Silvia Maria Massa

Large language models (LLMs) are driving significant advancements across various sectors. Combined with robotics, they can lay the foundation for a new paradigm in healthcare, particularly in elderly care and companionship. For years, research has focused on the concept of carebots and robot companions. The latest capabilities of LLMs and robotics promise to address many challenges regarding interaction and personalized experience faced by earlier generations of companion robots. However, the potential of these emerging technologies is accompanied by significant ethical, technical, and social challenges. This chapter presents the design of an elderly care robot that exploits the capabilities of the latest generation of LLMs and considers the ethical, technical, and social implications for improving human-robot interaction capabilities. We report the technical design of a novel robotic platform for elderly care, detailing the devised interaction scenarios, a prototype implementation with NAO and PEPPER robots, and the results of a technical validation that shows the efficiency and feasibility of our system. This study serves as a guide to identify future needs, explore the impact of companion robots on older adults, and understand acceptance of these new approaches.

Smart sensory devices, such as smart watches, scales, and blood pressure gauges, are increasingly adopted by individuals aiming to improve their health and fitness. Those devices gather extensive data about cardiovascular parameters, physical activities, sleep quality, and behavior. Thanks to data analytics and artificial intelligence algorithms, they provide insights into the health status of individuals. Derived data is used to support self-care interventions and to provide practitioners with additional health information acquired on a continuous basis. However, most of the current solutions focus on the physical dimension of health, while the mental dimension is often neglected. In this paper, we present the initial investigation of a system to recognize a wide range of psychological parameters, including behavioral inhibition/activation, anxiety, and stress, leveraging data acquired from personal healthcare devices. We experimented with the application of different supervised learning algorithms on features extracted from heart, sleep, and inertial sensor data acquired from a cohort of 21 individuals over 24 hours each. Our preliminary findings suggest that our method may yield promising outcomes in recognizing different aspects of mental well-being. However, due to the limited size of the used dataset, a more comprehensive experimental evaluation, with a broader number of participants and carried out over an extended monitoring period, is imperative to substantiate the results.

As several studies demonstrate, good sleep quality is essential for individuals’ well-being, as a lack of restoring sleep may disrupt different physical, mental, and social dimensions of health. For this reason, there is increasing interest in tools for the monitoring of sleep based on personal sensors. However, there are currently few context-aware methods to help individuals to improve their sleep quality through behavior change tips. In order to tackle this challenge, in this paper, we propose a system that couples machine learning algorithms and large language models to forecast the next night’s sleep quality, and to provide context-aware behavior change tips to improve sleep. In order to encourage adherence and to increase trust, our system includes the use of large language models to describe the conditions that the machine learning algorithm finds harmful to sleep health, and to explain why the behavior change tips are generated as a consequence. We develop a prototype of our system, including a smartphone application, and perform experiments with a set of users. Results show that our system’s forecast is correlated to the actual sleep quality. Moreover, a preliminary user study suggests that the use of large language models in our system is useful in increasing trust and engagement.

The proliferation of sensors in smart homes makes it possible to monitor human activities, routines, and complex behaviors in an unprecedented way. Hence, human activity recognition has gained increasing attention over the last few years as a tool to improve healthcare and well-being in several applications. However, most existing activity recognition systems rely on cameras or wearable sensors, which may be obtrusive and may invade the user’s privacy, especially at home. Moreover, extracting expressive features from a stream of data provided by heterogeneous smart-home sensors is still an open challenge. In this paper, we investigate a novel method to detect activities of daily living by exploiting unobtrusive smart-home sensors (i.e., passive infrared position sensors and sensors attached to everyday objects) and vision-based deep learning algorithms, without the use of cameras or wearable sensors. Our method relies on depicting the locomotion traces of the user and visual clues about their interaction with objects on a floor plan map of the home, and utilizes pre-trained deep convolutional neural networks to extract features for recognizing ongoing activity. One additional advantage of our method is its seamless extendibility with additional features based on the available sensor data. Extensive experiments with a real-world dataset and a comparison with state-of-the-art approaches demonstrate the effectiveness of our method.