Zhao, M., Adib, F., & Katabi, D. (2018).
Communications of the ACM, 61(9), 91–100.
Abstract
This paper demonstrates a new technology that can infer a person's emotions from RF signals reflected off his body. EQ-Radio transmits an RF signal and analyzes its reflections off a person's body to recognize his emotional state (happy, sad, etc.). The key enabler underlying EQ-Radio is a new algorithm for extracting the individual heartbeats from the wireless signal at an accuracy comparable to on-body ECG monitors. The resulting beats are then used to compute emotion-dependent features which feed a machine-learning emotion classifier. We describe the design and implementation of EQ-Radio, and demonstrate through a user study that its emotion recognition accuracy is on par with state-of-the-art emotion recognition systems that require a person to be hooked to an ECG monitor.
Here are some thoughts:
First, if you are prone to paranoia, please stop here.
The research introduces EQ-Radio, a system developed by MIT CSAIL that uses wireless signals to detect and classify human emotions such as happiness, sadness, anger, and excitement. By analyzing subtle changes in heart rate and breathing patterns through radio frequency reflections, EQ-Radio achieves 87% accuracy in emotion classification without requiring subjects to wear sensors or act emotionally. This non-invasive, privacy-preserving method outperforms video- and audio-based emotion recognition systems and works even when people are moving or located in different rooms.