Abstract
The aging population has become a world-wide social concern. The number of people living alone and experiencing falls is increasing. This is a major health risk, especially among the elderly; thus, the early detection of fall motion is of great significance. A smart home care system is needed to monitor abnormal events. This paper first conducts a survey of existing smart systems and techniques in detecting fall motion in human movement, including the emergence of new natural user interface (NUI) devices and systems in the consumer market. Secondly, the paper categorizes smart technologies for fall motion detection into three main technological groups: acoustic and ambient sensor-based, kinematic sensor-based, and lastly the computer vision and NUI. An insightful discussion of each category’s advantages and disadvantages is provided. The findings show a promising research direction of integrating the computer vision with the novel consumer-grade NUI device, such as Kinect, in achieving of an affordable and practical smart home fall motion detection system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Noury, N., Fleury, A., Rumeau, P., Bourke, A.K., Laighin, G.Ó., Rialle, V., Lundy, J.E.: Fall Detection – Principles and Methods. In: Proceedings of the 29th International Conference of the Engineering in Medicine and Biology Society (EMBS), pp. 1663–1666 (2007)
Health-EU, The Public Health Portal of the European Union-Elderly (July 25, 2011), http://ec.europa.eu/healtheu/my_health/elderly/index_en.html
Hijaz, F., Afzal, N., Ahmad, T., Hasan, O.: Survey of Fall Detection and Daily Activity Monitoring Techniques. In: Proceedings of International Conference on Information and Emerging Technologies (ICIET), pp. 1–6 (2010)
Blake, J.: The Natural User Interface Revolution. In: Natural User Interfaces in .NET, Manning, pp. 4–35 (2010)
Loureiro, B., Rodrigues, R.: Multi-Touch as a Natural User Interface for Elders: A Survey. In: Proceedings of 6th Iberian International Conference on Information Systems and Technologies (CISTI), pp. 1–6 (2011)
Murphy, S.: Design Considerations for a Natural User Interface (NUI) (May 08, 2012), http://www.ti.com/lit/wp/spry181/spry181
Regol, P.A., Moreira, P.M., Reis, L.P.: Natural User Interfaces in Serious Games for Rehabilitation. In: Proceedings of 6th Iberian International Conference on Information Systems and Technologies (CISTI), pp. 1–4 (2011)
Kido, S., Miyasaka, T., Tanaka, T., Shimizu, T., Saga, T.: Fall Detection in Toilet Rooms using Thermal Imaging Sensors. In: Proceedings of IEEE/SICE International Symposium on System Integration, pp. 83–88 (2009)
Popescu, M., Mahnot, A.: Acoustic Fall Detection using One-Class Classifiers. In: Proceedings of International Symposium on Engineering in Medicine and Biology Society, Minnesota, USA, September 2-6, pp. 3505–3508 (2009)
Sixsmith, A., Johnson, N., Whatmore, R.: Pyroelectric IR Sensor Arrays for Fall Detection in the Older Population. In: EDP Sciences, pp. 153–160 (2005)
Shan, S., Yuan, T.: A Wearable Pre-impact Fall Detector using Feature Selection and Support Vector Machine. In: Proceedings of the 10th International Conference of Signal Processing (ICSP), pp. 1686–1689 (2010)
Srinivasan, S., Han, J., Lal, D., Gacic, A.: Towards Automatic Detection of Falls using Wireless Sensors. In: Proceedings of the 29th International Conference of Engineering in Medicine and Biology Society (EMBS), Lyon, France, August 23-26, pp. 1379–1382 (2007)
Bourke, A.K., O’Brien, J.V., Lyons, G.M.: Evaluation of A Threshold-Based Tri-Axial Accelerometer Fall Detection Algorithm. Gait & Posture 26(2), 194–199 (2007)
Jantaraprim, P., Phukpattaranont, P., Limsakul, C., Wongkittisuksa, B.: Improving the Accuracy of a Fall Detection Algorithm using Free Fall Characteristics. In: Proceedings of International Conference of Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), pp. 501–504 (2010)
Sazonov, E.S., Bumpus, T., Zeigler, S., Marocco, S.: Classification of Plantar Pressure and Heel Acceleration Patterns using Neural Networks. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 3007–3010 (2005)
