PrISM-Q&A: Step-Aware Voice Assistant on a Smartwatch Enabled by Multimodal Procedure Tracking and Large Language Models
Riku Arakawa, Jill Fain Lehman, Mayank Goel
Abstract
Voice assistants capable of answering user queries during various physical tasks have shown promise in guiding users through complex procedures. However, users often find it challenging to articulate their queries precisely, especially when unfamiliar with the specific terminologies required for machine-oriented tasks. We introduce PrISM-Q&A, a novel question-answering (Q&A) interaction termed step-aware Q&A, which enhances the functionality of voice assistants on smartwatches by incorporating Human Activity Recognition (HAR) and providing the system with user context. It continuously monitors user behavior during procedural tasks via audio and motion sensors on the watch and estimates which step the user is performing. When a question is posed, this contextual information is supplied to Large Language Models (LLMs) as part of the context used to generate a response, even in the case of inherently vague questions like "What should I do next with this?" Our studies confirmed that users preferred the convenience of our approach compared to existing voice assistants. Our real-time assistant represents the first Q&A system that provides contextually situated support during tasks without camera use, paving the way for the ubiquitous, intelligent assistant.