Abstract. This paper uses a case study of collaborative work practices within the radiology department of a hospital, for examining the usefulness of spatial approaches to collaboration. It takes a socio-political perspective on understanding the shaping effects of spatial arrangements on work practices, and seeks to identify some of the key CSCW issues that can be addressed in spatial terms. We analyse the spatial settings or layers (physical, digital and auditory) within which work takes place, and the qualities of connections between them, examining in how far they support (professional) boundaries or help maintain a sense of context. Guiding themes are the relationships between space and the visibility of work, and how to accommodate social world needs through spatial arrangements.

Background Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590

Building automation is concerned with closed- and open-loop control of building services such as heating, cooling, ventilation and air conditioning, lighting and shading. The ultimate goal is to reduce energy consumption while providing comfort for the occupants. However, ensuring human comfort is a complex affair. In case of dissatisfaction, users need to inform the building operators about apparently badly adjusted setpoints. Then, service units of the facility management have to manually analyze how to improve the situation. Due to the complex characteristics of human perception and derived feedback, this can become a troublesome and time-consuming task. This paper describes the main results of our investigations to improve occupant comfort in office buildings using environmental information monitored by a wsn and human perception collected from a feedback tool. A joint information base aligned with static data from building information modeling integrates the information gathered. Reasoning on these data sources allows adjustments of the bas to automatically enhance the tenant’s comfort or suggest necessary adjustments for facility managers. Communication between the different system components is handled via mqtt. A real-world field study shows the potential of the developed approach, proves its feasibility, and demonstrates the functionality of the feedback tool.

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