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Executive function operations (Formulate a Goal, Plan, Carry out the task and Verify goal attainment) are important for supporting independence, and are often impacted early in dementia, yet are seldom considered in the design of assistive technologies for cognition for older adults with dementia. This...
Deep learning models have achieved significant success in human activity recognition, particularly in assisted living and telemonitoring. However, training these models requires substantial amounts of labeled training data, which is time-consuming and costly to acquire in real-world environments. Contrastive self-supervised learning has recently garnered attention...
Deep learning models have significantly contributed to recognizing older adults’ daily activities for telemonitoring and assistance. However, recognizing human activities in real-world smart homes over the long term presents substantial challenges. Obtaining the ground truth is time-consuming and costly, yet it is crucial for training...
Assistive technologies for cognition (ATC) can help alleviate some of the impacts of executive dysfunction and support independence. This article presents a scoping review to highlight the research gaps in this area. Search of scientific and gray literature was conducted in clinical and computer science...
To enable ageing in place, innovative and integrative technologies such as smart living environments may be part of the solution. Despite extensive published literature reviews on this topic, the effectiveness of smart living environments in supporting ageing in place, and in particular involving unobtrusive technologies,...
Deep learning models have gained prominence in human activity recognition using ambient sensors, particularly for telemonitoring older adults’ daily activities in real-world scenarios. However, collecting large volumes of annotated sensor data presents a formidable challenge, given the time-consuming and costly nature of traditional manual annotation...
Within large and growing human communities where interactions occur, trust is a key factor to consider. Computational trust models have then been widely studied since the 2000s targeting items ratings (e.g. in e-commerce) or M2M (e.g. in IoT network). Among these models, EigenTrust is today...
One of the best ways to build better vision models is to train large models on big datasets. However, the process of building such datasets is often costly and tedious. With the ever increasing adoption of Mixed Reality in professional settings, and with the performance...
This work presents a real-time system for tracking multiple object in the context of meal preparation when using the Cognitive Orthosis for CoOKing (COOK). This system is called SafeCOOK. It aims to provide more capabilities to detect some dangerous situations that the current system does...
Human activity recognition (HAR) using ambient sensors has emerged as a promising approach to telemonitoring daily activities and enhancing the elderly quality of life. Deep learning models have demonstrated competitive performance in HAR on real-world datasets. However, acquiring large amounts of annotated sensor data for...
