Smart Sensors and Measurements Methods for Quality of Life and Ambient Assisted Living

Dear Colleagues,

Ambient Assisted Living (AAL) encompasses systems, applications, and services that adopt sensing devices, measurement methods, and Information and communications technologies (ICT) to offer new products and solutions to address health and well-being needs, particularly for the aging population. The expected outcomes from the introduction of such a paradigm include a positive impact on older adults’ health-related quality of life (QoL), the management of health conditions, and improvements in overall well-being, whilst, at the same time, reducing the costs of healthcare provision.

Smart sensors and measurement methods aimed at objectively capturing and quantifying an individual’s health-related to life quality stands at the foundation of the above-mentioned paradigm, enabling the assessment of the impact of AAL-related solutions and the evaluation of their performance. Despite their importance, however, reliable and easy-to-quantify measures of health-related QoL of older adults are still missing, as well as measurement methods that can be easily integrated and applied into daily life routines, ensuring accuracy, reliability, and adherence.

This Special Issue aims to highlight advances in the development, testing, and modeling of smart wearable and ambient sensors, methods, and frameworks to measure health-related QoL and well-being of an individual, with a particular focus on the AAL domain and older adults. Topics include, but are not limited, to the following:

• Minimally obtrusive methods to measure physiological and health-related parameters in living environments.
• Metrics and methods to measure health-related quality of life of older adults
• Accuracy of wearable and ambient sensors and the impact of measurement uncertainty on the performance of learning algorithms for AAL and well-being
• Sensing solutions for health-related quality of life, targeting older adults
• Metrological issues in health-related quality of life and well-being measurements
• Smart sensing and advanced signal processing (i.e., machine learning) in AAL
• Application-driven sensing solutions for living environments and AAL

Dr. Susanna Spinsante
Prof. Dr. Lorenzo Scalise
Guest Editors

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