Summary:
The studies show that assistive robots are making life better for people who need extra help, like the elderly or those with dementia and developmental disorders. They improve emotional well-being, make activities more engaging, and promote independence. Features like sensory feedback and AI help users stay connected and supported. However, some robots still can’t do physical tasks, and their high cost makes them hard to afford. Also, many systems are only tested on small groups, so it’s unclear how well they work for everyone. Improvements could focus on making robots cheaper, adding physical assistance, and creating better ways for them to adapt to users’ needs. With these changes, robots could help even more people live comfortably and independently.
1) A Multirobot System in an Assisted Home Environment to Support the Elderly in Their Daily Lives:
Assistive robotics and Ambient Assisted Living (AAL) have emerged as key solutions to support the growing elderly population by integrating smart sensors and AI-driven robots for independent living. Prior research on social robots like Paro, Aibo, and humanoid assistants such as Romeo2 has shown improvements in companionship and interaction but lacks physical assistance capabilities. Multirobot systems, including Samsung’s BotCare and the H2020 ENRICHME project, provide interactive support but do not perform household tasks. The HIMTAE system addresses this gap by combining a small social robot for emotional well-being with a domestic manipulator for household tasks, leveraging AI for mood prediction using wearable sensors like Empatica E4. Advanced navigation and planning techniques, such as Fast Marching Methods (FM2), enable real-time obstacle avoidance and efficient task execution. While these technologies offer significant benefits, challenges remain in affordability, user adaptability, and AI reliability. Future research should focus on improving human-robot interaction, cost-effectiveness, and multirobot collaboration to enhance real-world deployment and accessibility.
2) A Novel Implementation of a Social Robot for Sustainable Human Engagement in Homecare Services for Ageing Populations :
The study presents a social robot designed to enhance human engagement in elderly home care, addressing issues like social isolation and caregiver shortages. Previous research highlights the benefits of humanoid robots in elderly care, with systems like Puffy and NICO utilizing AI for emotion recognition and adaptive interactions. Existing solutions, such as ElliQ and SAM, provide companionship but lack deep emotional intelligence. This paper introduces an advanced humanoid robot with emotional recognition, personalized responses, and soft-skill integration. The system employs AI-driven facial expression analysis using neural networks and Nvidia Jetson Nano for real-time mood detection. Unlike prior models, it integrates expressive eye movements and adaptive learning for sustained engagement. Pilot studies in nursing homes validate its potential to improve mental well-being and reduce loneliness. Future research should focus on large-scale deployment and improved human-robot interaction to ensure widespread adoption in elderly care.
3) Assistive Robot with an AI-Based Application for the Reinforcement of Activities of Daily Living: Technical Validation with Users Affected by Neurodevelopmental Disorders:
The study presents the development and technical validation of an assistive robotic platform, LOLA2, aimed at supporting individuals with neurodevelopmental disorders in learning and reinforcing daily living activities. Featuring AI-based online action detection, a user-friendly interface, and a ROS-based navigation system, LOLA2 enables real-time action monitoring, video tutorials, and progress tracking. Tested with four users, it demonstrated high accuracy in detecting actions and received positive feedback from both patients and healthcare professionals, showcasing its potential for improving therapy sessions despite some challenges due to user physical limitations.
4) Enhancing Elderly Health Monitoring: Achieving Autonomous and Secure Living through the Integration of Artificial Intelligence, Autonomous Robots, and Sensors:
The article explores the integration of artificial intelligence (AI), autonomous robots, and sensors to enhance elderly healthcare within the Internet of Robotic Things (IoRT). It introduces a humanoid robot, QTrobot, which collaborates with caregivers, medical personnel, and elderly individuals to ensure continuous health monitoring, manage emotional states, and alert caregivers to abnormalities. The system relies on the Modified Early Warning Score (MEWS) for health prediction, offering real-time data updates via a Telegram bot. By focusing on user-centered design and engaging geriatricians, the framework aims to promote independent living and reduce hospital dependence, with future enhancements planned for more dynamic health monitoring solutions.
