TY - GEN
T1 - A multichannel-near-infrared-spectroscopy-triggered robotic hand rehabilitation system for stroke patients
AU - Lee, Jongseung
AU - Mukae, Nobutaka
AU - Arata, Jumpei
AU - Iwata, Hiroyuki
AU - Iramina, Keiji
AU - Iihara, Koji
AU - Hashizume, Makoto
N1 - Funding Information:
* This research is supported under the Practical Realization Project Against Lifestyle-related Diseases (Cardiovascular Diseases and Diabetes Mellitus) by the Japan Agency for Medical Research and Development, AMED.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/11
Y1 - 2017/8/11
N2 - There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom motion for a hand's closing and opening, is triggered by a wireless command from a NIRS system, capturing a subject's motor cortex activation. To examine the feasibility of the prototype, we conducted a preliminary test involving six neurologically intact participants. The test comprised a series of evaluations for two aspects of neurorehabilitation training in a real-time manner: classification accuracy and execution time. The effects of classification-related factors, namely the algorithm, signal type, and number of NIRS channels, were investigated. In the comparison of algorithms, linear discrimination analysis performed better than the support vector machine in terms of both accuracy and training time. The oxyhemoglobin versus deoxyhemoglobin comparison revealed that the two concentrations almost equally contribute to the hand motion estimation. The relationship between the number of NIRS channels and accuracy indicated that a certain number of channels are needed and suggested a need for a method of selecting informative channels. The computation time of 5.84 ms was acceptable for our purpose. Overall, the preliminary prototype showed sufficient feasibility for further development and clinical testing with stroke patients.
AB - There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom motion for a hand's closing and opening, is triggered by a wireless command from a NIRS system, capturing a subject's motor cortex activation. To examine the feasibility of the prototype, we conducted a preliminary test involving six neurologically intact participants. The test comprised a series of evaluations for two aspects of neurorehabilitation training in a real-time manner: classification accuracy and execution time. The effects of classification-related factors, namely the algorithm, signal type, and number of NIRS channels, were investigated. In the comparison of algorithms, linear discrimination analysis performed better than the support vector machine in terms of both accuracy and training time. The oxyhemoglobin versus deoxyhemoglobin comparison revealed that the two concentrations almost equally contribute to the hand motion estimation. The relationship between the number of NIRS channels and accuracy indicated that a certain number of channels are needed and suggested a need for a method of selecting informative channels. The computation time of 5.84 ms was acceptable for our purpose. Overall, the preliminary prototype showed sufficient feasibility for further development and clinical testing with stroke patients.
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U2 - 10.1109/ICORR.2017.8009239
DO - 10.1109/ICORR.2017.8009239
M3 - Conference contribution
C2 - 28813811
AN - SCOPUS:85034848499
T3 - IEEE International Conference on Rehabilitation Robotics
SP - 158
EP - 163
BT - 2017 International Conference on Rehabilitation Robotics, ICORR 2017
A2 - Ajoudani, Arash
A2 - Artemiadis, Panagiotis
A2 - Beckerle, Philipp
A2 - Grioli, Giorgio
A2 - Lambercy, Olivier
A2 - Mombaur, Katja
A2 - Novak, Domen
A2 - Rauter, Georg
A2 - Rodriguez Guerrero, Carlos
A2 - Salvietti, Gionata
A2 - Amirabdollahian, Farshid
A2 - Balasubramanian, Sivakumar
A2 - Castellini, Claudio
A2 - Di Pino, Giovanni
A2 - Guo, Zhao
A2 - Hughes, Charmayne
A2 - Iida, Fumiya
A2 - Lenzi, Tommaso
A2 - Ruffaldi, Emanuele
A2 - Sergi, Fabrizio
A2 - Soh, Gim Song
A2 - Caimmi, Marco
A2 - Cappello, Leonardo
A2 - Carloni, Raffaella
A2 - Carlson, Tom
A2 - Casadio, Maura
A2 - Coscia, Martina
A2 - De Santis, Dalia
A2 - Forner-Cordero, Arturo
A2 - Howard, Matthew
A2 - Piovesan, Davide
A2 - Siqueira, Adriano
A2 - Sup, Frank
A2 - Lorenzo, Masia
A2 - Catalano, Manuel Giuseppe
A2 - Lee, Hyunglae
A2 - Menon, Carlo
A2 - Raspopovic, Stanisa
A2 - Rastgaar, Mo
A2 - Ronsse, Renaud
A2 - van Asseldonk, Edwin
A2 - Vanderborght, Bram
A2 - Venkadesan, Madhusudhan
A2 - Bianchi, Matteo
A2 - Braun, David
A2 - Godfrey, Sasha Blue
A2 - Mastrogiovanni, Fulvio
A2 - McDaid, Andrew
A2 - Rossi, Stefano
A2 - Zenzeri, Jacopo
A2 - Formica, Domenico
A2 - Karavas, Nikolaos
A2 - Marchal-Crespo, Laura
A2 - Reed, Kyle B.
A2 - Tagliamonte, Nevio Luigi
A2 - Burdet, Etienne
A2 - Basteris, Angelo
A2 - Campolo, Domenico
A2 - Deshpande, Ashish
A2 - Dubey, Venketesh
A2 - Hussain, Asif
A2 - Sanguineti, Vittorio
A2 - Unal, Ramazan
A2 - Caurin, Glauco Augusto de Paula
A2 - Koike, Yasuharu
A2 - Mazzoleni, Stefano
A2 - Park, Hyung-Soon
A2 - Remy, C. David
A2 - Saint-Bauzel, Ludovic
A2 - Tsagarakis, Nikos
A2 - Veneman, Jan
A2 - Zhang, Wenlong
PB - IEEE Computer Society
T2 - 2017 International Conference on Rehabilitation Robotics, ICORR 2017
Y2 - 17 July 2017 through 20 July 2017
ER -