{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T05:38:22Z","timestamp":1733377102497,"version":"3.30.1"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,4]]},"DOI":"10.1145\/3636534.3698123","type":"proceedings-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T23:13:18Z","timestamp":1733353998000},"page":"2353-2358","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient and Personalized Mobile Health Event Prediction via Small Language Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4697-3840","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3806-1493","authenticated-orcid":false,"given":"Ting","family":"Dang","sequence":"additional","affiliation":[{"name":"University of Melbourne, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2804-6038","authenticated-orcid":false,"given":"Vassilis","family":"Kostakos","sequence":"additional","affiliation":[{"name":"University of Melbourne, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6047-4158","authenticated-orcid":false,"given":"Hong","family":"Jia","sequence":"additional","affiliation":[{"name":"University of Melbourne, Melbourne, AU"}]}],"member":"320","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Importance of monitoring your health","author":"Centers for Disease Control and Prevention.","year":"2021","unstructured":"Centers for Disease Control and Prevention. Importance of monitoring your health, 2021. Accessed: 2024-08-29."},{"issue":"9","key":"e_1_3_2_1_2_1","first-page":"e12861","article-title":"Wearable health technology and electronic health record integration: scoping review and future directions","volume":"21","author":"Dinh-Le Cecilia","year":"2019","unstructured":"Cecilia Dinh-Le, Rebecca Chuang, Sonia Chokshi, and Devin Mann. Wearable health technology and electronic health record integration: scoping review and future directions. Journal of Medical Internet Research, 21(9):e12861, 2019.","journal-title":"Journal of Medical Internet Research"},{"key":"e_1_3_2_1_3_1","volume-title":"Wearable technology for monitoring and preventing chronic diseases","author":"National Institutes of Health.","year":"2020","unstructured":"National Institutes of Health. Wearable technology for monitoring and preventing chronic diseases, 2020. Accessed: 2024-08-29."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3560533"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/PerCom59722.2024.10494467"},{"key":"e_1_3_2_1_6_1","first-page":"863","volume-title":"Machine Learning for Healthcare Conference","author":"Wu Yu","year":"2023","unstructured":"Yu Wu, Dimitris Spathis, Hong Jia, Ignacio Perez-Pozuelo, Tomas I Gonzales, Soren Brage, Nicholas Wareham, and Cecilia Mascolo. Udama: Unsupervised domain adaptation through multi-discriminator adversarial training with noisy labels improves cardio-fitness prediction. In Machine Learning for Healthcare Conference, pages 863--883. PMLR, 2023."},{"key":"e_1_3_2_1_7_1","volume-title":"Health-llm: Large language models for health prediction via wearable sensor data. arXiv","author":"Kim Yubin","year":"2023","unstructured":"Yubin Kim, Xuhai Xu, Daniel McDuff, Cynthia Breazeal, and Hae Won Park. Health-llm: Large language models for health prediction via wearable sensor data. arXiv, 2023."},{"key":"e_1_3_2_1_8_1","volume-title":"Enabling on-device llms personalization with smartphone sensing. arXiv preprint arXiv:2407.04418","author":"Zhang Shiquan","year":"2024","unstructured":"Shiquan Zhang, Ying Ma, Le Fang, Hong Jia, Simon D'Alfonso, and Vassilis Kostakos. Enabling on-device llms personalization with smartphone sensing. arXiv preprint arXiv:2407.04418, 2024."},{"key":"e_1_3_2_1_9_1","volume-title":"Phi-3-mini-4k-instruct: A lightweight, state-of-the-art open model","year":"2024","unstructured":"Microsoft. Phi-3-mini-4k-instruct: A lightweight, state-of-the-art open model, 2024. Accessed: 2024-09-01."},{"key":"e_1_3_2_1_10_1","volume-title":"A compact llama model with 1.1b parameters","year":"2024","unstructured":"TinyLlama. Tinyllama-1.1b-chat-v1.0: A compact llama model with 1.1b parameters, 2024. Accessed: 2024-09-01."},{"key":"e_1_3_2_1_11_1","volume-title":"Gemma 2: A lightweight, state-of-the-art open model family","year":"2024","unstructured":"Google. Gemma 2: A lightweight, state-of-the-art open model family, 2024. Accessed: 2024-09-01."},{"key":"e_1_3_2_1_12_1","volume-title":"A series of small language models","author":"TB.","year":"2024","unstructured":"HuggingFaceTB. Smollm-1.7b-instruct: A series of small language models, 2024. Accessed: 2024-09-01."},{"key":"e_1_3_2_1_13_1","volume-title":"A new series of large language models","year":"2024","unstructured":"Qwen. Qwen2-1.5b: A new series of large language models, 2024. Accessed: 2024-09-01."},{"key":"e_1_3_2_1_14_1","volume-title":"Gemini: The next generation of ai","year":"2024","unstructured":"DeepMind. Gemini: The next generation of ai, 2024. Accessed: 2024-09-03."