CAE-MAS: Convolutional Autoencoder Interference Cancellation for Multiperson Activity Sensing With FMCW Microwave Radar
Human activity sensing is a crucial component of health monitoring and smart environment applications. Frequency-modulated continuous-wave (FMCW) radars can be used for target tracking, but their collected data are usually accompanied by a significant amount of interference, especially in indoor env...
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| Veröffentlicht in: | IEEE transactions on instrumentation and measurement Jg. 73; S. 1 - 10 |
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The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
2024
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| Abstract | Human activity sensing is a crucial component of health monitoring and smart environment applications. Frequency-modulated continuous-wave (FMCW) radars can be used for target tracking, but their collected data are usually accompanied by a significant amount of interference, especially in indoor environments hosting multiple human subjects, leading to a decrease in accuracy. In this article, we propose a method that compensates that interference and can detect individual activities of multiple humans, overcoming existing methods’ limitation of detecting single human activities. To this end, a range–Doppler map of the data is extracted with an FWCW radar, and the interference effect of this map is mitigated by a convolutional autoencoder (CAE). The CAE network learns to attenuate false-positive regions to strengthen the target areas. This is followed by a Gaussian filter, and then the targets are revealed by applying derivatives on both dimensions of the map. Evaluation results show that our method reaches activity recognition accuracies of 97.13% and 73.37% in the cases of one and two humans, respectively. |
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| AbstractList | Human activity sensing is a crucial component of health monitoring and smart environment applications. Frequency-modulated continuous-wave (FMCW) radars can be used for target tracking, but their collected data are usually accompanied by a significant amount of interference, especially in indoor environments hosting multiple human subjects, leading to a decrease in accuracy. In this article, we propose a method that compensates that interference and can detect individual activities of multiple humans, overcoming existing methods’ limitation of detecting single human activities. To this end, a range–Doppler map of the data is extracted with an FWCW radar, and the interference effect of this map is mitigated by a convolutional autoencoder (CAE). The CAE network learns to attenuate false-positive regions to strengthen the target areas. This is followed by a Gaussian filter, and then the targets are revealed by applying derivatives on both dimensions of the map. Evaluation results show that our method reaches activity recognition accuracies of 97.13% and 73.37% in the cases of one and two humans, respectively. |
| Author | Raeis, Hossein Shirmohammadi, Shervin Kazemi, Mohammad |
| Author_xml | – sequence: 1 givenname: Hossein orcidid: 0000-0002-4447-377X surname: Raeis fullname: Raeis, Hossein organization: Department of Electrical Engineering, University of Isfahan, Isfahan, Iran – sequence: 2 givenname: Mohammad orcidid: 0000-0003-1139-3076 surname: Kazemi fullname: Kazemi, Mohammad organization: Department of Electrical Engineering, University of Isfahan, Isfahan, Iran – sequence: 3 givenname: Shervin orcidid: 0000-0002-3973-4445 surname: Shirmohammadi fullname: Shirmohammadi, Shervin organization: DISCOVER Laboratory, School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada |
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| Title | CAE-MAS: Convolutional Autoencoder Interference Cancellation for Multiperson Activity Sensing With FMCW Microwave Radar |
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