Co-design of a digital app "WhatMatters" to support person-centred care: A critical reflection.

BACKGROUND: People with dementia often do not receive optimal person-centred care (PCC) in care settings. Family members can play a vital role as care partners to support the person with dementia with their psychosocial needs. Participatory research that includes the perspectives of those with lived experience is essential for developing high-quality dementia care and practices. OBJECTIVE: Throughout 2021-2022, a mobile app, called WhatMatters, was co-developed to provide easy-to-access and personalised support for people with dementia in hospitals and long-term care homes, with input from patients/residents, family partners and healthcare staff. This article discusses and critically reflects on the experiences of patients/residents, family partners, and healthcare staff involved in the co-design process. METHODS: For the app development, we applied a participatory co-design approach, guided by a User Experience (UX) model. The process involved co-design workshops and user testing sessions with users (patients/residents, family partners, healthcare staff) to co-develop the WhatMatters prototype. We also conducted focus groups and one on one interviews with staff and caregiver participants to explore their experiences. Our research team, which also included patient partners, took part in regular team meetings during the app's development, where we discussed and reflected on the co-design process. Reflexive thematic analysis was performed to identify themes that represent the challenges and rewarding experiences of the users involved in the co-design process, which guided our overall reflective process. FINDINGS: Our reflective analysis identified five themes (1) clarifying the co-design process, (2) ensuring inclusive collaborations of various users, and (3) supporting expression of emotion in a virtual environment, (4) feeling a sense of achievement and (5) feeling valued. IMPLICATIONS: WhatMatters offers potential for providing personally relevant and engaging resources in dementia care. Including the voices of relevant users is crucial to ensure meaningful benefits for patients/residents. We offer insights and lessons learned about the co-design process, and explore the challenges of involving people with lived experiences of dementia in co-design work, particularly during the pandemic.

Investigating the Role of Culture on Negative Emotion Expressions in the Wild.

Even though culture has been found to play some role in negative emotion expression, affective computing research primarily takes on a basic emotion approach when analyzing social signals for automatic emotion recognition technologies. Furthermore, automatic negative emotion recognition systems still train data that originates primarily from North America and contains a majority of Caucasian training samples. As such, the current study aims to address this problem by analyzing what the differences are of the underlying social signals by leveraging machine learning models to classify 3 negative emotions, contempt, anger and disgust (CAD) amongst 3 different cultures: North American, Persian, and Filipino. Using a curated data set compiled from YouTube videos, a support vector machine (SVM) was used to predict negative emotions amongst differing cultures. In addition a one-way ANOVA was used to analyse the differences that exist between each culture group in-terms of level of activation of underlying social signal. Our results not only highlighted the significant differences in the associated social signals that were activated for each culture, but also indicated the specific underlying social signals that differ in our cross-cultural data sets. Furthermore, the automatic classification methods showed North American expressions of CAD to be well-recognized, while Filipino and Persian expressions were recognized at near chance levels.

A Deeper Look at Autonomous Vehicle Ethics: An Integrative Ethical Decision-Making Framework to Explain Moral Pluralism.

The autonomous vehicle (AV) is one of the first commercialized AI-embedded robots to make autonomous decisions. Despite technological advancements, unavoidable AV accidents that result in life-and-death consequences cannot be completely eliminated. The emerging social concern of how an AV should make ethical decisions during unavoidable accidents is referred to as the moral dilemma of AV, which has promoted heated discussions among various stakeholders. However, there are research gaps in explainable AV ethical decision-making processes that predict how AVs' moral behaviors are made that are acceptable from the AV users' perspectives. This study addresses the key question: What factors affect ethical behavioral intentions in the AV moral dilemma? To answer this question, this study draws theories from multidisciplinary research fields to propose the "Integrative ethical decision-making framework for the AV moral dilemma." The framework includes four interdependent ethical decision-making stages: AV moral dilemma issue framing, intuitive moral reasoning, rational moral reasoning, and ethical behavioral intention making. Further, the framework includes variables (e.g., perceived moral intensity, individual factors, and personal moral philosophies) that influence the ethical decision-making process. For instance, the framework explains that AV users from Eastern cultures will tend to endorse a situationist ethics position (high idealism and high relativism), which views that ethical decisions are relative to context, compared to AV users from Western cultures. This proposition is derived from the link between individual factors and personal moral philosophy. Moreover, the framework proposes a dual-process theory, which explains that both intuitive and rational moral reasoning are integral processes of ethical decision-making during the AV moral dilemma. Further, this framework describes that ethical behavioral intentions that lead to decisions in the AV moral dilemma are not fixed, but are based on how an individual perceives the seriousness of the situation, which is shaped by their personal moral philosophy. This framework provides a step-by-step explanation of how pluralistic ethical decision-making occurs, reducing the abstractness of AV moral reasoning processes.

SFU-store-nav: A multimodal dataset for indoor human navigation.

This article describes a dataset collected in a set of experiments that involves human participants and a robot. The set of experiments was conducted in the computing science robotics lab in Simon Fraser University, Burnaby, BC, Canada, and its aim is to gather data containing common gestures, movements, and other behaviours that may indicate humans' navigational intent relevant for autonomous robot navigation. The experiment simulates a shopping scenario where human participants come in to pick up items from his/her shopping list and interact with a Pepper robot that is programmed to help the human participant. We collected visual data and motion capture data from 108 human participants. The visual data contains live recordings of the experiments and the motion capture data contains the position and orientation of the human participants in world coordinates. This dataset could be valuable for researchers in the robotics, machine learning and computer vision community.