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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Jurnal Matematika Sains dan Teknologi Jurnal Pengabdian Magister Pendidikan IPA Darmabakti Cendekia: Journal of Community Service and Engagements Indonesian Journal of Electrical Engineering and Computer Science Indonesian Applied Physics Letters Medika Respati : Jurnal Ilmiah Kesehatan
Khusnul Ain
Department Of Physics, Faculty Of Science And Technology, Universitas Airlangga
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Brain-computer interface-based hand exoskeleton with bidirectional long short-term memory methods Osmalina Nur Rahma; Khusnul Ain; Alfian Pramudita Putra; Riries Rulaningtyas; Khouliya Zalda; Nita Lutfiyah; Nafisa Rahmatul Laili Alami; Rifai Chai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp173-185

Abstract

It takes at least 3 months to restore hand and arm function to 70% of its original value. This condition certainly reduces the quality of life for stroke survivors. The effectiveness in restoring the motor function of stroke survivors can be improved through rehabilitation. Currently, rehabilitation methods for post-stroke patients focus on repetitive movements of the affected hand, but it is often stalled due to the lack of professional rehabilitation personnel. This research aims to design a brain-computer interface (BCI)-based exoskeleton hand motion control for rehabilitation devices. The Bidirectional long short-term memory (Bi-LSTM) method performs motion classification for the ESP32 microcontroller to control the movement of the DC motor on the exoskeleton hand in real-time. The statistical features, such as mean and standard deviation from the sliding windows process of electroencephalograph (EEG) signals, are used as the input for Bi-LSTM. The highest accuracy at the validation stage was obtained in the combination of mean and standard deviation features, with the highest accuracy of 91% at the offline testing stage and reaching an average of 90% in real-time (80%-100%). Overall, the control system design that has been made runs well to perform movements on the hand exoskeleton based on the classification of opening and grasping movements.
Co-Authors Abidah Alfi Maritsa Agus Rubiyanto Alfian Pramudita Putra Amillia Kartika Sari Amirul Amalia Arfan Eko Fahrudin Bambang Haris Betty Rahayuingsih David Buntoro Kamandjaja Deddy Kurniadi Dewi Masykuriyah Endarko Endarko Febriana Dwi Rahmadani Hanif Assyarify Iis Purwanti Imam Sapuan Khouliya Zalda Lailatul Muqmiroh Limajatini, Limajatini Lina Choridah Miranda Syafira Widyananda Muhammad Fadhel Maulama Nafisa Rahmatul Laili Alami Nita Lutfiyah Osmalina Nur Rahma Pramulen, Aji Sapta R Arif Wibowo Rifai Chai Riries Rulaningtyas Soegianto Soelistiono Solly Aryza Suprayitno Suprayitno Suryani Dyah Astuti Weni Endahing Warni Yusrinourdi Muhammad Zuchruf