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Article

A Biologically Inspired Movement Recognition System with Spiking Neural Networks for Ambient Assisted Living Applications

by
Athanasios Passias
1,
Karolos-Alexandros Tsakalos
1,
Ioannis Kansizoglou
2,
Archontissa Maria Kanavaki
3,
Athanasios Gkrekidis
3,
Dimitrios Menychtas
3,
Nikolaos Aggelousis
3,
Maria Michalopoulou
3,
Antonios Gasteratos
2 and
Georgios Ch. Sirakoulis
1,*
1
Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
2
Department of Production and Management Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
3
School of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
*
Author to whom correspondence should be addressed.
Biomimetics 2024, 9(5), 296; https://doi.org/10.3390/biomimetics9050296
Submission received: 31 December 2023 / Revised: 20 February 2024 / Accepted: 12 March 2024 / Published: 15 May 2024
(This article belongs to the Special Issue Biologically Inspired Vision and Image Processing)
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Abstract

This study presents a novel solution for ambient assisted living (AAL) applications that utilizes spiking neural networks (SNNs) and reconfigurable neuromorphic processors. As demographic shifts result in an increased need for eldercare, due to a large elderly population that favors independence, there is a pressing need for efficient solutions. Traditional deep neural networks (DNNs) are typically energy-intensive and computationally demanding. In contrast, this study turns to SNNs, which are more energy-efficient and mimic biological neural processes, offering a viable alternative to DNNs. We propose asynchronous cellular automaton-based neurons (ACANs), which stand out for their hardware-efficient design and ability to reproduce complex neural behaviors. By utilizing the remote supervised method (ReSuMe), this study improves spike train learning efficiency in SNNs. We apply this to movement recognition in an elderly population, using motion capture data. Our results highlight a high classification accuracy of 83.4%, demonstrating the approach’s efficacy in precise movement activity classification. This method’s significant advantage lies in its potential for real-time, energy-efficient processing in AAL environments. Our findings not only demonstrate SNNs’ superiority over conventional DNNs in computational efficiency but also pave the way for practical neuromorphic computing applications in eldercare.
Keywords: ambient assisted living (AAL); spiking neural networks (SNNs); reconfigurable neuromorphic processors; elderly activity recognition; energy-efficient processing; real-time processing; activity monitoring ambient assisted living (AAL); spiking neural networks (SNNs); reconfigurable neuromorphic processors; elderly activity recognition; energy-efficient processing; real-time processing; activity monitoring

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MDPI and ACS Style

Passias, A.; Tsakalos, K.-A.; Kansizoglou, I.; Kanavaki, A.M.; Gkrekidis, A.; Menychtas, D.; Aggelousis, N.; Michalopoulou, M.; Gasteratos, A.; Sirakoulis, G.C. A Biologically Inspired Movement Recognition System with Spiking Neural Networks for Ambient Assisted Living Applications. Biomimetics 2024, 9, 296. https://doi.org/10.3390/biomimetics9050296

AMA Style

Passias A, Tsakalos K-A, Kansizoglou I, Kanavaki AM, Gkrekidis A, Menychtas D, Aggelousis N, Michalopoulou M, Gasteratos A, Sirakoulis GC. A Biologically Inspired Movement Recognition System with Spiking Neural Networks for Ambient Assisted Living Applications. Biomimetics. 2024; 9(5):296. https://doi.org/10.3390/biomimetics9050296

Chicago/Turabian Style

Passias, Athanasios, Karolos-Alexandros Tsakalos, Ioannis Kansizoglou, Archontissa Maria Kanavaki, Athanasios Gkrekidis, Dimitrios Menychtas, Nikolaos Aggelousis, Maria Michalopoulou, Antonios Gasteratos, and Georgios Ch. Sirakoulis. 2024. "A Biologically Inspired Movement Recognition System with Spiking Neural Networks for Ambient Assisted Living Applications" Biomimetics 9, no. 5: 296. https://doi.org/10.3390/biomimetics9050296

APA Style

Passias, A., Tsakalos, K.-A., Kansizoglou, I., Kanavaki, A. M., Gkrekidis, A., Menychtas, D., Aggelousis, N., Michalopoulou, M., Gasteratos, A., & Sirakoulis, G. C. (2024). A Biologically Inspired Movement Recognition System with Spiking Neural Networks for Ambient Assisted Living Applications. Biomimetics, 9(5), 296. https://doi.org/10.3390/biomimetics9050296

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