{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:50Z","timestamp":1761176150602,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Human Activity Recognition (HAR) is pivotal for behavior monitoring in public healthcare, supporting tasks like medical rehabilitation and targeted wellness campaigns. Initially utilizing hand-crafted features and traditional machine learning models such as support vector machines and decision trees, HAR has since evolved to include deep learning techniques, especially convolutional neural networks and Transformers, which excel at modeling temporal and task-specific information from sensor data. Despite these advancements, challenges related to high task dependency and data scarcity persist. To address these issues, there have been efforts to harness the vast pre-trained knowledge of Large Language Models (LLMs) for HAR. Yet, LLMs often fail to fully capture the temporal dynamics inherent in sensor data. We introduce HyMv, a novel hybrid approach that combines an auxiliary HAR model with soft-prompt tuning of LLMs. This approach leverages the auxiliary model\u2019s proficiency in processing sensor data to guide the parameter optimization of LLMs during prompt tuning. Importantly, the auxiliary HAR model is only active during training, augmenting the LLM\u2019s parameters without adding computational overhead during testing. We evaluate the performance of HyMv for various HAR tasks on diverse datasets and demonstrate its adaptability to different sensor modalities, further showcasing its broad applicability.<\/jats:p>","DOI":"10.3233\/faia250926","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:46:12Z","timestamp":1761126372000},"source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Multi-View Approach Towards Augmenting Large Language Models for Human Activity Recognition"],"prefix":"10.3233","author":[{"given":"Suman","family":"Bhoi","sequence":"first","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Varsha","family":"Suresh","sequence":"additional","affiliation":[{"name":"Saarland University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wynne","family":"Hsu","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mong Li","family":"Lee","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250926","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:46:12Z","timestamp":1761126372000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250926"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250926","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}