Skip to main navigation menu Skip to main content Skip to site footer

Articles

Vol. 2 No. 2 (2015)

A UAV for Triage Assessment of Chronic Illness in a GPS-Denied Environment

DOI
https://doi.org/10.15377/2409-9694.2015.02.02.3
Submitted
May 10, 2015
Published
10.05.2015

Abstract

In this paper we conduct an extensive review of the literature toward an autonomous Unmanned Aerial Vehicle (UAV) for application in home healthcare. Based on the research findings, a system is proposed towards such a UAV for the purpose of patient care in an indoor environment, specifically in triage care for people living with chronic conditions. Our system seeks to provide an innovative solution for healthcare at home and to facilitate independent living as well as reduce over triaging through personalized robotics. The development of advanced navigation systems for UAVs has aroused extensive interest recently because of its enormous potential. In comparison to outdoor flight, GPS-denied navigation poses several distinct challenges in stability and control for quadcopter operability, including object detection and avoidance, real-time wireless client-server communications, stability and safety concerns. Medical Decision Support Systems (DSSs), which have been developed largely in the triage component of health assessment, care and decision making, also pose separate research challenges in terms of accuracy, consistency, response (processing) time and degree of automatic operation. As a single system, a drone-based DSS for chronic illness triage assessment poses unique challenges. For this application, the DSS requires voice-based responses, occurring in real-time and classified according to a dynamic and adaptive decision support engine that operates automatically; that is, with no human input and using non-invasive patient analysis. Existing healthcare systems of this nature have not yet been produced. Furthermore, patient recognition through real-time image fused with voice data in a noisy, GPS-denied environment has yet to be achieved. While path planning, navigation, control and stability concerns have been extensively addressed, accuracy for these systems can be improved and the technology as well as applied algorithms must be adapted to application-based requirements, in terms of weight, processing and dedicated communication requirements.