Morris, S.J., Paradiso, J.A.: Shoe-Integrated Sensor System for Wireless Gait Analysis and Real-Time Feedback. In: Proceedings of the Conference on the Second Joint EMBS/BMES, pp. 2468–2469 (2002)
Morris, S.J., Paradiso, J.A.: A Compact Wearable Sensor Package for Clinical Gait Monitoring. Offspring 1(1), 7–15 (2002)
Liao, T., Huang, C.-L.: Slip and Fall Events Detection by Analyzing the Integrated Spatiotemporal Energy Map. In: Proceedings of 2010 International Conference on Pattern Recognition, pp. 1718–1721 (2010)
Yu, M., Naqvi, S.M., Chambers, J.: Fall Detection in the Elderly by Head Tracking. In: Proceedings of Statistical Signal Processing, pp. 357–360 (2009)
Yu, M., Naqvi, S.M., Chambers, J.: A Robust Fall Detection System for the Elderly in a Smart Room. In: Proceedings of 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1666–1669 (2010)
Foroughi, H., Naseri, A., Saberi, A., Yazdi, H.S.: An Eigenspace-Based Approach for Human Fall Detection using Integrated Time Motion Image and Neural Network. In: Proceedings of the 9th International Conference of Signal Processing, pp. 1499–1503 (2008)
Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Fall Detection from Human Shape and Motion History using Video Surveillance. In: Proceedings of the 21st International Conference of Advanced Information Networking and Applications Workshops, AINAW 2007 (2007)
Diraco, G., Leone, A., Siciliano, P.: An Active Vision System for Fall Detection and Posture Recognition in Elderly Healthcare. In: Proceedings of the Conference on Design, Automation and Test in Europe, pp. 1536–1541 (2010)
Lu, Y., Payandeh, S.: Intelligent Cooperative Tracking in Multi-Camera Systems. In: Proceedings of 9th International Conference on Intelligent Systems Design and Applications, pp. 608–613 (2009)
Zhou, Q., Yu, S., Wu, X., Gao, Q., Li, C., Xu, Y.: HMMs-Based Human Action Recognition for an Intelligent Household Surveillance Robot. In: Proceedings of IEEE International Conference on Robotics and Biomimetics (ROBIO 2009), China, pp. 2295–2300 (2009)
Collins, R.T., Amidi, O., Kanade, T.: An Active Camera System for Acquiring Multi-View Video. In: Proceedings of International Conference on Image Processing, pp. 517–520 (2002)
Microsoft Corporation, Kinect for Xbox 360-Xbox.com (May 12, 2011), http://www.xbox.com/en-GB/kinect/
Ryden, F.: Tech to the Future: Making a “Kinection” with Haptic Interaction. IEEE Journals & Magazines, 34–36 (2012)
Pattanotai, N., Mongkolnam, P., Watanapa, B.: Automatic Detection of Locations and Orientations for Kinect Cameras. In: Proceedings of the 2011 International Conference on Computer Science and Engineering Conference, pp. 251–255 (2011)
Grassi, M., Lombardi, A., Rescio, G., Malcovati, P., Malfatti, M., Gonzo, L., Leone, A., Diraco, G., Distante, C., Siciliano, P., Libal, V., Huang, J., Potamianos, G.: A Hardware-Software Framework for High-Reliability People Fall Detection. In: Proceedings of International Conference on SENSORS, pp. 1328–1331 (2008)
Leone, A., Diraco, G., Distante, C., Siciliano, P., Malfatti, M., Gonzo, L., Grassi, M., Lombardi, A., Rescio, G., Malcovati, P., Libal, V., Huang, J., Potamianos, G.: A Multi-Sensor Approach for People Fall Detection in Home Environment. In: Proceedings of Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2 2008, Marseille, France, pp. 1–12 (2008)
Ogata, K., Terada, K., Kuniyoshi, Y.: Falling Motion Control for Humanoid Robots While Walking. In: Proceedings of the 7th International Conference of Humanoid Robots, pp. 306–311 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Patsadu, O., Nukoolkit, C., Watanapa, B. (2012). Survey of Smart Technologies for Fall Motion Detection: Techniques, Algorithms and Tools. In: Papasratorn, B., Charoenkitkarn, N., Lavangnananda, K., Chutimaskul, W., Vanijja, V. (eds) Advances in Information Technology. IAIT 2012. Communications in Computer and Information Science, vol 344. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35076-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-35076-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35075-7
Online ISBN: 978-3-642-35076-4
eBook Packages: Computer ScienceComputer Science (R0)