A portable continuous wave radar system to detect elderly fall

Muhammad Arslan Ali; Malikeh Pour Ebrahim; Mehmet Rasit Yuce

doi:10.1007/978-3-030-34833-5_1

A portable continuous wave radar system to detect elderly fall

Muhammad Arslan Ali, Malikeh Pour Ebrahim, Mehmet Rasit Yuce

Electrical and Computer Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

Fall is the leading cause of death among elderly people worldwide. In this work a low power portable continuous wave radar (CWR) system is proposed to detect elderly fall. This paper presents experimental evaluation of the system to detect human fall motion among various sitting, standing and walking activities. Signals from three subjects with different heights and weights engaged with the different movement activities including walking, sitting, standing and fall in front of the proposed radar system are analyzed. Overall, 60 fall and 180 non-fall activities were recorded. The Short-time Fourier Transform (STFT) is employed to obtain time-frequency Doppler signatures of different human activities. Radar data is analysed by using MATLAB and an algorithm is employed to classify the fall on the basis of analysed data. The results show that the proposed portable CWR can be used to detect fall from non-fall activities with almost 100% accuracy.

Original language	English
Title of host publication	Body Area Networks - Smart IoT and Big Data for Intelligent Health Management
Subtitle of host publication	14th EAI International Conference, BODYNETS 2019, Proceedings
Editors	Lorenzo Mucchi, Matti Hämäläinen, Sara Jayousi, Simone Morosi
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	3-11
Number of pages	9
Edition	14th
ISBN (Electronic)	9783030348335
ISBN (Print)	9783030348328
DOIs	https://doi.org/10.1007/978-3-030-34833-5_1
Publication status	Published - 2019
Event	International Conference on Body Area Networks 2019 - Florence, Italy Duration: 2 Oct 2019 → 3 Oct 2019 Conference number: 14th http://bodynets2019.eai-conferences.org/ https://link.springer.com/book/10.1007/978-3-030-34833-5 (Proceedings)

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Publisher	Springer
Volume	297
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	International Conference on Body Area Networks 2019
Abbreviated title	BodyNets 2019
Country	Italy
City	Florence
Period	2/10/19 → 3/10/19
Internet address	http://bodynets2019.eai-conferences.org/ https://link.springer.com/book/10.1007/978-3-030-34833-5 (Proceedings)

Keywords

CWR
Fall detection
STFT

Access to Document

10.1007/978-3-030-34833-5_1

Link to publication in Scopus

Cite this

Ali, M. A., Ebrahim, M. P., & Yuce, M. R. (2019). A portable continuous wave radar system to detect elderly fall. In L. Mucchi, M. Hämäläinen, S. Jayousi, & S. Morosi (Eds.), Body Area Networks - Smart IoT and Big Data for Intelligent Health Management: 14th EAI International Conference, BODYNETS 2019, Proceedings (14th ed., pp. 3-11). (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; Vol. 297). Springer. https://doi.org/10.1007/978-3-030-34833-5_1

Ali, Muhammad Arslan ; Ebrahim, Malikeh Pour ; Yuce, Mehmet Rasit. / A portable continuous wave radar system to detect elderly fall. Body Area Networks - Smart IoT and Big Data for Intelligent Health Management: 14th EAI International Conference, BODYNETS 2019, Proceedings. editor / Lorenzo Mucchi ; Matti Hämäläinen ; Sara Jayousi ; Simone Morosi. 14th. ed. Cham Switzerland : Springer, 2019. pp. 3-11 (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering).

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abstract = "Fall is the leading cause of death among elderly people worldwide. In this work a low power portable continuous wave radar (CWR) system is proposed to detect elderly fall. This paper presents experimental evaluation of the system to detect human fall motion among various sitting, standing and walking activities. Signals from three subjects with different heights and weights engaged with the different movement activities including walking, sitting, standing and fall in front of the proposed radar system are analyzed. Overall, 60 fall and 180 non-fall activities were recorded. The Short-time Fourier Transform (STFT) is employed to obtain time-frequency Doppler signatures of different human activities. Radar data is analysed by using MATLAB and an algorithm is employed to classify the fall on the basis of analysed data. The results show that the proposed portable CWR can be used to detect fall from non-fall activities with almost 100% accuracy.",

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Ali, MA, Ebrahim, MP & Yuce, MR 2019, A portable continuous wave radar system to detect elderly fall. in L Mucchi, M Hämäläinen, S Jayousi & S Morosi (eds), Body Area Networks - Smart IoT and Big Data for Intelligent Health Management: 14th EAI International Conference, BODYNETS 2019, Proceedings. 14th edn, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 297, Springer, Cham Switzerland, pp. 3-11, International Conference on Body Area Networks 2019, Florence, Italy, 2/10/19. https://doi.org/10.1007/978-3-030-34833-5_1

A portable continuous wave radar system to detect elderly fall. / Ali, Muhammad Arslan; Ebrahim, Malikeh Pour; Yuce, Mehmet Rasit.

Body Area Networks - Smart IoT and Big Data for Intelligent Health Management: 14th EAI International Conference, BODYNETS 2019, Proceedings. ed. / Lorenzo Mucchi; Matti Hämäläinen; Sara Jayousi; Simone Morosi. 14th. ed. Cham Switzerland : Springer, 2019. p. 3-11 (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; Vol. 297).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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Ali MA, Ebrahim MP, Yuce MR. A portable continuous wave radar system to detect elderly fall. In Mucchi L, Hämäläinen M, Jayousi S, Morosi S, editors, Body Area Networks - Smart IoT and Big Data for Intelligent Health Management: 14th EAI International Conference, BODYNETS 2019, Proceedings. 14th ed. Cham Switzerland: Springer. 2019. p. 3-11. (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering). https://doi.org/10.1007/978-3-030-34833-5_1