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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Vol.41 No.6
- 발행연도
- 2024.6
- 수록면
- 481 - 488 (8page)
- DOI
- 10.7736/JKSPE.024.040
이용수
초록· 키워드
오류제보하기
Brain-computer interface (BCI) is a technology used in various fields to analyze electroencephalography (EEG) signals to recognize an individual"s intention or state and control a computer or machine. However, most of the research on BCI is on motor imagery, and research on active movement is concentrated on upper limb movement. In the case of lower limb movement, most of the research is on the static state or single movements. Therefore, in this research, we developed a deep-learning model for classifying walking behavior(1: walking, 2: upstairs, 3: downstairs) based on EEG signals in a dynamic environment to verify the possibility of classifying EEG signals in a dynamic state. We developed a model that combined a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM). The model obtained an average recognition performance of 82.01%, with an average accuracy of 93.77% for walking, 76.52% for upstairs, and 75.75% for downstairs. It is anticipated that various robotic devices aimed at assisting people with disabilities and the elderly could be designed in the future with multiple features, such as human-robot interaction, object manipulation, and path-planning utilizing BCI for control.
목차
1. 서론
2. 연구 방법
3. 연구결과
4. 고찰 및 결론
REFERENCES
참고문헌 (24)
참고문헌 신청
Kim, J., Campbell, A. S., de Ávila, B. E.-F., Wang, J., (2019), Wearable biosensors for healthcare monitoring, Nature Biotechnology, 37(4), 389-406.
Kim, J., Koo, B., Nam, Y., Kim, Y., (2021), sEMG-Based hand posture recognition considering electrode shift, feature vectors, and posture groups, Sensors, 21(22), 7681.
Palumbo, A., Vizza, P., Calabrese, B., Ielpo, N., (2021), Biopotential signal monitoring systems in rehabilitation: a review, Sensors, 21(21), 7172.
Huamanchahua, D., Huamán-Lévano, L. A., Asencios-Chávez, J., Caballero-Canchanya, N., (2022), Biological signals for the control of robotic devices in rehabilitation: an innovative review, 2022 IEEE International IOT, Electronics and Mechatronics Conference, 1-7.
Maceira-Elvira, P., Popa, T., Schmid, A. C., Hummel, F. C., (2019), Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment, Journal of Neuroengineering and Rehabilitation, 16(1), 142.
Kim, J., Koo, B., Nam, Y., Kim, Y., (2021), sEMG-Based hand posture recognition considering electrode shift, feature vectors, and posture groups, Sensors, 21(22), 7681.
Palumbo, A., Vizza, P., Calabrese, B., Ielpo, N., (2021), Biopotential signal monitoring systems in rehabilitation: a review, Sensors, 21(21), 7172.
Huamanchahua, D., Huamán-Lévano, L. A., Asencios-Chávez, J., Caballero-Canchanya, N., (2022), Biological signals for the control of robotic devices in rehabilitation: an innovative review, 2022 IEEE International IOT, Electronics and Mechatronics Conference, 1-7.
Maceira-Elvira, P., Popa, T., Schmid, A. C., Hummel, F. C., (2019), Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment, Journal of Neuroengineering and Rehabilitation, 16(1), 142.
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