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Syst."],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>While Human Activity Recognition systems may benefit from Active Learning 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\u2019 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.<\/jats:p>","DOI":"10.1145\/3616017","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T11:21:29Z","timestamp":1692012089000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Generalisable Dialogue-based Approach for Active Learning of Activities of Daily Living"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4014-5736","authenticated-orcid":false,"given":"Ronnie","family":"Smith","sequence":"first","affiliation":[{"name":"Edinburgh Centre for Robotics, Scotland, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9013-2100","authenticated-orcid":false,"given":"Mauro","family":"Dragone","sequence":"additional","affiliation":[{"name":"Edinburgh Centre for Robotics, Scotland, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25167-2_12"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/193"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2638728.2641674"},{"key":"e_1_3_2_5_2","unstructured":"Tom Bocklisch Joey Faulkner Nick Pawlowski and Alan Nichol. 2017. 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