Promising directions for human-robot interactions defined by older adults.

Introduction: Older adults are engaging more and more with voice-based agent and social robot technologies, and roboticists are increasingly designing interactions for these systems with older adults in mind. Older adults are often not included in these design processes, yet there are many opportunities for older adults to collaborate with design teams to design future robot interactions and help guide directions for robot development. Methods: Through a year-long co-design project, we collaborated with 28 older adults to understand the key focus areas that older adults see promise in for older adult-robot interaction in their everyday lives and how they would like these interactions to be designed. This paper describes and explores the robot-interaction guidelines and future directions identified by older adults, specifically investigating the change and trajectory of these guidelines through the course of the co-design process from the initial interview to the design guideline generation session to the final interview. Results were analyzed through an adapted ethnographic decision tree modeling approach to understand older adults' decision making surrounding the various focus areas and guidelines for social robots. Results: Overall, over the course of the co-design process between the beginning and end, older adults developed a better understanding of the robot that translated to them being more certain of their attitudes of how they would like a robot to engage with them in their lives. Older adults were more accepting of transactional functions such as reminders and scheduling and less open to functions that would involve sharing sensitive information and tracking and/or monitoring of them, expressing concerns around surveillance. There was some promise in robot interactions for connecting with others, body signal monitoring, and emotional wellness, though older adults brought up concerns around autonomy, privacy, and naturalness of the interaction with a robot that need to be further explored. Discussion: This work provides guidance for future interaction development for robots that are being designed to interact with older adults and highlights areas that need to be further investigated with older adults to understand how best to design for user concerns.

Personal Narratives in Technology Design: The Value of Sharing Older Adults' Stories in the Design of Social Robots.

The storytelling lens in human-computer interaction has primarily focused on personas, design fiction, and other stories crafted by designers, yet informal personal narratives from everyday people have not been considered meaningful data, such as storytelling from older adults. Storytelling may provide a clear path to conceptualize how technologies such as social robots can support the lives of older or disabled individuals. To explore this, we engaged 28 older adults in a year-long co-design process, examining informal stories told by older adults as a means of generating and expressing technology ideas and needs. This paper presents an analysis of participants' stories around their prior experience with technology, stories shaped by social context, and speculative scenarios for the future of social robots. From this analysis, we present suggestions for social robot design, considerations of older adults' values around technology design, and promotion of participant stories as sources for design knowledge and shifting perspectives of older adults and technology.

Speed Dating with Voice User Interfaces: Understanding How Families Interact and Perceive Voice User Interfaces in a Group Setting.

As voice-user interfaces (VUIs), such as smart speakers like Amazon Alexa or social robots like Jibo, enter multi-user environments like our homes, it is critical to understand how group members perceive and interact with these devices. VUIs engage socially with users, leveraging multi-modal cues including speech, graphics, expressive sounds, and movement. The combination of these cues can affect how users perceive and interact with these devices. Through a set of three elicitation studies, we explore family interactions (N = 34 families, 92 participants, ages 4-69) with three commercially available VUIs with varying levels of social embodiment. The motivation for these three studies began when researchers noticed that families interacted differently with three agents when familiarizing themselves with the agents and, therefore, we sought to further investigate this trend in three subsequent studies designed as a conceptional replication study. Each study included three activities to examine participants' interactions with and perceptions of the three VUIS in each study, including an agent exploration activity, perceived personality activity, and user experience ranking activity. Consistent for each study, participants interacted significantly more with an agent with a higher degree of social embodiment, i.e., a social robot such as Jibo, and perceived the agent as more trustworthy, having higher emotional engagement, and having higher companionship. There were some nuances in interaction and perception with different brands and types of smart speakers, i.e., Google Home versus Amazon Echo, or Amazon Show versus Amazon Echo Spot between the studies. In the last study, a behavioral analysis was conducted to investigate interactions between family members and with the VUIs, revealing that participants interacted more with the social robot and interacted more with their family members around the interactions with the social robot. This paper explores these findings and elaborates upon how these findings can direct future VUI development for group settings, especially in familial settings.

Quantitative models of feline lumbosacral dorsal root ganglia neuronal cell density.

BACKGROUND: Dorsal root ganglia (DRG) are spinal root components that contain the cell bodies of converging primary sensory neurons. DRG are becoming a therapeutic target for electrical neural interfaces. Our purpose was to establish methods for quantifying the non-random nature and distribution of neuronal cell bodies within DRG. NEW METHOD: We identified neuronal cell body locations in 26 feline lumbosacral DRG cross-section histological images and used computational tools to quantify spatial trends. We first analyzed spatial randomness using the nearest-neighbor distance method. Next we overlaid a 6×6 grid, modeling neuronal cellular density in each grid square and comparing regions statistically. Finally we transformed DRG onto a polar map and calculated neuronal cellular density in annular sectors. We used a recursive partition model to determine regions of high and low density, and validated the model statistically. RESULTS: We found that the arrangement of neuronal cell bodies at the widest point of DRG is distinctly non-random with concentration in particular regions. The grid model suggested a radial trend in density, with increasing density toward the outside of the DRG. The polar transformation model showed that the highest neuronal cellular density is in the outer 23.9% radially and the dorsal ±61.4° angularly. COMPARISON WITH EXISTING METHODS: To our knowledge, DRG neuronal cell distribution has not been previously quantified. CONCLUSIONS: These results confirm and expand quantitatively on the existing understanding of DRG anatomy. Our methods can be useful for analyzing the distribution of cellular components of other neural structures or expanding to three-dimensional models.

Localization of Transcranial Targets for Photoacoustic-Guided Endonasal Surgeries.

Neurosurgeries to remove pituitary tumors using the endonasal, transsphenoidal approach often incur the risk of patient death caused by injury to the carotid arteries hidden by surrounding sphenoid bone. To avoid this risk, we propose intraoperative photoacoustic vessel visualization with an optical fiber attached to the surgical tool and an external ultrasound transducer placed on the temple. Vessel detection accuracy is limited by acoustic propagation properties, which were investigated with k-Wave simulations. In a two-layer model of temporal bone (3200 m/s sound speed, 1-4 mm thickness) and surrounding tissues, the localization error was ≤2 mm in the tranducer's axial dimension, while temporal bone curvature further degraded target localization. Phantom experiments revealed that multiple image targets (e.g. sphenoid bone and vessels) can be visualized, particularly with coherence-based beamforming, to determine tool-to-vessel proximity despite expected localization errors. In addition, the potential flexibility of the fiber position relative to the transducer and vessel was elucidated.