Emotion recognition using wireless signals
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.
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New applications such as tracking and identification of humans (through walls) as well as gesture and emotion recognition could be achieved using ML on radio signals.
“New applications beyond the traditional scope of communications, such as tracking and identification of humans (through walls) [65] as well as gesture and emotion recognition [66], could be achieved using ML on radio signals.”
From An Introduction to Deep Learning for the Physical Layer (2017) · cites this paper as [66]
Cites
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“This approach is more correlated with the person’s inner feelings since it taps into the interaction between the autonomic nervous system and the heart rhythm [51, 35].”
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An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980 · see citing works
Wireless Sensing for Human Activity: A Survey
Liu, J., Liu, H., Chen, Y., Wang, Y., & Wang, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2934489 · Cited by 414 · see citing works
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Hossain, M. S., & Muhammad, G. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2772959 · Cited by 273 · see citing works
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A multimodal emotion recognition method based on facial expressions and electroencephalography
Tan, Y., Sun, Z., Duan, F., Solé-Casals, J., & Caiafa, C. F. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103029 · Cited by 88 · see citing works
CardiacWave: A mmWave-based Scheme of Non-Contact and High-Definition Heart Activity Computing
Xu, C., Li, H., Li, Z., Zhang, H., Rathore, A. S., Chen, X., Wang, K., Huang, M.-C., & Xu, W. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3478127 · Cited by 49 · see citing works
Goshvarpour, A., Abbasi, A., & Goshvarpour, A. (2017) · Australasian Physical & Engineering Sciences in Medicine · doi:10.1007/s13246-017-0571-1 · Cited by 46 · see citing works
Liu, Y., Zhang, S., & Gowda, M. (2021) · Proceedings of the International Conference on Internet-of-Things Design and Implementation · doi:10.1145/3450268.3453537 · Cited by 39 · see citing works
- Authors
- Zhao, M., Adib, F., & Katabi, D.
- Affiliations
- Massachusetts Institute of Technology
- Venue
- Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking
- Publisher
- ACM
- Published
- 2016-10-03
- Pages
- 95-108
- Cited by
- 370
- References
- 6
- Funders
- Lincoln Laboratory, Massachusetts Institute of Technology (6932993)
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