Search arXivSearch

arXiv subjects

Emma Murphy

Publications and source records attributed to Emma Murphy.

3 recordsLinked to original sources

Vision-Language System using Open-Source LLMs for Gestures in Medical Interpreter Robots

Effective communication is vital in healthcare, especially across language barriers, where non-verbal cues and gestures are critical. This paper presents a privacy-preserving vision-language framework for medical interpreter robots that detects specific speech acts (consent and instruction) and generates corresponding robotic gestures. Built on locally deployed open-source models, the system utilizes a Large Language Model (LLM) with few-shot prompting for intent detection. We also introduce a novel dataset of clinical conversations annotated for speech acts and paired with gesture clips. Our identification module achieved 0.90 accuracy, 0.93 weighted precision, and a 0.91 weighted F1-Score. Our approach significantly improves computational efficiency and, in user studies, outperforms the speech-gesture generation baseline in human-likeness while maintaining comparable appropriateness.

cs.RO

Iteration and Co-design of a Physical Web Application for Outdoor Activities with Older Adults

Existing research and physical activity guidelines highlight the benefits of outdoor physical activities for ageing populations. There is potential for technology to facilitate outdoor activity through Physical Web infrastructure. We proposed that embedding Physical Web applications that are engaging and interactive in public open spaces as part of interactive wellness parks can encourage older adults to participate in physical activities outdoors and motivate rehabilitation. We have created an initial design prototype based on design requirements generated from a qualitative field study with 24 older adults to explore their perceptions, experiences, and routines of outdoor physical activities. In this paper, we present an initial prototype and findings from a co-design session with 12 older adults, eliciting their feedback on the design and their ideas for future design iterations.

cs.HC

Lost in Data: How Older Adults Perceive and Navigate Health Data Representations

As the ageing population grows, older adults increasingly rely on wearable devices to monitor chronic conditions. However, conventional health data representations (HDRs) often present accessibility challenges, particularly for critical health parameters like blood pressure and sleep data. This study explores how older adults interact with these representations, identifying key barriers such as semantic inconsistency and difficulties in understanding. While research has primarily focused on data collection, less attention has been given to how information is output and understood by end-users. To address this, an end-user evaluation was conducted with 16 older adults (65+) in a structured workshop, using think-aloud protocols and participatory design activities. The findings highlight the importance of affordance and familiarity in improving accessibility, emphasising the familiarity and potential of multimodal cues. This study bridges the gap between domain experts and end-users, providing a replicable methodological approach for designing intuitive, multisensory HDRs that better align with older adults' needs and abilities.

cs.HC