Evaluation of a Sensor System for Detecting Humans Trapped under Rubble: A Pilot Study

Rapid localization of injured survivors by rescue teams to prevent death is a major issue. In this paper, a sensor system for human rescue including three different types of sensors, a CO2 sensor, a thermal camera, and a microphone, is proposed. The performance of this system in detecting living victims under the rubble has been tested in a high-fidelity simulated disaster area. Results show that the CO2 sensor is useful to effectively reduce the possible concerned area, while the thermal camera can confirm the correct position of the victim. Moreover, it is believed that the use of microphones in connection with other sensors would be of great benefit for the detection of casualties. In this work, an algorithm to recognize voices or suspected human noise under rubble has also been developed and tested.

[1]  Gaurav S. Sukhatme,et al.  Autonomous deployment and repair of a sensor network using an unmanned aerial vehicle , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[2]  Gurkan Tuna,et al.  An autonomous wireless sensor network deployment system using mobile robots for human existence detection in case of disasters , 2014, Ad Hoc Networks.

[3]  Saurabh Kr Srivastava,et al.  Life Detection System during Natural Calamity , 2016, 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT).

[4]  Holly A. Yanco,et al.  Improved interfaces for human-robot interaction in urban search and rescue , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[5]  Alex M. Andrew.  An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods by Nello Christianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, xiii+189 pp., ISBN 0-521-78019-5 (Hbk, £27.50) , 2000, Robotica.

[6]  Changzhi Li,et al.  A Review on Recent Advances in Doppler Radar Sensors for Noncontact Healthcare Monitoring , 2013, IEEE Transactions on Microwave Theory and Techniques.

[7]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[8]  Yong Huang,et al.  Microwave life-detection systems for searching human subjects under earthquake rubble or behind barrier , 2000, IEEE Transactions on Biomedical Engineering.

[9]  Linan Zu,et al.  Microphone array based auditory localization for rescue robot , 2011, 2011 Chinese Control and Decision Conference (CCDC).

[10]  Illah R. Nourbakhsh,et al.  Human-robot teaming for search and rescue , 2005, IEEE Pervasive Computing.

[11]  M Statheropoulos,et al.  The trapped human experiment , 2011, Journal of breath research.

[12]  Mohamed F. Younis,et al.  Strategies and techniques for node placement in wireless sensor networks: A survey , 2008, Ad Hoc Networks.

[13]  Yu Wang,et al.  Robot-Assisted Sensor Network Deployment and Data Collection , 2007, 2007 International Symposium on Computational Intelligence in Robotics and Automation.

[14]  John J. Leonard,et al.  Cooperative concurrent mapping and localization , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[15]  Alper Bozkurt,et al.  Sound Localization Sensors for Search and Rescue Biobots , 2016, IEEE Sensors Journal.

[16]  Claude Sammut,et al.  Effective user interface design for rescue robotics , 2006, HRI '06.

[17]  Yoshito Tobe,et al.  Deployment of Wireless Sensor Network Using Mobile Robots to Construct an Intelligent Environment in a Multi-Robot Sensor Network , 2008 .

[18]  Hugh F. Durrant-Whyte,et al.  Simultaneous Localization and Mapping with Sparse Extended Information Filters , 2004, Int. J. Robotics Res..

[19]  Robin R. Murphy,et al.  Human-robot interaction in rescue robotics , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[20]  P. Rudol,et al.  Human Body Detection and Geolocalization for UAV Search and Rescue Missions Using Color and Thermal Imagery , 2008, 2008 IEEE Aerospace Conference.

[21]  Juan Manuel Górriz,et al.  Voice Activity Detection. Fundamentals and Speech Recognition System Robustness , 2007 .

[22]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[23]  Linan Zu,et al.  An Acoustic Localization System Using Microphone Array for Mobile Robot , 2009 .

[24]  Hwanjo Yu,et al.  SVM Tutorial - Classification, Regression and Ranking , 2012, Handbook of Natural Computing.

[25]  Ivan Figueira,et al.  Rescuers at risk: a systematic review and meta-regression analysis of the worldwide current prevalence and correlates of PTSD in rescue workers , 2012, Social Psychiatry and Psychiatric Epidemiology.