A Generalisable Dialogue-Based Approach for Active Learning of Activities of Daily Living

While Human Activity Recognition (HAR) systems may benefit from Active Learning (AL) by allowing users to self-annotate their Activities of Daily Living (ADLs), many proposed methods for collecting such annotations are for short-term data collection campaigns for specific datasets. We present a reusable dialogue-based approach to user interaction for active learning in activity recognition systems, which utilises semantic similarity measures and a dataset of natural language descriptions of common activities (which we make publicly available). Our approach involves system-initiated dialogue, including follow-up questions to reduce ambiguity in user responses where appropriate. We apply this approach to two active learning scenarios: (i) using an existing CASAS dataset, demonstrating long-term usage; and (ii) using an online activity recognition system, which tackles the issue of online segmentation and labelling. We demonstrate our work in context, in which a natural language interface provides knowledge that can help interpret other multi-modal sensor data. We provide results highlighting the potential of our dialogue- and semantic similarity-based approach. We evaluate our work: (i) quantitatively, as an efficient way to seek users’ input for active learning of ADLs; and (ii) qualitatively, through a user study in which users were asked to compare our approach and an established method. Results show the potential of our approach as a hands-free interface for annotation of sensor data as part of an active learning system. We provide insights into the challenges of active learning for activity recognition under real-world conditions, and identify potential ways to address them.