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Computer Vision-based Fall Detection Methods Using The Kinect Camera: A Survey


Affiliations
1 Department of Computer Science, King Abdelaziz University, Jeddah, Saudi Arabia
 

Disabled people can overcome their disabilities in carrying out daily tasks in many facilities [1]. However, they frequently report that they experience difficulty being independently mobile. And even if they can, they are likely to have some serious accidents such as falls. Furthermore, falls constitute the second leading cause of accidental or injury deaths after injuries of road traffic which call for efficient and practical/comfortable means to monitor physically disabled people in order to detect falls and react urgently. Computer vision (CV) is one of the computer sciences fields, and it is actively contributing in building smart applications by providing for image\video content “understanding.” One of the main tasks of CV is detection and recognition. Detection and recognition applications are various and used for different purposes. One of these purposes is to help of the physically disabled people who use a cane as a mobility aid by detecting the fall. This paper surveys the most popular approaches that have been used in fall detection, the challenges related to developing fall detectors, the techniques that have been used with the Kinect in fall detection, best points of interest (joints) to be tracked and the well-known Kinect-Based Fall Datasets. Finally, recommendations and future works will be summarized.

Keywords

Fall Detection, Kinect Camera, Physically Disabled People, Mobility Aid Systems.
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  • P. Rashidi and A. Mihailidis, "A survey on ambient-assisted living tools for older adults," Biomedical and Health Informatics, IEEE Journal of, vol. 17, pp. 579-590, 2013.

  • E. Melis, R. Torres-Moreno, H. Barbeau, and E. Lemaire, "Analysis of assisted-gait characteristics in persons with incomplete spinal cord injury," Spinal Cord, vol. 37, pp. 430-439, 1999.

  • E. Dean and J. Ross, "Relationships among cane fitting, function, and falls," Physical therapy, vol.

  • , pp. 494-494, 1993.

  • L. P. Fried, L. Ferrucci, J. Darer, J. D. Williamson, and G. Anderson, "Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care," The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, vol. 59, pp. M255-M263, 2004.

  • M. Mubashir, L. Shao, and L. Seed, "A survey on fall detection: Principles and approaches,"

  • Neurocomputing, vol. 100, pp. 144-152, 2013.

  • A. Bulling, U. Blanke, and B. Schiele, "A tutorial on human activity recognition using body-worn inertial sensors," ACM Computing Surveys (CSUR), vol. 46, p. 33, 2014.

  • R. Igual, C. Medrano, and I. Plaza, "Challenges, issues and trends in fall detection systems," Biomed. Eng. Online, vol. 12, pp. 1-66, 2013.

  • Z. Zhang, C. Conly, and V. Athitsos, "A survey on vision-based fall detection," in Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2015, p. 46.

  • N. Noury, A. Fleury, P. Rumeau, A. Bourke, G. Laighin, V. Rialle, et al., "Fall detection-principles and methods," in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 1663-1666.

  • Y. Li, D. Zhou, X. Wei, Q. Zhang, and X. Yang, "Key Frames Extraction of Human Motion Capture Data Based on Cosine Similarity," presented at the The 30th International Conference on Computer Animation and Social Agents (CASA 2017), Seoul, South Korea, 2017.

  • C. K. Lee and V. Y. Lee, "Fall detection system based on kinect sensor using novel detection and posture recognition algorithm," in Inclusive Society: Health and Wellbeing in the Community, and Care at Home, ed: Springer, 2013, pp. 238-244.

  • B. Kwolek and M. Kepski, "Human fall detection on embedded platform using depth maps and wireless accelerometer," Computer methods and programs in biomedicine, vol. 117, pp. 489-501, 2014.

  • E. E. Stone and M. Skubic, "Fall detection in homes of older adults using the Microsoft Kinect,"

  • Biomedical and Health Informatics, IEEE Journal of, vol. 19, pp. 290-301, 2015.

  • F. Abdali-Mohammadi, M. Rashidpour, and A. Fathi, "Fall Detection Using Adaptive Neuro-Fuzzy Inference System," International Journal of Multimedia and Ubiquitous Engineering, vol. 11, pp. 91106, 2016.

  • O. D. Lara and M. A. Labrador, "A survey on human activity recognition using wearable sensors,"

  • Communications Surveys & Tutorials, IEEE, vol. 15, pp. 1192-1209, 2013.

  • M.-C. Giuroiu and T. Marita, "Gesture recognition toolkit using a Kinect sensor," in Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on, 2015, pp. 317-324.

  • Z. Zhang, "Microsoft kinect sensor and its effect," MultiMedia, IEEE, vol. 19, pp. 4-10, 2012.

  • H.-H. Wu and A. Bainbridge-Smith, "Advantages of using a Kinect Camera in various applications,"

  • University of Canterbury, 2011.

