1. A Biologically Inspired Movement Recognition System with Spiking Neural Networks for Ambient Assisted Living Applications.
- Author
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Passias, Athanasios, Tsakalos, Karolos-Alexandros, Kansizoglou, Ioannis, Kanavaki, Archontissa Maria, Gkrekidis, Athanasios, Menychtas, Dimitrios, Aggelousis, Nikolaos, Michalopoulou, Maria, Gasteratos, Antonios, and Sirakoulis, Georgios Ch.
- Subjects
ARTIFICIAL neural networks ,CONGREGATE housing ,OLDER people ,MOTION capture (Human mechanics) ,ACTION potentials ,ELDER care - 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 ( R e S u M e ), 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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