The figure shows a test example with a single person. It demonstrates that our system tracks the pose as the person enters the room and even when he is fully occluded behind the wall. Top: Images captured by a camera colocated with the radio sensor, and presented here for visual reference. Middle: Keypoint confidence maps extracted from RF signals alone, without any visual input. Bottom: Skeleton parsed from keypoint confidence maps showing that we can use RF signals to estimate the human pose even in the presence of full occlusion.

This thesis 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. I am grateful to the members of the NETMIT for their insightful discussions and to all the human subjects for their participation in our experiments.

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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-theart emotion recognition systems that require a person to be hooked to an ECG monitor.