A survey on fall detection: Principles and approaches
Introduction
Falls are a major cause of fatal injury especially for the elderly and create a serious obstruction for independent living. Statistics [57] show that falls are the primary reason of injury related death for seniors aged 79 or more and the second leading cause of injury related (unintentional) death for all ages. The demand for surveillance systems, especially for fall detection, has increased within the healthcare industry with the rapid growth of the population of the elderly in the world. It has become very important to develop intelligent surveillance systems, especially vision-based systems, which can automatically monitor and detect falls. It has been proved that the medical consequences of a fall are highly contingent upon the response and rescue time. Thus, a highly-accurate automatic fall detection system is likely to be a significant part of the living environment for the elderly to expedite and improve the medical care provided whilst allowing people to retain autonomy for longer.
The quality of an individual's life is significantly affected by the levels of functional ability. Plenty of research has been done in this area to develop systems and algorithms for enhancing the functional ability of the elderly and patients. The maturity of cameras, sensors and computer technologies make such systems feasible. Such systems cannot only increase the independent living ability of the elderly, by raising the confidence levels in a supportive care environment within the public sector, but also save on manual labour in terms of the presence of nurses or support staff at all times.
The rest of the paper is organised as follows. In Section 2, different types of fall are introduced, followed by the classification of fall detection methods. We review three different categories of fall detection approaches in 3 Wearable device based approaches, 3.4 Tri-axial accelerometry, 4 Ambient device based approaches, 5 Camera (vision) based approaches. Finally, we conclude and discuss future directions of research in Section 6.
Section snippets
Classification of falls and fall detection techniques
In this section, different kinds of falls are first identified. Specifying different types of falls help towards an understanding of the existing approaches. It also guides and contributes towards the design of new algorithms.
Different scenarios should be considered when identifying different kinds of falls: falls from walking or standing, falls from standing on supports, e.g., ladders etc., falls from sleeping or lying in the bed and falls from sitting on a chair. There are some common
Wearable device based approaches
Wearable device based approaches rely on garments with embedded sensors to detect the motion and location of the body of the subject. In the following we summarise the different methods.
Ambient device based approaches
Ambience based devices attempt to fuse audio and visual data and event sensing through vibrational data.
Camera (vision) based approaches
Cameras are increasingly included, these days, in in-home assistive/care systems as they convey multiple advantages over other sensor based systems. Cameras can be used to detect multiple events simultaneously with less intrusion.
Conclusion and future work
We have reviewed different techniques for the detection of a fall event. Table 1 lists various characteristics of those approaches. A comprehensive and robust fall detection system should possess both high sensitivity and good specificity. The existing approaches have not comprehensively satisfied the accuracy as well as robustness of a fall detection system. However, the existing approaches do provide a framework to further develop techniques as well as modify the existing algorithms to
Muhammad Mubashir received his B.Eng (Hons) in Electronic Engineering from Aston University in 2008. He is currently a research student in the Department of Electronic and Electrical Engineering at the University of Sheffield. His research interests are computer vision and image processing.
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Muhammad Mubashir received his B.Eng (Hons) in Electronic Engineering from Aston University in 2008. He is currently a research student in the Department of Electronic and Electrical Engineering at the University of Sheffield. His research interests are computer vision and image processing.
Ling Shao received the B.Eng. degree in Electronic Engineering from the University of Science and Technology of China (USTC), the M.Sc. degree in Medical Image Analysis and the Ph.D. (D.Phil.) degree in Computer Vision at the Robotics Research Group from the University of Oxford.
Dr. Ling Shao is currently a Senior Lecturer (Associate Professor) in the Department of Electronic and Electrical Engineering at the University of Sheffield. Before joining Sheffield University, he worked for 4 years as a Senior Scientist in Philips Research, The Netherlands. Prior to that, he worked shortly as a Senior Research Engineer at the Institute of Electronics, Communications and Information Technology, Queen's University of Belfast. His research interests include Computer Vision, Pattern Recognition and Video Processing. He has published over 60 academic papers in refereed journals and conference proceedings and has filed over 10 patent applications. Ling Shao is an associate editor of the International Journal of Image and Graphics, the EURASIP Journal on Advances in Signal Processing, and Neurocomputing, and has edited several special issues for journals of IEEE, Elsevier and Springer. He has organised several workshops with ICCV, ACM Multimedia and ACCV. He has been serving as Programme Committee member for many international conferences, including ICIP, ICASSP, ICME, ICMR, ACM MM, CIVR, BMVC, etc. He is a senior member of the IEEE.
Luke Seed (B.Eng. Sheffield, '83, PhD Sheffield, '89) is a Senior Lecturer in the Department of Electronic and Electrical Engineering at the University of Sheffield. His current research interests are mainly centred on the use of holographic lithography for patterning non-planar surfaces for electronics manufacturing and other applications. His work in the area of vision has been related to stereopsis—particularly algorithmic approaches to real-time implementation.