Efficient Compressive Sensing on the Shimmer Platform for Fall Detection

Mehdi Neggazi, Latifa Hamami, Abbes Amira

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Long-time cycle wireless monitoring of patients with health concerns is highly required. The quality of care, ability of fall detection and prevention is tremendously increased through enabling continous remote human movement monitoring. The aim of this paper is twofold. Firstly, to propose a real-time energy-aware wireless fall detection system based on emerging compresive sensing (CS). Secondly, to define the best way for an efficient deployment of CS-based approach on limited hardware resources sensing platforms. In addition a compression algorithm for high accuracy fall recognition is presented. The CS-based approach is carried out on the low power Shimmer sensing platform, and is aimed to reduce the 3D acceleration data for energy efficiency improvement of the energy-hungry wireless links. The sparsity degree for an efficient representation of 3D acceleration signal and high fall detection accuracy rate is also studied. Interestingly, our results show an average power consumption of less than 38% on the Shimmer Bluetooth link, and the average 3D acceleration data compression rate is about 56%. In addition, an error between the original and reconstruted 3D acceleration signal of 7% after applying CS would yield a space savings of 56% and fall detection accuracy of 96%, for a sparsity S=77 and signal length N=512. Moreover, the proposed energy-aware fall detection system has been proven to distinguish among falls and activities of daily living, and the accuracy has been evaluated in terms of specificity and sensitivity and has shown excellent results.
Original languageEnglish
Title of host publication2014 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
PublisherIEEE
Pages2401-2404
ISBN (Print)978-1-4799-3432-4
DOIs
Publication statusPublished - 2014

Publication series

NameIEEE International Symposium on Circuits and Systems
PublisherIEEE
ISSN (Print)0271-4302

Keywords

  • Real-time 3D acceleration monitoring
  • Fall detection
  • compressive sensing

Cite this

Neggazi, M., Hamami, L., & Amira, A. (2014). Efficient Compressive Sensing on the Shimmer Platform for Fall Detection. In 2014 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) (pp. 2401-2404). (IEEE International Symposium on Circuits and Systems). IEEE. https://doi.org/10.1109/ISCAS.2014.6865656
Neggazi, Mehdi ; Hamami, Latifa ; Amira, Abbes. / Efficient Compressive Sensing on the Shimmer Platform for Fall Detection. 2014 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS). IEEE, 2014. pp. 2401-2404 (IEEE International Symposium on Circuits and Systems).
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title = "Efficient Compressive Sensing on the Shimmer Platform for Fall Detection",
abstract = "Long-time cycle wireless monitoring of patients with health concerns is highly required. The quality of care, ability of fall detection and prevention is tremendously increased through enabling continous remote human movement monitoring. The aim of this paper is twofold. Firstly, to propose a real-time energy-aware wireless fall detection system based on emerging compresive sensing (CS). Secondly, to define the best way for an efficient deployment of CS-based approach on limited hardware resources sensing platforms. In addition a compression algorithm for high accuracy fall recognition is presented. The CS-based approach is carried out on the low power Shimmer sensing platform, and is aimed to reduce the 3D acceleration data for energy efficiency improvement of the energy-hungry wireless links. The sparsity degree for an efficient representation of 3D acceleration signal and high fall detection accuracy rate is also studied. Interestingly, our results show an average power consumption of less than 38{\%} on the Shimmer Bluetooth link, and the average 3D acceleration data compression rate is about 56{\%}. In addition, an error between the original and reconstruted 3D acceleration signal of 7{\%} after applying CS would yield a space savings of 56{\%} and fall detection accuracy of 96{\%}, for a sparsity S=77 and signal length N=512. Moreover, the proposed energy-aware fall detection system has been proven to distinguish among falls and activities of daily living, and the accuracy has been evaluated in terms of specificity and sensitivity and has shown excellent results.",
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Neggazi, M, Hamami, L & Amira, A 2014, Efficient Compressive Sensing on the Shimmer Platform for Fall Detection. in 2014 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS). IEEE International Symposium on Circuits and Systems, IEEE, pp. 2401-2404. https://doi.org/10.1109/ISCAS.2014.6865656

Efficient Compressive Sensing on the Shimmer Platform for Fall Detection. / Neggazi, Mehdi; Hamami, Latifa; Amira, Abbes.

2014 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS). IEEE, 2014. p. 2401-2404 (IEEE International Symposium on Circuits and Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Long-time cycle wireless monitoring of patients with health concerns is highly required. The quality of care, ability of fall detection and prevention is tremendously increased through enabling continous remote human movement monitoring. The aim of this paper is twofold. Firstly, to propose a real-time energy-aware wireless fall detection system based on emerging compresive sensing (CS). Secondly, to define the best way for an efficient deployment of CS-based approach on limited hardware resources sensing platforms. In addition a compression algorithm for high accuracy fall recognition is presented. The CS-based approach is carried out on the low power Shimmer sensing platform, and is aimed to reduce the 3D acceleration data for energy efficiency improvement of the energy-hungry wireless links. The sparsity degree for an efficient representation of 3D acceleration signal and high fall detection accuracy rate is also studied. Interestingly, our results show an average power consumption of less than 38% on the Shimmer Bluetooth link, and the average 3D acceleration data compression rate is about 56%. In addition, an error between the original and reconstruted 3D acceleration signal of 7% after applying CS would yield a space savings of 56% and fall detection accuracy of 96%, for a sparsity S=77 and signal length N=512. Moreover, the proposed energy-aware fall detection system has been proven to distinguish among falls and activities of daily living, and the accuracy has been evaluated in terms of specificity and sensitivity and has shown excellent results.

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T3 - IEEE International Symposium on Circuits and Systems

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BT - 2014 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)

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Neggazi M, Hamami L, Amira A. Efficient Compressive Sensing on the Shimmer Platform for Fall Detection. In 2014 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS). IEEE. 2014. p. 2401-2404. (IEEE International Symposium on Circuits and Systems). https://doi.org/10.1109/ISCAS.2014.6865656