Abstract
In this paper, we present Watchful-Eye, a 3D skeleton-based system to monitor a physically disabled person using a cane as a mobility aid. Watchful-Eye detects fall occurrences using skeleton tracking with a Microsoft Kinect camera. Compared to existing systems, it has the merit of detecting various types of fall under multiple scenarios and postures, while using a small set of features extracted from Kinect captured video streams. To achieve this merit, we followed the typical machine learning process: First, we collected a rich fall detection dataset. Second, we experimentally determined the most relevant features that best-distinguish fall from non-fall frames, and the best performing classifier. As we report in this paper, the offline evaluation results show that Watchful-Eye reached an accuracy between 87.2% and 94.5% with 5.5% to 12.8% error rate depending on the used classifier. Furthermore, the online evaluation shows that it can detect falls with an accuracy between 89.47% and 100%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kwolek, B., Kepski, M.: Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput. Methods Programs Biomed. 117, 489–501 (2014)
Lee, C.K., Lee, V.Y.: Fall detection system based on Kinect sensor using novel detection and posture recognition algorithm. In: Biswas, J., Kobayashi, H., Wong, L., Abdulrazak, B., Mokhtari, M. (eds.) ICOST 2013. LNCS, vol. 7910, pp. 238–244. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39470-6_30
Stone, E.E., Skubic, M.: Fall detection in homes of older adults using the Microsoft Kinect. IEEE J. Biomed. Health Inf. 19, 290–301 (2015)
Kawatsu, C., Li, J., Chung, C.: Development of a fall detection system with Microsoft Kinect. In: Kim, J.H., Matson, E., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications 2012. AISC, vol. 208, pp. 623–630. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37374-9_59
Kozina, S., Gjoreski, H., Gams, M., Luštrek, M.: Efficient activity recognition and fall detection using accelerometers. In: B, Juan A., Álvarez-García, J.A., Fujinami, K., Barsocchi, P., Riedel, T. (eds.) EvAAL 2013. CCIS, vol. 386, pp. 13–23. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41043-7_2
Abdali-Mohammadi, F., Rashidpour, M., Fathi, A.: Fall detection using adaptive neuro-fuzzy inference system. Int. J. Multimed. Ubiquit. Eng. 11, 91–106 (2016)
Noury, N., Fleury, A., Rumeau, P., Bourke, A., Laighin, G., Rialle, V., et al.: Fall detection-principles and methods. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1663–1666 (2007)
Melis, E., Torres-Moreno, R., Barbeau, H., Lemaire, E.: Analysis of assisted-gait characteristics in persons with incomplete spinal cord injury. Spinal Cord 37, 430–439 (1999)
Rhemyst and Rymix. Kinect SDK Dynamic Time Warping (DTW) Gesture Recognition, 30 July 2011–2 January 2017. http://kinectdtw.codeplex.com/
Pterneas, V.: Measuring Distances using Kinect – The Right Way, 29 October 2016. http://pterneas.com/2016/08/11/measuring-distances-kinect/
Le, T.-L., Nguyen, M.-Q.: Human posture recognition using human skeleton provided by Kinect. In: 2013 International Conference on Computing, Management and Telecommunications (ComManTel), pp. 340–345 (2013)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11, 10–18 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Alzahrani, M.S., Kammoun Jarraya, S., Ali, M.S., Ben-Abdallah, H. (2018). Watchful-Eye: A 3D Skeleton-Based System for Fall Detection of Physically-Disabled Cane Users. In: Perego, P., Rahmani, A., TaheriNejad, N. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-98551-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-98551-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98550-3
Online ISBN: 978-3-319-98551-0
eBook Packages: Computer ScienceComputer Science (R0)