Auto-adaptive robot-aided therapy using machine learning techniques

Computer Methods and Programs in Biomedicine

Volumn 116, Issue 2, 2014, Pages 123-130

  Badesa, Francisco J ^a   Morales, Ricardo ^a   Garcia Aracil, Nicolas ^a   Sabater, J M ^a   Casals, Alicia ^b   Zollo, Loredana ^c  

^a MIGUEL HERNÁNDEZ UNIVERSITY   (Spain)

^b UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^c UNIVERSITY CAMPUS BIO MEDICO   (Italy)

Author keywords

Multimodal interfaces; Physiological state; Rehabilitation robotics; Stroke rehabilitation

Indexed keywords

ARTIFICIAL INTELLIGENCE; PATIENT TREATMENT; PHYSIOLOGY; ROBOTICS; VIRTUAL REALITY;

CLASSIFICATION METHODS; MACHINE LEARNING CLASSIFICATION; MACHINE LEARNING TECHNIQUES; MULTI-MODAL INTERFACES; PHYSIOLOGICAL REACTIONS; PHYSIOLOGICAL STATE; REHABILITATION ROBOTICS; STROKE REHABILITATION;

LEARNING SYSTEMS;

ACCURACY; ARTICLE; BAYESIAN LEARNING; CLASSIFICATION; DISCRIMINANT ANALYSIS; HUMAN; K NEAREST NEIGHBOR; LEARNING ALGORITHM; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; PERCEPTRON; REHABILITATION CARE; ROBOTICS; SUPPORT VECTOR MACHINE; VIRTUAL REALITY; ALGORITHM; ARM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; COMPLICATION; COMPUTER ASSISTED THERAPY; COMPUTER INTERFACE; PARESIS; PATHOPHYSIOLOGY; PROCEDURES; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; STROKE;

ALGORITHMS; ARM; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DISCRIMINANT ANALYSIS; HUMANS; LOGISTIC MODELS; NEURAL NETWORKS (COMPUTER); PARESIS; ROBOTICS; STROKE; SUPPORT VECTOR MACHINES; THERAPY, COMPUTER-ASSISTED; USER-COMPUTER INTERFACE;

EID: 84903134879     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2013.09.011     Document Type: Article

References (16)

1. Kwakkel G., Kollen B.J., Kreb H.I. Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabilitation & Neural Repair 2008, 22:111-121.
2. Norouzi-Gheidari N., Archambault P.S., Fung J. Effects of robot-assisted therapy on stroke rehabilitation in upper limbs: systematic review and meta-analysis of the literature. Journal of Rehabilitation Research and Development 2012, 49(4):479-496.
3. Miller E.L., Murray L., Richards L., Zorowitz R.D., Bakas T., Clark P., Billinger S.A. Comprehensive overview of nursing and interdisciplinary rehabilitation care of the stroke patient: a scientific statement from the American Heart Association. Stroke 2010, 41(10):2402-2448.
4. Duschau-Wicke A., Caprez A., Riener R. Patient-cooperative control increases active participation of individuals with SCI during robot-aided gait training. Journal of NeuroEngineering and Rehabilitation (JNER) 2010, 7:43.
5. Riener R., Munih M. Guest editorial special section on rehabilitation via bio-cooperative control. IEEE Transactions on Neural Systems and Rehabilitation Engineering Aug. 2010, 18(4):337-338.
6. Byrne E.A., Parasuraman R. Psychophysiology and adaptive automation. Biological Psychology 1996, 42:249-268.
7. Novak D., Mihelj M., Munih M. A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing. Interacting with Computers 2012, 24:154-172.
8. Harper P.R. A review and comparison of classification algorithms for medical decision making. Health Policy 2005, 71:315-331.
9. Novak D., Mihelj M., Ziherl J., Olensek A., et al. Psychophysiological measurements in a biocooperative feedback loop for upper extremity rehabilitation?. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2011, 19(4):400-410.
10. Koenig A., Omlin X., Zimmerli L., Sapa M., Krewer C., Bolliger M., Müller F., Riener R. Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation. Journal of Rehabilitation Research and Development 2011, 48(4):367-386.
11. Bishop C.M. Pattern Recognition and Machine Learning 2006, Springer-Verlag New York, Inc., Secaucus, NJ, USA.
12. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. Data Mining, Inference and Prediction, Springer Series in Statistics 2001, Springer-Verlag, New York, NY.
13. Abu-Mostafa Y.S., Magdon-Ismail M., Lin H.-T. Learning from Data: A Short Course 2012, AMLBook, ISBN-13: 978-1600490064, March.
14. Gallant S.I. Perceptron-based learning algorithms. IEEE Transactions on Neural Networks 1990, 1:179-191.
15. Badesa F.J., Morales R., Garcia Aracil N., Sabater J.M., Perez-Vidal C., Fernandez E. Multimodal interfaces to improve therapeutic outcomes in robot-assisted rehabilitation. IEEE Transactions on Systems, Man, and Cybernetics, Part C 2012, 42(6):1152-1158.
16. Novak D., Mihelj M., Munih M. Psychophysiological responses to different levels of cognitive and physical workload in haptic interaction. Robotica 2011, 29:367-374.