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

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.

Kidney Care during COVID-19 in the UK: Perspectives of Healthcare Professionals on Impacts on Care Quality and Staff Well-Being.

In light of the rapid changes in healthcare delivery due to COVID-19, this study explored kidney healthcare professionals' (HCPs) perspectives on the impact of these changes on care quality and staff well-being. Fifty-nine HCPs from eight NHS Trusts across England completed an online survey and eight took part in complementary semi-structured interviews between August 2020 and January 2021. Free-text survey responses and interviews were analysed using inductive thematic analysis. Themes described the rapid adaptations, concerns about care quality, benefits from innovations, high work pressure, anxiety and mental exhaustion in staff and the team as a well-being resource. Long-term retention and integration of changes and innovations can improve healthcare access and efficiency, but specification of conditions for its use is warranted. The impact of prolonged stress on renal HCPs also needs to be accounted for in quality planning. Results are further interpreted into a theoretical socio-technical framework.