The chapter examines how robotic systems and ambient intelligence can improve care for older adults in both residential aged care homes and private homes, highlighting the extreme and challenging nature of these environments. It reviews embodied and non‑embodied solutions for daily living, safety monitoring, and social engagement, emphasizing hybrid deployments that combine robots with fixed and wearable sensors. Design considerations focus on accessibility, privacy, trust, and practical evaluation metrics such as false alarms, fault recovery time, staff workload, and long‑term reliability, while outlining future research challenges for scalable, regulation‑ready deployments.
By David Ahmedt-Aristizabal, Mohammad Ali Armin, Akila Pemasiri, Harshala Gammulle, Tharindu Fernando, David Silvera-Tawil, Simon Denman
arXiv:2606. 19247v1 Announce Type: cross Abstract: Family members caring for individuals with Alzheimer's disease and related dementias (AD/ADRD) provide the foundation of long-term care worldwide.
By Keran Wang, Drishti Goel, Jiayue Melissa Shi, Violeta J. Rodriguez, Daniel S. Brown, Dong Whi Yoo, Ravi Karkar, Koustuv Saha
Artificial intelligence (AI) systems are increasingly used across domains to provide personalized information, recommendations, and decision support. However, in some contexts, AI-generated information may not be suitable for direct delivery to the final recipient.
arXiv:2608. 19950v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are increasingly used across domains to provide personalized information, recommendations, and decision support.
By Nini Kurashvili, Yana Ivanchenko, Greta Schiavo, Cansu Koyuturk, Dimitri Ognibene
The paper surveys the evolution of smart glasses into AI smart glasses, framing them as wearable intelligence platforms that integrate egocentric sensing, resource-aware computing, intelligent reasoning, multimodal interaction, and real-world constraints for personalized assistance. It organizes the discussion into four dimensions: hardware foundations, wearable intelligence, interaction design, and application scenarios across healthcare, accessibility, learning, daily life, tourism, and industry. The authors identify five cross-cutting research challenges—next-generation hardware, trustworthy egocentric intelligence, lifelong personalized memory, proactive intelligence, and embodied foundation models—to guide future work.
By Xu Yuan, Yi Wang, Zhuohang Jiang, Haohao Qu, Yujuan Ding, Shanru Lin, Guoliang Xing, Hongxia Yang, Jiannong Cao, Qing Li, Wenqi Fan
arXiv:2606. 10208v1 Announce Type: cross Abstract: Demand for older-adult and patient care is growing rapidly as populations age worldwide.
By Zhiwen Qiu, Wei Liu, Yuexing Hao
While personalisation is becoming a defining capability in human-robot interaction (HRI), the existing literature on responsible personalisation remains fragmented, offering isolated accounts of ethical risks without a structured understanding of how they emerge across interaction contexts. This gap is particularly critical in HRI, where robots' embodiment and social presence can amplify and reshape such risks or generate new types of risks.
arXiv:2606. 01912v1 Announce Type: new Abstract: Smart homes are evolving toward complex state-dependent living environments, requiring Large Language Models (LLMs) to reason over user intent, preferences, and multi-device interactions.
By Kuan Li, Shuo Zhang, Huacan Wang, Fangzhou Yu, Zecheng Sheng, Yi Gu, Weipeng Ming, Lei Xue, Chen Liu, Sen Hu, Ronghao Chen, Siyue Lin, Yuqing Hou, Xiaofeng Mou, Yi Xu
The paper surveys the evolution of smart glasses from simple capture devices to first‑person intelligence platforms that integrate human perception, context, and action. It introduces a unified framework that formalizes data flow, hardware capabilities, and seven foundational capabilities, and presents an L0‑L5 hierarchy for capture to embodied action. The study also maps nine application scenes, proposes a nine‑dimensional deployment framework, and outlines an evidence ladder for evaluation and trustworthiness.
By Jiangning Zhang, Haojun Chen, Yong Liu
Eliciting explainable AI (XAI) requirements from stroke survivors presents a methodological challenge with direct implications for the design of trustworthy brain-computer interfaces for rehabilitation. How can patients and caregivers articulate preferences about algorithmic transparency when they lack conceptual frameworks for explainability, and when standard elicitation approaches are structurally inadequate for users with acquired communication disorders?
We’re rolling out parental controls and a new parent resource page to help families guide how ChatGPT works in their homes.
An information theory-based framework is proposed in attempt to explain insistence on sameness in autism as an instance of a general behavior pattern in which an individual tries to reduce surprise and uncertainty. It offers a new definition of autism as an impairment in which cognitive functions are restricted to discrimination, memorization and prediction of tangible properties of the environment.