I am Robot, Your Health Adviser for Older Adults: Do You Trust My Advice?

Artificial intelligence and robotic solutions are seeing rapid development for use across multiple occupations and sectors, including health and social care. As robots grow more prominent in our work and home environments, whether people would favour them in receiving useful advice becomes a pressing question. In the context of human–robot interaction (HRI), little is known about people’s advice-taking behaviour and trust in the advice of robots. To this aim, we conducted an experimental study with older adults to measure their trust and compliance with robot-based advice in health-related situations. In our experiment, older adults were instructed by a fictional human dispenser to ask a humanoid robot for advice on certain vitamins and over-the-counter supplements supplied by the dispenser. In the first experimented condition, the robot would give only information-type advice, i.e., neutral informative advice on the supplements given by the human. In the second condition, the robot would give recommendation-type advice, i.e., advice in favour of more supplements than those suggested initially by the human. We measured the trust of the participants in the type of robot-based advice, anticipating that they would be more trusting of information-type advice. Moreover, we measured the compliance with the advice, for participants who received robot-based recommendations, and a closer proxy of the actual use of robot health advisers in home environments or facilities in the foreseeable future. Our findings indicated that older adults continued to trust the robot regardless of the type of advice received, highlighting a type of protective role of robot-based recommendations on their trust. We also found that higher trust in the robot resulted in higher compliance with its advice. The results underpinned the likeliness of older adults welcoming a robot at their homes or health facilities

A Conversational Agent in mHealth for Self-Management of Parkinson’s Disease

Nos dias que correm vivemos rodeados de tecnologia, onde os “smartphones” preenchem um espaço muito importante nas nossas vidas. O uso de serviços móveis pelos “smartphones” no âmbito da saúde tem sido cada vez mais próspero, com um uso acessível por parte de todos. Com os avanços ao nível de inteligência artificial, especialmente no que toca à criação de sistemas inteligentes que comuniquem de forma natural com os humanos, torna-se possível criar agentes de conversação adequados para uma interação pessoa-máquina com distintos objetivos. Um dos objetivos que o projeto ONParkinson tem é o de aumentar a adesão terapêutica por parte das pessoas com doença de Parkinson. Sendo que a execução recorrente de exercício físico é essencial na gestão dos sintomas da doença de Parkinson. Por isso, existe a necessidade de interagir, educar e motivar os pacientes com doença de Parkinson para uma maior adesão aos exercícios terapêuticos. Este trabalho propõe uma solução, no âmbito do projeto ONParkinson, que envolve a criaçãao de um agente de conversação com unidades de conhecimento mais focadas nos exercicios terapêuticos e com unidades que visam motivar e manter a pessoa com doença de Parkinson motivada para a realização de exercícios terapêuticos. A avaliação da solução envolve fisioterapeutas e pessoas com doença de Parkinson. O plano de avaliação estabelece o estudo do desempenho técnico, da experiência do utilizador e da investigação na área da Saúde. Grande parte do conjunto dos pacientes com doença de Parkinson tem uma idade avançada, o que poderia levar a uma maior resistência ao uso das novas tecnologias. No entanto, os valores obtidos nos indicadores referentes à perspetiva de utilidade, facilidade de uso e satisfação da utilização demonstram um bom nível de usabilidade da solução proposta. Como a investigação de eficácia clínica ainda não foi conduzida, não é possível concluir a efiácia da solução proposta no aumento da adesão terapêutica por parte dos pacientes com doença de Parkinson.Nowadays, we live surrounded by technology, where smartphones fill a very important space in our lives. The use of mobile services by smartphones in the health sector has been increasingly prosperous, with accessible use by everyone. With advances in artificial intelligence methodologies, regarding the creation of intelligent systems that communicate naturally with humans, it is possible to create conversational agents for person-machine interaction with different objectives. One of the goals of the ONParkinson project is to increase therapeutic adherence by people with Parkinson’s disease. The recurrent execution of physical exercise is essential in the management of the symptoms of Parkinson’s disease. Therefore, there is a need to interact, educate and motivate patients with Parkinson’s disease for greater adherence to therapeutic exercises. This work proposes a solution, within the scope of the ONParkinson project, which involves the creation of a conversation agent with units of knowledge more focused on therapeutic exercises and with units aiming to motivate and keep the person with Parkinson’s disease motivated to perform therapeutic exercises. The evaluation of the solution involves physical therapists and patients with Parkinson’s disease. The evaluation plan establishes the study of technical performance, the study of user experience and Health research study. A large part of the set of patients with Parkinson’s disease is of advanced age, which could lead to greater resistance to the use of new technologies. However, the values obtained in the indicators referring to the perception of usefulness, ease of use and interaction satisfaction demonstrate a good level of usability of the proposed solution. As the investigation of clinical efficacy has not yet been conducted, it is not possible to conclude the effectiveness of the proposed solution in increasing therapeutic adherence by patients with Parkinson’s disease

Training Effects of Adaptive Emotive Responses From Animated Agents in Simulated Environments