},{"key":"e_1_3_2_1_15_1","volume-title":"Gpt-4 technical report. arXiv","author":"AI.","year":"2024","unstructured":"OpenAI. Gpt-4 technical report. arXiv, 2024."},{"key":"e_1_3_2_1_16_1","volume-title":"Exploring large-scale language models to evaluate eeg-based multimodal data for mental health. arXiv preprint arXiv:2408.07313","author":"Hu Yongquan","year":"2024","unstructured":"Yongquan Hu, Shuning Zhang, Ting Dang, Hong Jia, Flora D Salim, Wen Hu, and Aaron J Quigley. Exploring large-scale language models to evaluate eeg-based multimodal data for mental health. arXiv preprint arXiv:2408.07313, 2024."},{"issue":"1","key":"e_1_3_2_1_17_1","first-page":"1","article-title":"Leveraging large language models for mental health prediction via online text data. Proceedings of the ACM on Interactive, Mobile","volume":"8","author":"Xu Xuhai","year":"2024","unstructured":"Xuhai Xu, Bingsheng Yao, Yuanzhe Dong, Saadia Gabriel, Hong Yu, James Hendler, Marzyeh Ghassemi, Anind K Dey, and Dakuo Wang. Mental-llm: Leveraging large language models for mental health prediction via online text data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8(1):1--32, 2024.","journal-title":"Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_1_18_1","volume-title":"Leveraging llms to predict affective states via smartphone sensor features. arXiv preprint arXiv:2407.08240","author":"Zhang Tianyi","year":"2024","unstructured":"Tianyi Zhang, Songyan Teng, Hong Jia, and Simon D'Alfonso. Leveraging llms to predict affective states via smartphone sensor features. arXiv preprint arXiv:2407.08240, 2024."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06291-2"},{"key":"e_1_3_2_1_20_1","volume-title":"Dean Carignan, and Eric Horvitz. Capabilities of gpt-4 on medical challenge problems. arXiv preprint arXiv:2303.13375","author":"Nori Harsha","year":"2023","unstructured":"Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz. Capabilities of gpt-4 on medical challenge problems. arXiv preprint arXiv:2303.13375, 2023."},{"key":"e_1_3_2_1_21_1","volume-title":"Hugging face - the ai community building the future. https:\/\/huggingface.co\/, n.d. Accessed","author":"Face Hugging","year":"2024","unstructured":"Hugging Face. Hugging face - the ai community building the future. https:\/\/huggingface.co\/, n.d. Accessed: 02 September 2024."},{"key":"e_1_3_2_1_22_1","volume-title":"Yilun Zhou, Shelby Heinecke, Sachin Desai, Jason Wu, Ran Xu, Sarah Tan, et al. Mobileaibench: Benchmarking llms and lmms for on-device use cases.","author":"Murthy Rithesh","year":"2023","unstructured":"Rithesh Murthy, Liangwei Yang, Juntao Tan, Tulika Manoj Awalgaonkar, Yilun Zhou, Shelby Heinecke, Sachin Desai, Jason Wu, Ran Xu, Sarah Tan, et al. Mobileaibench: Benchmarking llms and lmms for on-device use cases. 2023."},{"key":"e_1_3_2_1_23_1","unstructured":"Ggerganov. Ggerganov\/llama.cpp: Llm inference in c\/c++."},{"key":"e_1_3_2_1_24_1","first-page":"231","volume-title":"Proceedings of the 11th ACM Multimedia Systems Conference, MMSys '20","author":"Thambawita Vajira","year":"2020","unstructured":"Vajira Thambawita, Steven Alexander Hicks, Hanna Borgli, H\u00e5kon Kvale Stensland, Debesh Jha, Martin Kristoffer Svensen, Svein-Arne Pettersen, Dag Johansen, H\u00e5vard Dagenborg Johansen, Susann Dahl Pettersen, Simon Nordvang, Sigurd Pedersen, Anders Gjerdrum, Tor-Morten Gr\u00f8nli, Per Morten Fredriksen, Ragnhild Eg, Kjeld Hansen, Siri Fagernes, Christine Claudi, Andreas Bi\u00f8rn-Hansen, Duc Tien Dang Nguyen, Tomas Kupka, Hugo Lewi Hammer, Ramesh Jain, Michael Alexander Riegler, and P\u00e5l Halvorsen. Pmdata: A sports logging dataset. In Proceedings of the 11th ACM Multimedia Systems Conference, MMSys '20, page 231--236, New York, NY, USA, 2020. Association for Computing Machinery."},{"key":"e_1_3_2_1_25_1","volume-title":"Hotpotqa: A dataset for diverse, explainable multi-hop question answering","author":"Yang Zhilin","year":"2018","unstructured":"Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering, 2018."}],"event":{"name":"ACM MobiCom '24: 30th Annual International Conference on Mobile Computing and Networking","location":"Washington D.C. DC USA","acronym":"ACM MobiCom '24","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing"]},"container-title":["Proceedings of the 30th Annual International Conference on Mobile Computing and Networking"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3636534.3698123","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T23:13:37Z","timestamp":1733354017000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3636534.3698123"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"references-count":25,"alternative-id":["10.1145\/3636534.3698123","10.1145\/3636534"],"URL":"https:\/\/doi.org\/10.1145\/3636534.3698123","relation":{},"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"2024-12-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}