Emotion recognition using wireless signals

By Mingmin Zhao, Fadel M. Adib, Dina Katabi. ACM/IEEE International Conference on Mobile Computing and Networking, 2016

481 citations recorded by the source corpus.

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.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/d0853a92d3c011d7e22fd019ef3f5d4d0d80a1f4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

Device-Free Human Activity Recognition Using Commercial WiFi Devices (2017, background): “It is shown that CSI is also able to detect and recognize small movements such as lip movements [26], keystrokes [7], and heartbeats [36].”
Towards 3D human pose construction using wifi (2020, background): “WiFi Sensing: Recently, a considerable number of investigations are conducted to make use ofWiFi devices to enable various sensing tasks, including indoor localization and tracking [5, 22, 25, 33, 39, 42, 48–50, 52, 53, 65], walking speed estimation [17, 43], breathing and heartbeats inference [6, 5”
Through-Wall Human Pose Estimation Using Radio Signals (2018, background): “Different papers either localize the whole body [5, 23], monitor the person’s walking speed [43, 19], track the chest motion to extract breathing and heartbeats [6, 51, 52], or track the arm motion to identify a particular gesture [32, 26].”
An Introduction to Deep Learning for the Physical Layer (2017, background): “ radio environment needs. New applications beyond the traditional scope of communications, such as tracking and identiﬁcation of humans (through walls) [47] as well as gesture and emotion recognition [48], could be achieved using ML on radio signals. IV. CONCLUSION We have discussed a number of prom”
RF-based 3D skeletons (2018, background): “Different papers localize the people in the environment [2, 17], monitor their walking speed [15, 31], track their chest motion to extract breathing and heartbeats [3, 39, 41], or track the arm motion to identify a particular gesture [21, 23].”
Wireless Sensing for Human Activity: A Survey (2020, unknown): “For instance, the researchers utilize FMCW signals generated by USRP software radio with directional antennas to capture human figure through a wall [103], track user’s 3D motion [28], estimate gait velocity and stride length [104], detect vital signs [56], monitor sleep and insomnia [105], and reco”