As life expectancy increases, it is imperative that the elderly take advantage of the benefits of technology to remain active and independent. Mobile health applications are widely used nowadays as they promote a healthy lifestyle and self-management of diseases, opening new horizons in the interactive health service delivery. However, adapting these applications to the needs and requirements of the elderly is still a challenge. This article presents a smartphone application that is part of a multifactorial intervention to support older people with balance disorders. The application aims to enable users to self-evaluate their activity and progress, to communicate with each other and, through strategically selected motivational features, to engage with the system with undiminished interest for a long period of time. Mock-up interfaces were evaluated in semi-structured focus groups and interviews that were performed across three European countries. Further evaluation in the form of four pilot studies with 160 participants will be performed and qualitative and quantitative measures will be used to process the feedback about the use of the application.

Nowadays, changes in the conditions and nature of the workplace make it imperative to create unobtrusive systems for the automatic detection of occupational stress, which can be feasibly addressed through the adoption of Internet of Things (IoT) technologies and advances in data analysis. This paper presents the development of a multimodal automated stress detection system in an office environment that utilizes measurements derived from individuals’ interactions with the computer and its peripheral units. In our analysis, behavioral parameters of computer keyboard and mouse dynamics are combined with physiological parameters recorded by sensors embedded in a custom-made smart computer mouse device. To validate the system, we designed and implemented an experimental protocol simulating an office environment and included the most known work stressors. We applied known classifiers and different data labeling methods to the physiological and behavioral parameters extracted from the collected data, resulting in high-performance metrics. The feature-level fusion analysis of physiological and behavioral parameters successfully detected stress with an accuracy of 90.06% and F1 score of 0.90. The decision-level fusion analysis, combining the features extracted from both the computer mouse and keyboard, showed an average accuracy of 66% and an average F1 score of 0.56.

Occupational stress is a major challenge in modern societies, related with many health and economic implications. Its automatic detection in an office environment can be a key factor toward effective management, especially in the post-COVID era of changing working norms. The aim of this study is the design, development and validation of a multisensor system embedded in a computer mouse for the detection of office work stress. An experiment is described where photoplethysmography (PPG) and galvanic skin response (GSR) signals of 32 subjects were obtained during the execution of stress-inducing tasks that sought to simulate the stressors present in a computer-based office environment. Kalman and moving average filters were used to process the signals and appropriately formulated algorithms were applied to extract the features of pulse rate and skin conductance. The results found that the stressful periods of the experiment significantly increased the participants’ reported stress levels while negatively affecting their cognitive performance. Statistical analysis showed that, in most cases, there was a highly significant statistical difference in the physiological parameters measured during the different periods of the experiment, without and with the presence of stressors. These results indicate that the proposed device can be part of an unobtrusive system for monitoring and detecting the stress levels of office workers.

Healthcare infrastructures face escalating cybersecurity risks caused by legacy systems, connected IoMT devices, and complex data-sharing environments. Conventional intrusion detection and SIEM systems provide limited protection against zero-day and lateral-movement attacks. This paper presents the ASKOS architecture, a federated and deception-enhanced security framework that integrates Conpot honeypots with federated machine learning and software-defined networking (SDN) to enable privacy-preserving, adaptive cyber defense. By combining deception telemetry, distributed anomaly detection, and risk-aware orchestration, ASKOS delivers an intelligent and compliant security layer that can be implemented across healthcare infrastructures.