Humans are distinct from machines in their capacity to emote, stimulate, and express emotions. Because emotions play such an important role in human interactions, human-like agents used in pedagogical roles for simulation-based training should properly reflect emotions. Currently, research concerning the development of this type of agent focuses on basic agent interface characteristics, as well as character building qualities. However, human-like agents should provide emotion-like qualities that are clearly expressed, properly synchronized, and that simulate complex, real-time interactions through adaptive emotion systems. The research conducted for this dissertation was a quantitative investigation using 3 (within) x 2 (between) x 3 (within) factorial design. A total of 56 paid participants consented to complete the study. Independent variables included emotion intensity (i.e., low, moderate, and high emotion), levels of expertise (novice participant versus experienced participant), and number of trials. Dependent measures included visual attention, emotional response towards the animated agents, simulation performance score, and learners\u27 perception of the pedagogical agent persona while participants interacted with a pain assessment and management simulation. While no relationships were indicated between the levels of emotion intensity portrayed by the animated agents and the participants\u27 visual attention, emotional response towards the animated agent, and simulation performance score, there were significant relationships between the level of expertise of the participant and the visual attention, emotional responses, and performance outcomes. The results indicated that nursing students had higher visual attention during their interaction with the animated agents. Additionally, nursing students expressed more neutral facial expression whereas experienced nurses expressed more emotional facial expressions towards the animated agents. The results of the simulation performance scores indicated that nursing students obtained higher performance scores in the pain assessment and management task than experienced nurses. Both groups of participants had a positive perception of the animated agents persona

A semantic memory bank assisted by an embodied conversational agents for mobile devices

Alzheimer’s disease is a type of dementia that causes memory loss and interferes with intellectual abilities seriously. It has no current cure and therapeutic efficiency of current medication is limited. However, there is evidence that non-pharmacological treatments could be useful to stimulate cognitive abilities. In the last few year, several studies have focused on describing and under- standing how Virtual Coaches (VC) could be key drivers for health promotion in home care settings. The use of VC gains an augmented attention in the considerations of medical innovations. In this paper, we propose an approach that exploits semantic technologies and Embodied Conversational Agents to help patients training cognitive abilities using mobile devices. In this work, semantic technologies are used to provide knowledge about the memory of a specific person, who exploits the structured data stored in a linked data repository and take advantage of the flexibility provided by ontologies to define search domains and expand the agent’s capabilities. Our Memory Bank Embodied Conversational Agent (MBECA) is used to interact with the patient and ease the interaction with new devices. The framework is oriented to Alzheimer’s patients, caregivers, and therapists

Comparing Older and Younger Adults Perceptions of Voice and Text-based Search for Consumer Health Information Tasks

The increased prevalence of voice search presents opportunities to address consumer challenges accessing online health information. However, it is essential to understand how users’ perceptions of voice affect their search processes for health information, concerns, and different scenarios for using voice for health information tasks. We conducted semi-structured interviews with 16 younger (18-25) and older (60-64) adult participants to understand and compare their perceptions of using voice and text-based search for non-health-related and health related tasks. While most participants preferred traditional text search, younger adults were not inclined to use voice search for health information due to concerns about privacy, credibility, and perceived efficiency in filtering results. Older adults found voice search potentially beneficial for reducing manual query generation burdens; however, some were unsure of how to use the technology effectively. We provide a set of considerations to address concerns about voice search for health information tasks in the futur

LLM-Powered Conversational Voice Assistants: Interaction Patterns, Opportunities, Challenges, and Design Guidelines

Conventional Voice Assistants (VAs) rely on traditional language models to discern user intent and respond to their queries, leading to interactions that often lack a broader contextual understanding, an area in which Large Language Models (LLMs) excel. However, current LLMs are largely designed for text-based interactions, thus making it unclear how user interactions will evolve if their modality is changed to voice. In this work, we investigate whether LLMs can enrich VA interactions via an exploratory study with participants (N=20) using a ChatGPT-powered VA for three scenarios (medical self-diagnosis, creative planning, and debate) with varied constraints, stakes, and objectivity. We observe that LLM-powered VA elicits richer interaction patterns that vary across tasks, showing its versatility. Notably, LLMs absorb the majority of VA intent recognition failures. We additionally discuss the potential of harnessing LLMs for more resilient and fluid user-VA interactions and provide design guidelines for tailoring LLMs for voice assistance

"Mango Mango, How to Let The Lettuce Dry Without A Spinner?'': Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking Partner

The rapid advancement of the Large Language Model (LLM) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. However, users' real-world experiences interacting with these assistants remain unexplored. In this research, we chose cooking, a complex daily task, as a scenario to investigate people's successful and unsatisfactory experiences while receiving assistance from an LLM-based CA, Mango Mango. We discovered that participants value the system's ability to provide extensive information beyond the recipe, offer customized instructions based on context, and assist them in dynamically planning the task. However, they expect the system to be more adaptive to oral conversation and provide more suggestive responses to keep users actively involved. Recognizing that users began treating our LLM-CA as a personal assistant or even a partner rather than just a recipe-reading tool, we propose several design considerations for future development.Comment: Under submission to CHI202