References

  1. Global Burden of Disease Study 2013 Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study Lancet 2015; 386: 743-800.
  2. NSW Health Chronic Disease Management Program Final Report October 2014, available: http://www.health.nsw.gov.au/cdm/Documents/CDMPEvaluation-Report-2014.pdf. Accessed 08/08/2015
  3. Peetoom KKB, Lexis MAS, Joore M, et al. Literature Review on monitoring technologies and their outcomes in independently living elderly people. Disability and Rehabilitation Assistive Technology 2015; 10(4): 271-294. http://dx.doi.org/10.3109/17483107.2014.961179
  4. Sa I, He H, Huyh V and Corke P. Monocular vision based autonomous navigation for a cost-effective MAV in GPSdenied environments. In: Advanced Intelligent Mechatronics 2013 IEEE/ASME 2013; 1355-1360.
  5. Huang S, Bachrach A, Henry P, Krainin M, Maturana D, Fox D, et al. Visual odometry and mapping for autonomous flight using an RGB-D camera, IEEE/ICRA 2011; 1-16.
  6. Leichtfried M, Kaltenriner C, Mossel A and Kaufmann H. Autonomous Flight using a Smartphone as On-Board Processing Unit in GPS-Denied Environments. In: Proceedings of International Conference on Advances in Mobile Computing and Multimedia ACM 2013; 341. http://dx.doi.org/10.1145/2536853.2536898
  7. Kaess M, Ranganathan A and Dellaert F. iSAM: Incremental smoothing and mapping. In: Robotics, IEEE Transactions on 2008; 24(6): 1365-1378. http://dx.doi.org/10.1109/tro.2008.2006706
  8. Kaess M, Johannsson H, Roberts R, Ila V, Leonard J and Dellaert F. iSAM2: Incremental smoothing and mapping with fluid relinearization and incremental variable reordering. In: Robotics and Automation (ICRA), 2011 IEEE International Conference on 2011; 3281-3288.
  9. Kim C, Sakthivel R and Chung WK. Unscented FastSLAM: a robust and efficient solution to the SLAM problem. In: Robotics, IEEE Transactions on 2008; 24(4): 808-820. http://dx.doi.org/10.1109/tro.2008.924946
  10. Engel J, Sturm J and Cremers D. Camera-based navigation of a low-cost quadrocopter. In: Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on 2012; 2815-2821. http://dx.doi.org/10.1109/iros.2012.6385458
  11. Soundararaj S, Sujeeth A and Saxena A. Autonomous indoor helicopter flight using a single onboard camera. In: Intelligent Robots and Systems, 2009 IEEE/RSJ International Conference 2009; 5307-5314. http://dx.doi.org/10.1109/iros.2009.5354617
  12. Beygelzimer A, Kakade S and Langford J. Cover trees for nearest neighbor. In: International Conference on Machine Learning (ICML) 2006; 97-104. http://dx.doi.org/10.1145/1143844.1143857
  13. He Y, Zeng Q, Liu J, Xu G and Deng X. Path planning for indoor UAV based on ant colony optimization. In: the Chinese Control and Decision Conference (CCDC) 2013; 2919-2923. http://dx.doi.org/10.1109/ccdc.2013.6561444
  14. Koenig S and Likhachev M. D* Lite. In: AAAI/IAAI 2002; 476- 483.
  15. Bry A and Roy N. Rapidly-exploring random belief trees for motion planning under uncertainty. In: Robotics and Automation (ICRA), IEEE International Conference on 2011; 723-730. http://dx.doi.org/10.1109/icra.2011.5980508
  16. Ok K, Gamage D, Drummond T, Dellaert F and Roy N. Monocular image space tracking on a computationally limited MAV. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2015) 2015; 6415-6422. http://dx.doi.org/10.1109/ICRA.2015.7140100
  17. Montemerlo M, Thrun S, Koller D and Wegbreit B. FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: AAAI/IAAI 2002; 593-598.
  18. Junior JCV, De Paula JC, Leandro GV and Bonfirm MC. Stability control of a quad-rotor using a PID controller. Brazilian Journal of Instrumentation and Control 2013; 1(1): 15-22. http://dx.doi.org/10.3895/S2318-45312013000100003
  19. Pounds PEI, Bersak DR and Dollar AM. Stability of smallscale UAV helicopters and quad-rotors with added payload mass under PID control. Autonomous Robot 2012; 33(1-2): 129-142. http://dx.doi.org/10.1007/s10514-012-9280-5
  20. Goodwin GC, Graebe SF and Salgado ME. Control system design. Chapter 6, Prentice Hall PTR Upper Saddle River NJ USA 2000; ISBN: 0139586539.
  21. Fu C, Olivares-Mendes MA, Suarez-Fernandez R and Campoy P. Monocular visual-inertial SLAM-based collision avoidance strategy for fail-safe UAV using fuzzy logic controllers. In: Intelligent Robots and Systems, IEEE/RSJ International Conference 2014; 73(1-4): 513-33. http://dx.doi.org/10.1007/s10846-013-9918-3
  22. Olivares-Mendez MA, Campoy P, Mellado-Bataller I and Mejias L. See-and-avoid quadcopter using fuzzy control optimized by cross-entropy. In: Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on 2012; 1-7.
  23. Basri M, Ariffanan KA, Rashid Hussain A and Danapalasingam AR. Design and optimization of backstepping controller for an underactuated autonomous quadrotor unmanned aerial vehicle. In: Transactions of FAMENA 2014; 38(3): 27-44.
