When designing socially assistive robots, HRI researchers often focus on robot personality as a means of increasing a person’s engagement, enjoyment, and trust. In this work, we argue that using only trait-based personality models is often limited in its ability to capture the nuance that matches end users’ desires, experiences, and cultural backgrounds. To address this gap, we introduce the concept of a robot character , a holistic framing of robot personality that extends the trait-based approach to include external factors, such as shared interests between the user and robot, as sociocultural and environmental factors. We introduced and validated the Robot Role Character Creation (R2C2) tool, an accessible scaffolding tool to co-design robot characters with end users in order to support more nuanced and personalized robots. R2C2 highlights the voices of end users and enables them to easily ideate and communicate their unique robot characters, particularly for populations often underrepresented in robot design. Through a cross-cultural study (the U.S. and Mexico), we validated the R2C2 tool in eliciting rich design insights for robot characters from people with mild cognitive impairment (MCI) and dementia (PwD). We report our findings, enabled by the R2C2 tool, on the role participants envisioned for their desired robot characters, the multidimensionality and adaptability of these robot characters, and how participants’ socio-cultural backgrounds influenced their characters. Our findings demonstrate that R2C2 can facilitate the creation of nuanced and personalized robot characters that resonate with user experiences, needs, and preferences. We analyze how participants envisioned the roles, multidimensionality, and cultural influences shaping their ideal robot characters, highlighting R2C2’s ability to capture these diverse perspectives. This work will serve as a basis for HRI designers to create more effective robot interactions, enhance acceptance and trust, and promote engagement with robot characters while centering the wisdom and personhood of people with cognitive impairments.

Many HRI researchers have engaged in participatory research to include users in robot design processes. However, to our knowledge, people with mild cognitive impairment (PwMCI) and early stage dementia have yet to be included in developing and programming robots, and the HRI community lacks tools to facilitate their inclusion. We bridge this gap by introducing PODER (PrOgramming framework to Develop Robot behaviors), which enables a lived technology experience for PwMCI via scaffolding, peer programming, and development tools to support them as key developers of social robots. We conducted a study where PwMCI and early stage dementia used PODER to program robot interactions, and found that participants were highly engaged and deeply enjoyed their experience, creating programs for robots that reflected their interests, experiences, and needs. Our results show the impact of including participants with MCI and early stage dementia in robot programming, including an increased understanding of technology, shifting their perceived role from technology users to programmers, and desire to be involved with the end-to-end process. By releasing PODER to the community, we hope this work can facilitate the intentional inclusion of people with cognitive impairments in further HRI research.

People with dementia (PwD) often exhibit behavioral, physiological, and social symptoms that affect their relationships and reduce their quality of life. Socially assistive robots (SARs) have proven effective in reducing some of these symptoms. However, the design of SARs for dementia care remains a challenging task. In this article, we present a human-centered design framework to assist researchers, designers, and roboticist from the conceptualization of engaging prototypes of SARs until the assessment of their efficacy at producing dementia-relevant health outcomes. The framework incorporates several key principles of a Human-Centered Artificial Intelligence (HCAI) approach. It incorporates the needs and perspectives of the different stakeholders into the design and evaluation of the interactions. It fosters accessibility and inclusion through personalization and encourages human–robot collaboration by facilitating natural and engaging interactions that build trust and rapport. We illustrate the use of the framework with the design of an SAR to support a Cognitive Stimulation Therapy. From this case study, we derive a set of design insights for the development of SARs behaviors and interactions for dementia care.

Cognitively assistive robots (CARs) have great potential to extend the reach of clinical interventions to the home. Due to the wide variety of cognitive abilities and rehabilitation goals, these systems must be flexible to support rapid and accurate implementation of intervention content that is grounded in existing clinical practice. To this end, we detail the system architecture of CARMEN (Cognitively Assistive Robot for Motivation and Neurorehabilitation), a flexible robot system we developed in collaboration with our key stakeholders: clinicians and people with mild cognitive impairment (PwMCI). We implemented a well-validated compensatory cognitive training (CCT) intervention on CARMEN, which it autonomously delivers to PwMCI. We deployed CARMEN in the homes of these stakeholders to evaluate and gain initial feedback on the system. We found that CARMEN gave participants confidence to use cognitive strategies in their everyday life, and participants saw opportunities for CARMEN to exhibit greater levels of autonomy or be used for other applications. Furthermore, elements of CARMEN are open source to support flexible home-deployed robots. Thus, CARMEN will enable the HRI community to deploy quality interventions to robots, ultimately increasing their accessibility and extensibility.

