Group interactions are a natural part of our daily life, and as robots become more integrated into society, they must be able to socially interact with multiple people at the same time. However, group human-robot interaction (HRI) poses unique computational challenges often overlooked in the current HRI literature. We conducted a scoping review including 44 group HRI papers from the last decade (2015–2024). From these papers, we extracted variables related to perception and behaviour generation challenges, as well as factors related to the environment, group, and robot capabilities that influence these challenges. Our findings show that key computational challenges in perception included detection of groups, engagement, and conversation information, while challenges in behaviour generation involved developing approaching and conversational behaviours. We also identified research gaps, such as improving detection of subgroups and interpersonal relationships, and recommended future work in group HRI to help researchers address these computational challenges.

Whisper fails to correctly transcribe dementia speech because persons with dementia (PwDs) often exhibit irregular speech patterns and disfluencies such as pauses, repetitions, and fragmented sentences. It was trained on standard speech and may have had little or no exposure to dementia-affected speech. However, correct transcription is vital for dementia speech for cost-effective diagnosis and the development of assistive technology. In this work, we fine-tune Whisper with the open-source dementia speech dataset (DementiaBank) and our in-house dataset to improve its word error rate (WER). The fine-tuning also includes filler words to ascertain the filler inclusion rate (FIR) and F1 score. The fine-tuned models significantly outperformed the off-the-shelf models. The medium-sized model achieved a WER of 0.24, outperforming previous work. Similarly, there was a notable generalisability to unseen data and speech patterns.

Individual cognitive stimulation therapy (iCST) is a non-pharmacological intervention for improving the cognition and quality of life of persons with dementia (PwDs); however, its effectiveness is limited by low adherence to delivery by their family members. In this work, we present the user-centered design and evaluation of a novel socially assistive robotic system to provide iCST therapy to PwDs in their homes for long-term use. We consulted with 16 dementia caregivers and professionals. Through these consultations, we gathered design guidelines and developed the prototype. The prototype was validated by testing it with three dementia professionals and five PwDs. The evaluation revealed PwDs enjoyed using the system and are willing to adopt its use over the long term. One shortcoming was the system’s speech-to-text capabilities, where it frequently failed to understand the PwDs.