  24. Mueller MW and D'Andrea R. Stability and control of a quadrocopter despite the complete loss of one, two, or three propellers. In: IEEE International Conference on Robotics and Automation 2014; 45-52. http://dx.doi.org/10.1109/icra.2014.6906588
  25. Zohar I, Ailon A and Guterman H. An automatic stabilization system for quad-rotors with applications to vertical take-off and landing. Ben-Gurion University of the Negev. BeerSheva 84105 Israel 2012.
  26. Gupte S, Mohandas PIT and Conrad JM. A survey of quadrotor unmanned aerial vehicles. In: Southeastcon, Proceedings of IEEE 2012; 1-6.
  27. Lee GH, Achtelik M, Fraundorfer F, Pollefeys M and Siegwart R. A benchmarking tool for MAV visual pose estimation. In: Control Automation Robotics and Vision (ICARCV), 11th International Conference on. Dec 2010; 1541-1546. http://dx.doi.org/10.1109/icarcv.2010.5707339
  28. Cutler M, Michini B and How JP. Lightweight infrared sensing for relative navigation of quadrotors. In: Unmanned Aircraft Systems (ICUAS). May 2013; 1156-1164. http://dx.doi.org/10.1109/icuas.2013.6564807
  29. Gageik N, Muller T and Montenegro S. Obstacle detection and collision avoidance using ultrasonic distance sensors for an autonomous quadrocopter. University of Wurzburg. Aerospace Information Technology. Wurzburg, Germany. 2012.
  30. Roberts JF, Stirling TS, Zufferey JC and Floreano D. Quadrotor using minimal sensing for autonomous indoor flight. Ecole Polytechnique Federale de Lausanne (EPFL). Lausanne, Switzerland. 2007.
  31. Hehn M and D'Andrea R. Quadrocopter trajectory generation and control. In: IFAC World Congress 2011; 1485-91.
  32. Salaskar P, Paranjpe S, Reddy J and Shah A. Quadcopter - obstacle detection and collision avoidance. In: International Journal of Engineering Trends and Technology (IJETT). Nov 2014; 17: 84-87. http://dx.doi.org/10.14445/22315381/IJETT-V17P218
  33. Singh OG. Self-navigating quadcopter. International Journal of Computer Science and Information Technologies (IJCSIT) 2015; 6(3): 2761-2765.
  34. Heng L, Meier L, Tanskanen P, Fraundorfer F and Pollefeys M. Autonomous obstacle avoidance and maneuvering on a vision-guided MAV using on-board processing. In: Robotics and Automation (ICRA), IEEE International Conference May 2011; 2472-77. http://dx.doi.org/10.1109/icra.2011.5980095
  35. Mohamed A, Xiaodong C and Yuqing G. A Family of stereobased stochastic mapping algorithms for noisy speech recognition. Mihelic F, Zibert J (Eds.). Speech Recognition, Technologies and Application. ISBN: 978-953-7619-29-9. Intech publisher 2008.
  36. Jose MC, Manuel D and Jose MDP. Automated speech recognition in air traffic control (ATC). In: 2012 ATACCS International Conference 2012; 49-51.
  37. Geacăr CM. Reducing pilot/ATC communication errors using voice recognition. In: Proceedings of International Congress of Aeronautical Sciences 2010; 27: 1-7.
  38. Tschirk W. Neural net speech recognizers: voice remote control devices for disabled people. E and I Elektrotechnik und Informationstechnik 2001; 118(7): 367-70. http://dx.doi.org/10.1007/BF03157841
  39. Shafkat K. Speech recognition for robotic control. 2005 Stockholm International Fair. SE-901 87 UMEA, Sweden 2005; 29-36.
  40. Shanthi Therese S and Lingam C. Review of feature extraction techniques in automatic speech recognition. In: International Journal of Scientific Engineering and Technology 2013; 2(6): 479-484.
  41. Muda L, Begam M and Elamvazuthi I. Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. Journal of Computing 2010; 2(3): 138-43.
  42. Ramirez J, Gorriz JM and Segura JC. Voice activity detection: fundamentals and speech recognition system robustness. Robust Speech Recognition and Understanding. Grimm M, Kroschel K (Eds.). 2007 EU 6th Framework Programme. ISBN: 978-3-902613-08-0. Intech Publisher 2007; 10-19. http://dx.doi.org/10.5772/4740
  43. Rotkrantz LJM and Wiggers P. Automatic speech recognition using hidden markov model. Real time AI and Automatische Spraakherkenning 2003; 5-17.
  44. Vanus J, Koziorek J and Hercik R. The design of the voice communication in smart home care. In: IEEE Transactions, 36th International Conference on Telecommunications and Signal Processing (TSP) 2013; 561-564. http://dx.doi.org/10.1109/TSP.2013.6613996
  45. Singh B, Kapur N and Kaur P. Speech recognition with hidden markov model: a review. International Journal of Advanced Research in Computer Science and Software Engineering 2012; 2(3): 400-403.
  46. Surwade SS and Angal YS. Speech recognition using HMM/ANN hybrid model. International Journal on Recent and Innovation Trends in Computing and Communication 2015; 3(6): 4155-4157.
  47. Frikha M and Hamida ABA. Comparative survey of ANN and Hybrid HMM/ANN architectures for robust speech recognition. American Journal of Intelligent Systems 2012; 2-6.
