Personalized Emotion Detection using IoT and Machine Learning
This work addresses personalized emotion monitoring for individuals with autism, but it is incremental as it builds on existing medical IoT solutions.
The paper tackles emotion detection for patients with autism spectrum disorder using a non-invasive IoT system that monitors heart rates and sweat changes, achieving up to 92% accuracy in detecting emotions under normal resting conditions.
The Medical Internet of Things, a recent technological advancement in medicine, is incredibly helpful in providing real-time monitoring of health metrics. This paper presents a non-invasive IoT system that tracks patients' emotions, especially those with autism spectrum disorder. With a few affordable sensors and cloud computing services, the individual's heart rates are monitored and analyzed to study the effects of changes in sweat and heartbeats per minute for different emotions. Under normal resting conditions of the individual, the proposed system could detect the right emotion using machine learning algorithms with a performance of up to 92% accuracy. The result of the proposed approach is comparable with the state-of-the-art solutions in medical IoT.