Abstract
Scientific mental health crisis analysis through smart Internet of Things (IoT) system is able to provide an overall assessment of personalized emotion state. With recent advancements in IoT technology, we developed an embedded smart IoT system (sEmoD) for emotion detection. We designed and implemented a wearable Cyber-Human System (CHS) with a low power Bluetooth communication module to collect Body Area Sensor's (BAS) data using a smartphone. This wearable IoT device is capable of collecting physiological data over an extended period of time. Experimentation and verification have been conducted on a group of test subjects with different test scenarios including a happy, depressed, stressed, and calm state of being. This study introduces the use of signal processing and machine learning techniques for sensor data analytics to detect a (potential) personalized mental health crisis. The proposed system, sEmoD, can distinguish between the different emotional crises events with a high degree of accuracy (~80%).
Published Version
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