Wearable Aromatherapy Feedback System for Sleep Monitoring and Intervention: Using Single-Channel EEG and a Lightweight Model

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.