presented. Then, nine machine learning techniques are compared in order to select the best candidate in terms of accuracy. Finally, first experimental results are presented to show that the therapy can be modulated in function of the patient state using machine learning classification techniques.
authors.affiliation: Virtual Reality and Robotics Lab, Biomedical Neuroengineering Universidad Miguel Hernandez de Elche, 03202 Elche, Alicante, Spain(1). Electronic address: fbadesa@umh.es.
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Badesa
authors.affiliation: Virtual Reality and Robotics Lab, Biomedical Neuroengineering Universidad Miguel Hernandez de Elche, 03202 Elche, Alicante, Spain(1). Electronic address: rmorales@umh.es.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Morales
authors.affiliation: Virtual Reality and Robotics Lab, Biomedical Neuroengineering Universidad Miguel Hernandez de Elche, 03202 Elche, Alicante, Spain(1). Electronic address: nicolas.garcia@umh.es.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Garcia-Aracil
authors.affiliation: Virtual Reality and Robotics Lab, Biomedical Neuroengineering Universidad Miguel Hernandez de Elche, 03202 Elche, Alicante, Spain(1). Electronic address: j.sabater@umh.es.
authors.forename: J M
authors.initials: JM
authors.lastname: Sabater
authors.affiliation: Institute for Bioengineering of Catalonia and Universitat Politecnica de Catalunya, BarcelonaTech, Spain(2). Electronic address: alicia.casals@upc.edu.
authors.forename: Alicia
authors.initials: A
authors.lastname: Casals
authors.affiliation: Laboratory of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico di Roma, 00128 Rome, Italy(3). Electronic address: l.zollo@unicampus.it.
authors.forename: Loredana
authors.initials: L
authors.lastname: Zollo
mesh_terms: D000465:Algorithms; D001132:Arm / Q000503:physiopathology; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016002:Discriminant Analysis; D006801:Humans; D016015:Logistic Models; D016571:Neural Networks, Computer; D010291:Paresis / Q000209:etiology / Q000503:physiopathology / Q000534:rehabilitation; D012371:Robotics / Q000379:methods* / Q000706:statistics & numerical data; D020521:Stroke / Q000150:complications / Q000503:physiopathology; D000071939:Stroke Rehabilitation; D060388:Support Vector Machine; D013813:Therapy, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D014584:User-Computer Interface
keywords: Multimodal interfaces; Physiological state; Rehabilitation robotics; Stroke rehabilitation