  • Z. Cai, J. Han, L. Liu, and L. Shao, "RGB-D datasets using microsoft kinect or similar sensors: a survey," Multimedia Tools and Applications, pp. 1-43, 2016.

  • B. Langmann, K. Hartmann, and O. Loffeld, "Depth Camera Technology Comparison and Performance Evaluation," in ICPRAM (2), 2012, pp. 438-444.

  • M. L. Anjum, O. Ahmad, S. Rosa, J. Yin, and B. Bona, "Skeleton tracking based complex human activity recognition using kinect camera," in Social Robotics, ed: Springer, 2014, pp. 23-33.

  • J. Han, L. Shao, D. Xu, and J. Shotton, "Enhanced computer vision with microsoft kinect sensor: A review," Cybernetics, IEEE Transactions on, vol. 43, pp. 1318-1334, 2013.

  • J. Smisek, M. Jancosek, and T. Pajdla, "3D with Kinect," Consumer Depth Cameras for Computer Vision, pp. 3-25, 2013.

  • S. Zennaro, M. Munaro, S. Milani, P. Zanuttigh, A. Bernardi, S. Ghidoni, et al., "Performance evaluation of the 1st and 2nd generation Kinect for multimedia applications," in Multimedia and Expo (ICME), 2015 IEEE International Conference on, 2015, pp. 1-6.

  • C. Sinthanayothin, N. Wongwaen, and W. Bholsithi, "Skeleton Tracking using Kinect Sensor & Displaying in 3D Virtual Scene," International Journal of Advancements in Computing Technology, vol. 4, 2012.

  • K. K. Biswas and S. K. Basu, "Gesture recognition using microsoft kinect®," in Automation, Robotics and Applications (ICARA), 2011 5th International Conference on, 2011, pp. 100-103.

  • Z. Sun, S. Tang, H. Huang, Z. Zhu, H. Guo, Y.-e. Sun, et al., "SOS: Real-time and accurate physical assault detection using smartphone," Peer-to-Peer Networking and Applications, 2016.

  • Z. Zhu, H. Guo, and Y.-e. Sun, "iProtect: Detecting Physical Assault Using Smartphone," in Wireless Algorithms, Systems, and Applications: 10th International Conference, WASA 2015, Qufu, China, August 10-12, 2015, Proceedings, 2015, p. 477.

  • G. Mastorakis and D. Makris, "Fall detection system using Kinect’s infrared sensor," Journal of Real-Time Image Processing, vol. 9, pp. 635-646, 2014.

  • F. Abdali-Mohammadi, M. Rashidpour, and A. Fathi, "Fall Detection Using Adaptive Neuro-Fuzzy Inference System," 2016.

  • C. Kawatsu, J. Li, and C. Chung, "Development of a fall detection system with Microsoft Kinect," in Robot Intelligence Technology and Applications 2012, ed: Springer, 2013, pp. 623-630.

  • Z.-P. Bian, J. Hou, L.-P. Chau, and N. Magnenat-Thalmann, "Fall detection based on body part tracking using a depth camera," IEEE journal of biomedical and health informatics, vol. 19, pp. 430439, 2015.

  • T.-L. Le and J. Morel, "An analysis on human fall detection using skeleton from Microsoft Kinect,"

  • in Communications and Electronics (ICCE), 2014 IEEE Fifth International Conference on, 2014, pp.

  • -489.

  • C. Maldonado, H. Ríos, E. Mezura-Montes, and A. Marin, "Feature selection to detect fallen pose using depth images," in Electronics, Communications and Computers (CONIELECOMP), 2016 International Conference on, 2016, pp. 94-100.


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  • Computer Vision-based Fall Detection Methods Using The Kinect Camera: A Survey

Abstract Views: 235  |  PDF Views: 121

Authors

Salma Kammoun Jarraya
Department of Computer Science, King Abdelaziz University, Jeddah, Saudi Arabia

Abstract


Disabled people can overcome their disabilities in carrying out daily tasks in many facilities [1]. However, they frequently report that they experience difficulty being independently mobile. And even if they can, they are likely to have some serious accidents such as falls. Furthermore, falls constitute the second leading cause of accidental or injury deaths after injuries of road traffic which call for efficient and practical/comfortable means to monitor physically disabled people in order to detect falls and react urgently. Computer vision (CV) is one of the computer sciences fields, and it is actively contributing in building smart applications by providing for image\video content “understanding.” One of the main tasks of CV is detection and recognition. Detection and recognition applications are various and used for different purposes. One of these purposes is to help of the physically disabled people who use a cane as a mobility aid by detecting the fall. This paper surveys the most popular approaches that have been used in fall detection, the challenges related to developing fall detectors, the techniques that have been used with the Kinect in fall detection, best points of interest (joints) to be tracked and the well-known Kinect-Based Fall Datasets. Finally, recommendations and future works will be summarized.

Keywords


Fall Detection, Kinect Camera, Physically Disabled People, Mobility Aid Systems.

References