Multisensor Low-Cost System for Real Time Human Detection and Remote Respiration Monitoring

The most important features of autonomous Search And Rescue robots are abilities to autonomously detect victims and assess their basic vital parameters, such as respiration and heartbeat status, by using their on-board sensors to classify survivors according to their need of medical care. This paper presents a novel sensor composition for autonomous victim detection and non-contact respiration monitoring with SAR robots having limited on-board computational power, using a combination of commercial low-cost components a visual sensor and Ultra-Wide Band radar. In the proposed method, a pretrained neural network (MobileNet) is used to process camera frames and detect human presence in real-time. Once the victim is localized, the radar is used to perform respiration monitoring. The proposed method is evaluated by building a prototype and performing measurements on volunteers in different positions, clothing and amount of subjects in frame.

[1]  Balza Achmad,et al.  Thermal image human detection using Haar-cascade classifier , 2017, 2017 7th International Annual Engineering Seminar (InAES).

[2]  Zheng Liu,et al.  Use of Sparse Representation for Pedestrian Detection in Thermal Images , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[3]  Alexander G. Yarovoy,et al.  Experimental study on human being detection using UWB radar , 2006, 2006 International Radar Symposium.

[4]  Milos Drutarovský,et al.  Short-range UWB radar: Surveillance robot equipment of the future , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[5]  Petar Kormushev,et al.  Casualty Detection from 3D Point Cloud Data for Autonomous Ground Mobile Rescue Robots , 2018, 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[6]  Bernt Schiele,et al.  Vision based victim detection from unmanned aerial vehicles , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Wolfram Burgard,et al.  Deep learning for human part discovery in images , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Zheng Liu,et al.  Pedestrian detection in thermal images using adaptive fuzzy C-means clustering and convolutional neural networks , 2015, 2015 14th IAPR International Conference on Machine Vision Applications (MVA).

[9]  Kun-mu Chen,et al.  An X-Band Microwave Life-Detection System , 1986, IEEE Transactions on Biomedical Engineering.