Abstract
Accurate and timely human fall detection is a strong requirement either for the surveillance of critical infrastructures or for ships. Indeed, sea-faring vessels are one of the most important means for maintaining the marine economy in many countries by transporting goods or people. However, unfortunate tragic accidents on-board ships involving people, either a member of the ship’s crew or a passenger who has fallen off the ship may take place, which is known by the term “man overboard” (MOB). Accordingly, the use of radar sensors for human safety monitoring applications is vital and is of special interest since it is proven that radar sensors are less influenced by environmental conditions (e.g. fog, rain, temperature) compared to other systems like video cameras. Consequently, human fall detection from either sea or ground infrastructures is easier to be identified using radars compared to the conventional methods. This paper focuses in the description of a real experimental approach based on multiple long-range millimeter-wave band radar sensors for human fall detection. The stream(s) of information collected by the system, are processed using clustering techniques. The clustering results are evaluated in terms of the ability to detect and track real human fall scenarios. The results reveal that the measure of velocity plays a key role in the detection procedure.
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Data Availability Statement
The datasets generated during and/or analysed during the current study are not publicly available due to Non Disclosure Agreement signed by the authors with the partners of the research project but are available from the corresponding author on reasonable request.
Notes
RGB sensors capture visible light and recognise/detect the colour of a material in RGB (red, green, blue) scale, while rejecting the unwanted infrared or ultraviolet light.
The source code repository at GitHub is available at https://github.com/mguentner/cannelloni.
User datagram protocol (UDP), is part of the internet protocol (IP) for low-latency communication and loss-tolerating connections over the Internet.
The CAN_ID messages [0 \(\times\) 702] carrying clusters’ quality information are not sent by default, and this option must be activated manually.
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Acknowledgements
This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call “RESEARCH-CREATE-INNOVATE” (Project Code: T1EDK-01169, Project Name: MHTIS).
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Armeniakos, C.K., Nikolaidis, V., Tsekenis, V. et al. Human fall detection using mmWave radars: a cluster-assisted experimental approach. J Ambient Intell Human Comput 14, 11657–11669 (2023). https://doi.org/10.1007/s12652-022-03728-w
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DOI: https://doi.org/10.1007/s12652-022-03728-w