TY - GEN
T1 - Wearable Aromatherapy Feedback System for Sleep Monitoring and Intervention
T2 - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
AU - Gu, Chengwei
AU - Tian, Fuze
AU - Jiang, Hua
AU - Zhao, Qinglin
AU - Hu, Bin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Sleep is a daily activity essential for well-being, yet many modern individuals experience sleep problems, and prolonged poor sleep negatively impacts both physiological and psychological health. Aromatherapy, an emerging complementary alternative medicine, has shown promise as a sleep aid, but it lacks objective and reliable monitoring and control methods. To address this gap, we propose a wearable, portable sleep monitoring and aromatherapy system that utilizes single-channel electroencephalogram (EEG) signals from the prefrontal lobe for sleep detection and provides aromatherapy feedback based on the detected sleep state. Our system incorporates a lightweight model based on convolutional neural networks (CNN) and long short-term memory (LSTM) networks, enabling on-board execution and classification. Trained on the publicly available Sleep-EDF dataset, the model achieves a classification accuracy of 85.1% and a macro-F1 score of 79.5%. The combination of our developed EEG sensor and the proposed model presents a promising solution for effective sleep monitoring and intervention, aiming to enhance sleep quality.
AB - Sleep is a daily activity essential for well-being, yet many modern individuals experience sleep problems, and prolonged poor sleep negatively impacts both physiological and psychological health. Aromatherapy, an emerging complementary alternative medicine, has shown promise as a sleep aid, but it lacks objective and reliable monitoring and control methods. To address this gap, we propose a wearable, portable sleep monitoring and aromatherapy system that utilizes single-channel electroencephalogram (EEG) signals from the prefrontal lobe for sleep detection and provides aromatherapy feedback based on the detected sleep state. Our system incorporates a lightweight model based on convolutional neural networks (CNN) and long short-term memory (LSTM) networks, enabling on-board execution and classification. Trained on the publicly available Sleep-EDF dataset, the model achieves a classification accuracy of 85.1% and a macro-F1 score of 79.5%. The combination of our developed EEG sensor and the proposed model presents a promising solution for effective sleep monitoring and intervention, aiming to enhance sleep quality.
KW - Aromatherapy
KW - Convolutional Neural Network
KW - Long-Short Term Memory
KW - Sleep
KW - Wearable EEG Sensor
UR - http://www.scopus.com/inward/record.url?scp=85217281922&partnerID=8YFLogxK
U2 - 10.1109/BIBM62325.2024.10822852
DO - 10.1109/BIBM62325.2024.10822852
M3 - Conference contribution
AN - SCOPUS:85217281922
T3 - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
SP - 6392
EP - 6399
BT - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
A2 - Cannataro, Mario
A2 - Zheng, Huiru
A2 - Gao, Lin
A2 - Cheng, Jianlin
A2 - de Miranda, Joao Luis
A2 - Zumpano, Ester
A2 - Hu, Xiaohua
A2 - Cho, Young-Rae
A2 - Park, Taesung
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 December 2024 through 6 December 2024
ER -