L. Nguyen, C. Á. Casado, O. Silvén and M. B. López, "Identification, Activity, and Biometric Classification using Radar-based Sensing," 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), Stuttgart, Germany, 2022, pp. 1-8, doi: 10.1109/ETFA52439.2022.9921651
Identification, activity, and biometric classification using radar-based sensing
|Author:||Nguyen, Le1; Álvarez Casado, Constantino1; Silvén, Olli1;|
1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
2VTT Technical Research Centre of Finland, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023020926619
|Publish Date:|| 2023-02-09
We explore the possibility of leveraging radar-based sensing systems to analyze vital signs for classification, user identification, and regression tasks. Specifically, we extract time-domain and frequency-domain features from distance, respiration, and pulse signals obtained by filtering radio-frequency signals. Our Random Forest classification models are trained on these features to recognize scenarios in which the radar data were collected, categorize individuals into age groups, and classify human activities. For classification, we achieved up to 94.7% of accuracy when distinguishing apnea and normal breathing in the lying position. We then show the feasibility of identifying individuals in a small group using vital signs, which can support model fine-tuning with data acquired from new users. Furthermore, we used a Random Forest regression model to estimate the Body Mass Index, height, and weight of subjects. These classification, identification, and regression models benefit smart systems that can simultaneously identify users, recognize their behaviours, and extract their vital signs from radar sensors.
|Pages:||1 - 8|
2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)
International Conference on Emerging Technologies and Factory Automation
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This research has been supported by the Academy of Finland 6G Flagship program under Grant 346208 and PROFI5 HiDyn under Grant 32629, and the InSecTT project, which is funded under the European ECSEL Joint Undertaking (JU) program under grant agreement No 876038.
|Academy of Finland Grant Number:||
346208 (Academy of Finland Funding decision)
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