Occupancy detection and activity recognition are crucial for building automation and patient monitoring. Traditional motion sensors have limitations, such as binary output and the inability to detect stationary individuals, compromising detection accuracy. This study explores environmental sensors - such as ambient light, temperature, humidity, and sound levels - as alternatives or complements to motion sensors for occupancy detection. Using the “Smart Home environment data across 4 European countries”, compromising various sensor configurations in individual rooms, we utilize event-based sensors (primarily motion sensors) as proxy labels for presence. We train both generalized and personalized XGBoost models: the generalized model predicts occupancy in new environments, while the personalized model is tailored to individuals. Comparing model performance, we find that personalized models generally outperform generalized ones, though the best configurations' F1-scores are close (0.77 vs. 0.76). The sound level features are the most influential in both models, indicating that environmental sensors, particularly with sound data, can enhance occupancy detection and potentially reduce the dependence on motion sensors. This study improves our understanding of the importance and configurations of sensors for occupancy detection and highlights the advantages of personalized models in smart home applications.

The phantom vibration syndrome (PVS), a pathology not fully understood, primarily garners attention from medical research. However, a better understanding would allow engineering implications also to emerge, particularly in tactile interface design. In this article, a novel approach utilising smartphones and citizen science is introduced as a new research avenue. A preliminary trial involving 22 volunteers demonstrated an approx. 70% prevalence of PVS, while also uncovering potential challenges in app development and validation. Despite these hurdles, smartphone apps potentially offer scalability and facilitate citizen science engagement, rendering PVS research conducive to broad participation. Collaboration across medical, engineering, and citizen domains is advocated for achieving deeper understanding and effective solutions.

For the redesign of a website targeting persons with any kind of disability, we worked with a group of people with cognitive disabilities. The goal was to test proposed enhancements to the menu of the website, simplifying the user experience but keeping the modifications small, so that users don't have to relearn how to use the website. We prepared a clickable prototype and had it evaluated by the test persons. The project confirmed that clickable prototypes are a useful tool for such evaluations. It shed light on the way how such persons find their way around a website. They preferred a graphical menu over a textual one. But contrary to our expectations, they ignored moving elements that were thought to focus attention on important content. We also found that a navigation is difficult to be evaluated if the website content is not provided in an easy-to-understand language, because non-easy language content of pages navigated to is not recognized as the desired information. The conclusion contains recommendations about the design of clickable prototypes and about priorities on the website (re)design in general.

In the scope of co-creation sessions, care workers provided insights on applications and on concerns about Intelligent Voice Assistants (IVA) in the home of their clients or patients. The sessions focused on the potential to support the care documentation process by IVA. Participants’ expectations and worries spanned from the ability to handle dialects, to confidentiality issues, to integration in existing care documentation systems. However, there is a general openness toward the idea to employ IVA as means to improve the quality of care. The challenge foreseen for using IVA is to become as time efficient as care documentation systems in place. Alternatively, as suggested by participants, IVA could complement existing processes or even create new ones in the care context.

In this study we evaluated system architecture for the use of smart glasses as a viewer of information, as a source of medical data (vital sign measurements: temperature, pulse rate, and respiration rate), and as a filter of healthcare information. All activities were based on patient/device identification procedures using graphical markers or features based on visual appearance. The architecture and particular use cases were implemented and verified using smart glasses prototypes developed under the eGlasses project and using a reference Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) server. The results show that information about the identified patient can be quickly retrieved from FHIR servers and annotated using voice recognition services. Smart glasses can be used in the measurement of vital signs of the observed patient, providing values of body temperature, pulse rate, and respiration rate by means of non-contact measurements. Such measurements are sufficiently reliable for medical screening and for fast data exchange using HL7 FHIR actions.

Recently, different smart glasses solutions have been proposed on the market. The rapid development of this wearable technology has led to several research projects related to applications of smart glasses in healthcare. In this paper we propose a general architecture of the system enabling data integration for the recognized person. In the proposed system smart glasses integrates data obtained for the recognized patient from health care information systems, from devices connected to the patient and from the patient himself. The data integration is possible, if proper patient recognition procedure is used. Therefore, we evaluated three identification methods based on face recognition and using the recognition of graphical markers (i.e. QR-codes and proposed color-based codes). The results show that it is possible to obtain reliable and fast recognition results during the video acquisition by the smart glasses camera.

Implicit interaction combined with object recognition techniques opens a new possibility for gathering data and analyzing user behavior for activity and context recognition. The electronic eyewear platform, eGlasses, is being developed, as an integrated and autonomous system to provide interactions with smart environment. In this paper we present a method for the interactions with the recognized objects that can be used for electronic eyewear. The design of the control node is presented. The control node is the extension of the power socket, equipped with a set of sensors and ZigBee communication interface. The node is used to control electronic objects through user interactions with a mobile device. The preliminary results are presented based on the Android mobile phone used to recognize controllable objects based on the captured image sequences.