Hybrid Emotion-Aware Monitoring System Based on Brainwaves for Internet of Medical Things

Driven by an increasing number of connected medical devices, Internet of Medical Things (IoMT), as an application of Internet of Things (IoT) in healthcare, is developed to help collect, analyze, and transmit medical data. During the outbreak of a pandemic like COVID-19, IoMT can be useful to monitor the status of patients and detect main symptoms remotely, by using various smart sensors. However, due to the lack of emotional care in the current IoMT, it is still a challenge to reach an efficient medical process. Especially under COVID-19, there is a need to monitor emotional status among particular people like the elderly. In this work, we propose an emotion-aware healthcare monitoring system in IoMT, based on brainwaves. With the fast development of electroencephalography (EEG) sensors in current headsets and some devices, brainwave-based emotion detection becomes feasible. The IoMT devices are used to capture the brainwaves of a patient in a scenario of smart home. Also, our system involves the analysis of touch behavior as the second layer to enhance the brainwave-based emotion recognition. In the user study with 60 participants, the results indicate the viability and effectiveness of our approach in detecting emotions like comfortable and uncomfortable, which can complement existing emotion-aware healthcare applications and mechanisms.