  48. Ananthi S and Dhanalakshmi P. Speech recognition system and isolated word recognition based on hidden markov model for hearing impaired. International Journal of Computer Applications 2013; 73(20): 30-34. http://dx.doi.org/10.5120/13012-0241
  49. Gupta D, Mounima CR, Manjunath N and Manoj PB. Isolated word speech recognition using vector quantization (VQ). International Journal of Advanced Research in Computer Science and Software Engineering 2012; 2(5): 164-166.
  50. Kamale HE and Kawitkar RS. Vector quantization approach for speaker recognition. International Journal of Computer Technology and Electronics Engineering (IJCTEE) 2013; 3: 111-113.
  51. Srivastava N. Speech recognition using artificial neutral network. International Journal of Engineering Science and Innovative Technology (IJESIT) 2014; 3(3): 406-408.
  52. Ryan W and Burk K. Perceptual and acoustic correlates in the speech of males. Journal of Communication Disorders 1974; 7: 181-192. http://dx.doi.org/10.1016/0021-9924(74)90030-6
  53. Gorham-Rowan MM and Laures-Gore J. Acoustic-perceptual correlates of voice quality in elderly men and women. Journal of Communication Disorders 2006; 39: 171-184. http://dx.doi.org/10.1016/j.jcomdis.2005.11.005
  54. Zhao W and Chellappa R, Phillips PJ, Rosenfeld A. Face recognition: a literature survey. In: ACM Computing Surveys (CSUR) 2003; 35(4): 399-458. http://dx.doi.org/10.1145/954339.954342
  55. Kalal Z, Mikolajczyk K and Matas J. Face-TLD: Trackinglearning-detection applied to faces. In: Image Processing (ICIP), 17th IEEE International Conference 2010; 3789-3792.
  56. Blanz V and Vetter T. A morphable model for the synthesis of 3d faces. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques 1999; 187-194. http://dx.doi.org/10.1145/311535.311556
  57. Chellappa R, Wilson C and Sirohey S. Human and Machine Recognition of Faces: A Survey. In: Proceedings of the IEEE 1995; 83(5): 705-741. http://dx.doi.org/10.1109/5.381842
  58. Sung KK and Poggio T. Example-based learning for viewbased human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998; 20(1): 39-51. http://dx.doi.org/10.1109/34.655648
  59. Osuna E, Freund R and Girosi F. Training support vector machines: an application to face detection. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 1997; 130-136. http://dx.doi.org/10.1109/CVPR.1997.609310
  60. Romdhani S, Torr P, Scholkopf B and Blake A. Computationally efficient face detection. In Proceedings of the International Conference on Computer Vision (ICCV) 2001; 2, 695-700. http://dx.doi.org/10.1109/iccv.2001.937694
  61. Rowley HA and Kanade T. Neural network -based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998; 20(1): 23-38. http://dx.doi.org/10.1109/34.655647
  62. Schneiderman H and Kanade T. Object detection using the statistics of parts. International Journal of Computer Vision 2004; 56(3): 151-177. http://dx.doi.org/10.1023/B:VISI.0000011202.85607.00
  63. Viola P and Jones M. Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, Proceedings of 2001 IEEE Computer Society Conference on 2001; 1(I-511). http://dx.doi.org/10.1109/cvpr.2001.990517
  64. Miller R, McNeil M, Challinor S, Masarie F and Myers J. The INTERNIST-i/QUICK medical reference project status report. The Western Journal of Medicine 1986; 145(6): 816-822.
  65. Shortliffe E, Scott A, Bischoff M, Melle W and Jacobs C. ONCOCIN: an expert system for oncology protocol management. In: 7th International Joint Conference on Artificial Intelligence (IJCAI '81) 1982; 876-881.
  66. Shortliffe T and Davis R. Some considerations for the implementation of knowledge-based expert systems. ACM SIGART Bulletin 1975; 55: 9-12. http://dx.doi.org/10.1145/1045253.1045254
  67. Barnett G, Hoffer E, Cimino J and Hupp J. DXplain: experience with knowledge acquisition and program evaluation. Proceedings of the Annual Symposium on Computer Applications in Medical Care 1987; 150-154. http://dx.doi.org/10.1001/jama.1987.03400010071030
  68. Padmanabhan N, Burstein F, Churilov L, Wassertheil J, Hornblower B and Parker N. A mobile emergency triage decision support system evaluation. Proceedings of the 39th Hawaii International Conference on System Sciences 2006; 1-10. http://dx.doi.org/10.1109/hicss.2006.17
  69. Yilmaz A and Ayan K. Cancer risk analysis by fuzzy logic approach and performance status of the model. Turkish Journal of Electrical Engineering and Computer Sciences 2013; 21(3): 897-912.
  70. Georgopoulos VC and Stylios CD. Introducing fuzzy cognitive maps for developing decision support system for triage at emergency room admissions for the elderly. In: Fuzziness and Medicine: Philosophical Reflections and Application Systems in Health Care 2013; 415-436.