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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.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.
References
Berrahal S, Kim JH, Rekhis S et al (2016) Border surveillance monitoring using quadcopter UAV-aided wireless sensor networks. J Commun Softw Syst 12(1):67. https://doi.org/10.24138/jcomss.v12i1.92
Bhadwal N, Madaan V, Agrawal P, et al (2019) Smart border surveillance system using wireless sensor network and computer vision. In: 2019 international conference on automation, computational and technology management (ICACTM). IEEE, pp 183–190. https://doi.org/10.1109/icactm.2019.8776749
Chapelle O, Scholkopf B, Zien A (2006) Semi-supervised learning. The MIT Press, Cambridge
Continental Engineering Services (2018) Continental ARS 408-21 Long Range Radar Sensor 77 GHz. https://hexagondownloads.blob.core.windows.net/public/AutonomouStuff/wp-content/uploads/2020/08/ARS-408-21-whitelabel.pdf
Feraru VA, Andersen RE, Boukas E (2020) Towards an autonomous UAV-based system to assist search and rescue operations in man overboard incidents. In: 2020 IEEE international symposium on safety, security, and rescue robotics (SSRR), pp 57–64. https://doi.org/10.1109/SSRR50563.2020.9292632
ISO, PAS 21195:2018(E) (2018) Plastics—ships and marine technology—systems for the detection of persons while going overboard from ships (man overboard detection). Standard International Organization for Standardization, Geneva
ITU-R M.2285-0 (2013) Maritime survivor locating systems and devices (man overboard systems), An overview of systems and their mode of operation. Approved in 12-2013, Status: In force (Main)
Jeong CM, Jung YG, Lee SJ (2018) Deep belief networks based radar signal classification system. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0774-7
Katsamenis I, Protopapadakis E, Voulodimos A et al (2020) Man overboard event detection from RGB and thermal imagery. In: Proceedings of the 13th ACM international conference on pervasive technologies related to assistive environments. ACM, pp 1–6. https://doi.org/10.1145/3389189.3397998
Kaufman L, Rousseeuw PJ (eds) (1990) Finding groups in data: an introduction to cluster analysis. Wiley. https://doi.org/10.1002/9780470316801
Li M, Stolz M, Feng Z et al (2018) An adaptive 3D grid-based clustering algorithm for automotive high resolution radar sensor. In: 2018 IEEE international conference on vehicular electronics and safety (ICVES). IEEE, pp 1–7. https://doi.org/10.1109/icves.2018.8519483
Örtlund E, Larsson M (2018) Man Overboard detecting systems based on wireless technology. Chalmers University of Technology. https://hdl.handle.net/20.500.12380/256283
Reinhardt D, Guntner M, Kucera M et al (2015) Mapping CAN-to-ethernet communication channels within virtualized embedded environments. In: 10th IEEE international symposium on industrial embedded systems (SIES). IEEE, pp 1–10. https://doi.org/10.1109/sies.2015.7185064
Scheel A, Dietmayer K (2019) Tracking multiple vehicles using a variational radar model. IEEE Trans Intell Transp Syst 20(10):3721–3736. https://doi.org/10.1109/tits.2018.2879041
Scheiner N, Appenrodt N, Dickmann J et al (2019) A multi-stage clustering framework for automotive radar data. In: 2019 IEEE intelligent transportation systems conference (ITSC). IEEE, pp 2060–2067. https://doi.org/10.1109/itsc.2019.8916873
Sevin A, Bayilmis C, Erturk I et al (2016) Design and implementation of a man-overboard emergency discovery system based on wireless sensor networks. Turk J Electr Eng Comput Sci 24:762–773. https://doi.org/10.3906/elk-1308-154
Sheu BH, Yang TC, Yang TM et al (2020) Real-time alarm, dynamic GPS tracking, and monitoring system for man overboard. Sens Mater 32(1):197. https://doi.org/10.18494/sam.2020.2582
Stolz M, Li M, Feng Z et al (2018) High resolution automotive radar data clustering with novel cluster method. In: 2018 IEEE radar conference (RadarConf18). IEEE, pp 0164–0168. https://doi.org/10.1109/radar.2018.8378550
Yousefi A, Dibazar AA, Berger TW (2008) Intelligent fence intrusion detection system: detection of intentional fence breaching and recognition of fence climbing. In: 2008 IEEE conference on technologies for homeland security. IEEE, pp 620–625. https://doi.org/10.1109/ths.2008.4635057
Zhang W, Gao XZ, Yang CF et al (2020) A object detection and tracking method for security in intelligence of unmanned surface vehicles. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02573-z
Zhao Y, Su Y (2017) Vehicles detection in complex urban scenes using gaussian mixture model with FMCW radar. IEEE Sens J 17(18):5948–5953. https://doi.org/10.1109/jsen.2017.2733223
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).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-022-03728-w