The global population is growing at an unprecedented pace, resulting in an increase in the number of people affected by cognitive decline. This paper presents a MR application for older adults with cognitive decline, aiming to stimulate cognitive and motor functioning and promote active ageing. Developed using Unity and deployed to HoloLens 2, the system allows users to interact with holograms through a series of engaging games. Before having elderly people with cognitive decline test the MR approach, preliminary studies were conducted to assess both its feasibility and usability. The results are encouraging, with participants reporting the approach is easy to learn and perform. They also felt confident and successful in accomplishing what they were asked to do. The immersive nature of MR has the potential to transform ageing into an experience filled with opportunities rather than limitations.

In recent decades, the detection of fatigue has been largely studied to improve safety and performance in various domains such as healthcare, transportation, and manufacturing. In fact, fatigue significantly affects cognitive and physical performance. There are numerous studies on the detection of fatigue, but most of them focus on binary classification (i.e., fatigue vs. resting state) or different levels of fatigue intensity, without distinguishing between specific fatigue types.This work aims to discriminate between cognitive and physical fatigue, and proposes the use of physiological signals to classify four distinct conditions: rest, cognitive fatigue, physical fatigue, and combined cognitive and physical fatigue. We analyze data from cardiac, eye, electrodermal, and electromyographic activity. We consider different feature selection methods, including correlation analysis, principal component analysis and sequential forward floating search method. Ultimately, we classify them using state-of-the-art machine learning methods.The highest classification accuracy (86.823%) is obtained from a support vector machine method using a selection of the features extracted from cardiac and electromyographic sensors.This study highlights the potential for real-time fatigue classification, which can enhance the adaptability of automated systems to human needs, particularly in high risk environments like transportation and healthcare. Furthermore, the findings suggest that fatigue monitoring can be effectively conducted with minimal sensor requirements, contributing to the design of more efficient wearable sensor systems.

Fatigue is a multifaceted construct, that represents an important part of human experience. The two main aspects of fatigue are the mental one and the physical one, that often intertwine, intensifying their collective impact on daily life and overall well-being. To soften this impact, understanding and quantifying fatigue is crucial. Physiological data play a pivotal role in the comprehension of fatigue, allowing a precious insight into the level and type of fatigue experienced. Though the analysis of these biosignals, researchers can determine whether the person is feeling mental fatigue, physical fatigue or a combination of both. This paper introduces MePhy, a comprehensive dataset containing various biosignals, gathered while inducing different fatigue conditions, in particular mental and physical fatigue. Among the biosignals closely associated with stress situations, we chose: eye activity, cardiac activity, electrodermal activity (EDA) and electromyography (EMG). Data were collected using different devices, including a camera, a chest strap and different sensors from the BITalino kit.

Building on the benefits of Industry 4.0, Industry 5.0 promotes human-centricity of factories, placing operator’s wellbeing at the center of production. Production processes are expected to be designed around operator’s needs, for a more sustainable contribution of industry to society. In this broad context, in this paper we consider the problem of monitoring operator’s condition and, specifically, detecting any mental or physical fatigue they might be experiencing in workplaces. Indeed, if the onset of any source of fatigue is monitored, it is possible to introduce assistive strategies that preserve productivity, on the one side, and operator’s wellbeing, on the other side. To achieve this goal, we detect mental and physical fatigue conditions via physiological monitoring, by means of a wearable device that measures cardiac activity. We design an experimental protocol such that test participants are exposed to mental and physical fatigue. Their heart rate variability is then extracted and analysed to discriminate among rest, mental fatigue, physical fatigue and joint mental and physical fatigue. The achieved results show that statistically significant differences can be found in time-domain metrics. Moreover, the analysis of the empirical distribution functions shows, for each metrics, the conditions that exhibit the greatest differences and, hence, that can be distinguished more accurately. However, results show also that, in the presence of physical fatigue, it is difficult to detect the presence of additional mental fatigue.