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Article

fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

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Department of Mechatronics and Biomedical Engineering, Air University, Main Campus, PAF Complex, Islamabad 44000, Pakistan
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Department of Mechanical Engineering, Khwaja Fareed University of Engineering & IT, Rahim Yar Khan 64200, Pakistan
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Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
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Institute of Manufacturing, Engineering Management, University of Engineering and Applied Sciences, Swat, Mingora 19060, Pakistan
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Department of Mechanical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt
*
Authors to whom correspondence should be addressed.
Academic Editor: Steve Ling
Sensors 2022, 22(3), 726; https://doi.org/10.3390/s22030726
Received: 26 November 2021 / Revised: 10 January 2022 / Accepted: 12 January 2022 / Published: 18 January 2022
(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)
Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis. View Full-Text
Keywords: artificial neural network (ANN); functional near-infrared spectroscopy (fNIRS); machine learning; upper-limb prosthesis; transhumeral amputee artificial neural network (ANN); functional near-infrared spectroscopy (fNIRS); machine learning; upper-limb prosthesis; transhumeral amputee
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MDPI and ACS Style

Sattar, N.Y.; Kausar, Z.; Usama, S.A.; Farooq, U.; Shah, M.F.; Muhammad, S.; Khan, R.; Badran, M. fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees. Sensors 2022, 22, 726. https://doi.org/10.3390/s22030726

AMA Style

Sattar NY, Kausar Z, Usama SA, Farooq U, Shah MF, Muhammad S, Khan R, Badran M. fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees. Sensors. 2022; 22(3):726. https://doi.org/10.3390/s22030726

Chicago/Turabian Style

Sattar, Neelum Yousaf, Zareena Kausar, Syed Ali Usama, Umer Farooq, Muhammad Faizan Shah, Shaheer Muhammad, Razaullah Khan, and Mohamed Badran. 2022. "fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees" Sensors 22, no. 3: 726. https://doi.org/10.3390/s22030726

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