Many robot-delivered health interventions aim to support people longitudinally at home to complement or replace in-clinic treatments. However, there is little guidance on how robots can support collaborative goal setting (CGS). CGS is the process in which a person works with a clinician to set and modify their goals for care; it can improve treatment adherence and efficacy. However, for home-deployed robots, clinicians will have limited availability to help set and modify goals over time, which necessitates that robots support CGS on their own. In this work, we explore how robots can facilitate CGS in the context of our robot CARMEN (Cognitively Assistive Robot for Motivation and Neurorehabilitation), which delivers neurorehabilitation to people with mild cognitive impairment (PwMCI). We co-designed robot behaviors for supporting CGS with clinical neuropsychologists and PwMCI, and prototyped them on CARMEN. We present feedback on how PwMCI envision these behaviors supporting goal progress and motivation during an intervention. We report insights on how to support this process with home-deployed robots and propose a framework to support HRI researchers interested in exploring this both in the context of cognitively assistive robots and beyond. This work supports designing and implementing CGS on robots, which will ultimately extend the efficacy of robot-delivered health interventions.

Much research in healthcare robotics explores extending rehabilitative interventions to the home. However, for adults, little guidance exists on how to translate human-delivered, clinic-based interventions into robot-delivered, home-based ones to support longitudinal interaction. This is particularly problematic for neurorehabilitation, where adults with cognitive impairments require unique styles of interaction to avoid frustration or overstimulation. In this paper, we address this gap by exploring the design of robot-delivered neurorehabilitation interventions for people with mild cognitive impairment (PwMCI). Through a multi-year collaboration with clinical neuropsychologists and PwMCI, we developed robot prototypes which deliver cognitive training at home. We used these prototypes as design probes to understand how participants envision long-term deployment of the intervention, and how it can be contextualized to the lives of PwMCI. We report our findings and specify design patterns and considerations for translating neurorehabilitation interventions to robots. This work will serve as a basis for future endeavors to translate cognitive training and other clinical interventions onto a robot, support longitudinal engagement with home-deployed robots, and ultimately extend the accessibility of longitudinal health interventions for people with cognitive impairments.

Socially Assistive Robots (SARs) have successfully been used in various types of health therapies as non-pharmacological interventions. A SAR called EVA (Embodied Voice Assistant) is an open-source robotics platform intended to serve as a tool to support research in Human-Robot Interaction. The EVA robot was originally developed to assist in non-pharmacological interventions for people with Dementia and has more recently been applied for children with Autism Spectrum Disorder. EVA provides multimodal interactions such as verbal and non-verbal communication, facial recognition and light sensory effects. Although EVA uses low-cost hardware and open-source software, it is not always possible, or practical, to have a physical robot at hand, particularly during rapid iterative cycles of design and evaluation of therapies. Thus, our motivation to develop a simulator that allows testing the scripts of therapies to be enacted by the EVA robot. This work proposes EvaSIM (EVA Robot Simulator), a simulator that can interpret an EVA script code and emulate the multimodal interaction capabilities of the physical robot, such as Text-To-Speech, facial expression recognition, controlling light sensory effects, etc. Several EVA scripts were run using the simulator attesting that they have the same behaviour as the physical robot. EvaSIM can serve as a support tool in the teaching/learning process of the robot’s scripting language, enabling the training of technicians and therapists in script development and testing for the EVA robot.

Several studies have been reported on the use of social robots for dementia care. These robots have been used for diverse tasks such as for companionship, as an exercise coach, and as daily life assistant. However, most of these studies have assessed impact on participants only at the time when the interaction takes place rather than their medium or long-term effects. In this work, we report on a nine-week study conducted in a nursing home in which a autonomous social robot, called Eva, acts as facilitator of a cognitive stimulation therapy (CST). During the study, eight persons with dementia interacted with the robot in a group session which included elements of music therapy, reminiscence, cognitive games, and relaxation. Using the Neuropsychiatric Inventory - Nursing Home version (NPI-NH), we analyzed the impact of the therapy guided by the robot. The results show a statistically significant decrease in the total score of NPI-NH. Also, three dementia-related symptoms: delusions, agitation/aggression, and euphoria/exaltation, show a statistically significant decrease after the intervention. In addition, a qualitative analysis on interviews conducted with caregivers shows that all participants exhibits positive short-term effects after the session and provides insights on why some changes in behavior prevailed beyond the therapy sessions. Our results provide evidence that a social robot could play a role in improving the quality of life of persons with dementia.

Nowadays there are models and standards which attempt to introduce quality in the enterprises' software development process with the objective to introduce high quality levels in the produced software. The NMX-I-059/02-NYCE-2005 standard (also known as MoProSoft) is focused on small and medium software enterprises, or small groups of software development within a larger organization, with the aim of promoting the standardization of an effective process in the software industry. Mexican enterprises now have a software standard that enables them to achieve a high level of quality in the software that they produce. However, the adoption of any standard is not an easy task. This paper aims to show that the development and implementation of a RIA-based tool that could support improvement initiatives, therefore strengthening the standard adoption.