PDF(Pubmed)
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.
摘要：
这项研究为环境辅助生活（AAL）应用提供了一种新颖的解决方案，该解决方案利用了尖峰神经网络（SNN）和可重构神经形态处理器。随着人口的变化导致对老年人护理的需求增加，由于大量的老年人喜欢独立，迫切需要有效的解决方案。传统的深度神经网络(DNN)通常是能量密集型和计算要求高的。相比之下,这项研究转向SNN，它们更节能，更模仿生物神经过程，为DNN提供可行的替代方案。我们提出了基于异步元胞自动机的神经元(ACAN)，它们以其硬件高效的设计和重现复杂神经行为的能力而脱颖而出。通过利用远程监督方法(ReSuMe)，这项研究提高了SNN中的尖峰训练学习效率。我们将其应用于老年人群的运动识别，使用运动捕捉数据。我们的结果突出了83.4%的高分类准确率，证明了该方法在精确运动活动分类中的有效性。这种方法的显著优势在于其潜在的实时，AAL环境中的节能处理。我们的发现不仅证明了SNN在计算效率上优于传统DNN，而且为老年人护理中的实际神经形态计算应用铺平了道路。