5) A CareRobot with Ethical Sensing System for Older Adults at Home:
The study develops and validates Dori, a robot designed to support prefrail and frail older adults at home, based on the Human-Centered Artificial Intelligence (HCAI) framework. Equipped with sensors, pose recognition, and caregiver-monitored settings, it provides services like cognitive and emotional activities, medication management, and fall detection while respecting user dignity and privacy. Interviews with caregivers and medical staff shaped its ethical design and services. Despite minor feedback on improvements for physical activities, initial user satisfaction was positive, showcasing its potential for future deployment in eldercare.
6) Context-Enhanced Human-Robot Interaction: Exploring the Role of System Interactivity and Multimodal Stimuli on the Engagement of People with Dementia:
The study explores how multimodal stimuli and system interactivity can enhance engagement for people with dementia (PWD) in long-term care. Using a robot and an augmented reality display called LiveNature, the study incorporated tactile, visual, and auditory feedback. The results showed that multimodal stimuli significantly improved PWD’s emotional engagement, verbal interaction, and attitude towards activities while also fostering social connection with caregivers. However, the study found no conclusive effects for system interactivity. It suggests that sensory richness and thoughtful facilitation play crucial roles in designing effective interventions for dementia care.
7) Context-Enhanced Human-Robot Interaction: Exploring the Role of System Interactivity and Multimodal Stimuli on the Engagement of People with Dementia:
The study investigates how system interactivity and multimodal stimuli enhance engagement for individuals with dementia in long-term care settings. Using a robot-assisted installation named LiveNature, which combines a social robot and an augmented reality display, researchers found that multimodal stimuli—such as combining tactile, visual, and auditory feedback—significantly improved participants' attitude, behavioral engagement, and social interaction. However, system interactivity had less noticeable effects, possibly due to cognitive challenges among participants. This research emphasizes the potential of multisensory design for promoting engagement and social connection in dementia care.
8) Human guided cooperative robotic agents in smart home using beetle antennae search:
This research introduces a novel control framework for cooperative robotic agents tailored to smart-home environments, emphasizing usability, autonomy, and intelligent decision-making. Traditional control systems, often designed for industrial robots, fall short in addressing household scenarios. The framework tackles collision avoidance among multiple robots through an optimization-driven approach, leveraging the beetle antennae search zeroing neural network (BASZNN). Inspired by beetle behavior, BASZNN resolves optimization challenges without relying on gradients, enhancing computational efficiency and supporting the distributed nature of the problem. Simulations using V-REP and MATLAB with three robotic agents successfully demonstrated the system's ability to accomplish collaborative tasks with precision and reliability, showcasing its potential to revolutionize domestic robotics.
9) Semantic Grounding for Long-Term Autonomy of Mobile Robots Toward Dynamic Object Search in Home Environments:
This article introduces a semantic grounding scheme for long-term mobile robots to enhance dynamic object search in open and dynamic home environments. The approach addresses challenges like unknown object layouts and objects moved without the robot's awareness. By combining a general probabilistic model and a semantic model, the scheme prioritizes the most promising areas to search based on spatial relations between objects and room types. It also introduces a semantic grounding solution to enable robots to infer and adapt to dynamic environments. The method supports knowledge sharing across robots to improve task performance. Extensive real-world tests demonstrated that this approach outperformed other methods, reducing search time and trajectory length while achieving human-like efficiency and robustness.
10) Using Artificial Intelligence and Companion Robots to Improve Home Healthcare for the Elderly:
This paper addresses the increasing elderly population and its impact on healthcare systems, highlighting the surge in emergency room visits. Interviews with geriatrics experts reveal that elderly individuals often alarm physicians over minor health alterations, escalating costs. Mental training is identified as a practice to manage cognitive decline, with caregivers playing a critical role in dementia care. The article stresses the need to address caregiver stress and emotional well-being. It proposes an innovative caregiving solution involving AI-integrated robots to monitor elderly health and cognitive status at home, showcasing a system design involving four stakeholders and a practical usage scenario.