{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T09:34:39Z","timestamp":1786095279167,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100002565","name":"Alzheimer's Drug Discovery Foundation","doi-asserted-by":"publisher","award":["RDADB-201906-2019049"],"award-info":[{"award-number":["RDADB-201906-2019049"]}],"id":[{"id":"10.13039\/100002565","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004853","name":"Chinese University of Hong Kong","doi-asserted-by":"publisher","award":["Direct Grant for Research 4055216"],"award-info":[{"award-number":["Direct Grant for Research 4055216"]}],"id":[{"id":"10.13039\/501100004853","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Grants Council, Hong Kong","award":["GRF 14201924"],"award-info":[{"award-number":["GRF 14201924"]}]},{"name":"Research Grants Council, Hong Kong","award":["CRF C4034-21G"],"award-info":[{"award-number":["CRF C4034-21G"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,6]]},"DOI":"10.1145\/3715014.3722062","type":"proceedings-article","created":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T23:39:01Z","timestamp":1746401941000},"page":"290-296","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["SHADE-AD: An LLM-Based Framework for Synthesizing Activity Data of Alzheimer\u2019s Patients"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2221-0143","authenticated-orcid":false,"given":"Heming","family":"Fu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, New York, USA"},{"name":"Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7206-6584","authenticated-orcid":false,"given":"Hongkai","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6362-2972","authenticated-orcid":false,"given":"Shan","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1772-7751","authenticated-orcid":false,"given":"Guoliang","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"2024 Alzheimer's Disease Facts and Figures. Annual Report","author":"Alzheimer's Association","year":"2024","unstructured":"Alzheimer's Association. 2024. 2024 Alzheimer's Disease Facts and Figures. Annual Report. Alzheimer's Association. https:\/\/alz.org\/media\/Documents\/Facts-And-Figures-2024-Executive-Summary.pdf"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML). 813--824","author":"Bertasius Gedas","year":"2021","unstructured":"Gedas Bertasius, Heng Wang, and Lorenzo Torresani. 2021. Is Space-Time Attention All You Need for Video Understanding?. In Proceedings of the International Conference on Machine Learning (ICML). 813--824."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci9020034"},{"key":"e_1_3_2_1_5_1","volume-title":"Tuanfeng Yang Wang, and Gordon Wetzstein.","author":"Cai Shengqu","year":"2023","unstructured":"Shengqu Cai, Duygu Ceylan, Matheus Gadelha, Chun-Hao Paul Huang, Tuanfeng Yang Wang, and Gordon Wetzstein. 2023. Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models. arXiv:2312.01409 [cs.CV] https:\/\/arxiv.org\/abs\/2312.01409"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Haoxin Chen Yong Zhang Xiaodong Cun Menghan Xia Xintao Wang Chao Weng and Ying Shan. 2024. VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models. arXiv:2401.09047 [cs.CV] https:\/\/arxiv.org\/abs\/2401.09047","DOI":"10.1109\/CVPR52733.2024.00698"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3625687.3625798"},{"key":"e_1_3_2_1_8_1","unstructured":"MMPose Contributors. 2020. OpenMMLab Pose Estimation Toolbox and Benchmark. https:\/\/github.com\/open-mmlab\/mmpose."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0316-z"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2023.101024"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN61024.2024.00029"},{"key":"e_1_3_2_1_12_1","first-page":"1","article-title":"Domain-Adversarial Training of Neural Networks","volume":"17","author":"Ganin Yaroslav","year":"2016","unstructured":"Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran\u00e7ois Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-Adversarial Training of Neural Networks. In Journal of Machine Learning Research (JMLR), Vol. 17. 1--35.","journal-title":"Journal of Machine Learning Research (JMLR)"},{"key":"e_1_3_2_1_13_1","volume-title":"Vivar: A Generative AR System for Intuitive Multi-Modal Sensor Data Presentation. arXiv:2412.13509 [cs.HC] https:\/\/arxiv.org\/abs\/2412.13509","author":"Guo Yunqi","year":"2024","unstructured":"Yunqi Guo, Kaiyuan Hou, Heming Fu, Hongkai Chen, Zhenyu Yan, Guoliang Xing, and Xiaofan Jiang. 2024. Vivar: A Generative AR System for Intuitive Multi-Modal Sensor Data Presentation. arXiv:2412.13509 [cs.HC] https:\/\/arxiv.org\/abs\/2412.13509"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3636534.3697456"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN61024.2024.00007"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0084-2"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3643832.3661844"},{"key":"e_1_3_2_1_18_1","unstructured":"Zhuoyan Li Hangxiao Zhu Zhuoran Lu and Ming Yin. 2023. Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations. arXiv:2310.07849 [cs.CL]"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3567772"},{"key":"e_1_3_2_1_20_1","volume-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)","volume":"42","author":"Liu Jie","year":"2019","unstructured":"Jie Liu, Amir Shahroudy, Mauricio Perez, Gang Wang, Ling-Yu Duan, and Alex C Kot. 2019. NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding. In IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 42. IEEE, 2684--2701."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Yang Liu Jiahuan Cao Chongyu Liu Kai Ding and Lianwen Jin. 2024. Datasets for Large Language Models: A Comprehensive Survey. arXiv:2402.18041 [cs.CL] https:\/\/arxiv.org\/abs\/2402.18041","DOI":"10.21203\/rs.3.rs-3996137\/v1"},{"key":"e_1_3_2_1_22_1","volume-title":"Timothy Kwok, and Guoliang Xing.","author":"Ouyang Xiaomin","year":"2024","unstructured":"Xiaomin Ouyang, Xian Shuai, Yang Li, Li Pan, Xifan Zhang, Heming Fu, Xinyan Wang, Shihua Cao, Jiang Xin, Hazel Mok, Zhenyu Yan, Doris Sau Fung Yu, Timothy Kwok, and Guoliang Xing. 2024. ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease. arXiv:2310.15301 [cs.LG]"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581791.3596844"},{"key":"e_1_3_2_1_24_1","unstructured":"Martin Prince Emiliano Albanese Ma\u00eblenn Guerchet and Matthew Prina. 2014. World Alzheimer Report 2014: Dementia and Risk Reduction."},{"key":"e_1_3_2_1_25_1","volume-title":"Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever.","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning Transferable Visual Models From Natural Language Supervision. arXiv:2103.00020 [cs.CV]"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00701-017-3385-8"},{"key":"e_1_3_2_1_28_1","first-page":"1","article-title":"A Survey on Deep Learning: Techniques, Applications, and Future Directions","volume":"6","author":"Shorten Connor","year":"2019","unstructured":"Connor Shorten and Taghi M Khoshgoftaar. 2019. A Survey on Deep Learning: Techniques, Applications, and Future Directions. Journal of Big Data 6, 1 (2019), 1--20.","journal-title":"Journal of Big Data"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1228--1237","author":"Wang Limin","year":"2018","unstructured":"Limin Wang, Yuanjun Xiong, Zhe Wang, and Others. 2018. Temporal Segment Networks for Action Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 1228--1237."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3569485"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3699765"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13183671"},{"key":"e_1_3_2_1_34_1","volume-title":"MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model. arXiv preprint arXiv:2208.15001","author":"Zhang Mingyuan","year":"2022","unstructured":"Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu. 2022. MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model. arXiv preprint arXiv:2208.15001 (2022)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.5582\/irdr.2023.01091"},{"key":"e_1_3_2_1_36_1","volume-title":"Shuheng Li, Dezhi Hong, Rajesh K Gupta, and Jingbo Shang.","author":"Zhang Xiyuan","year":"2024","unstructured":"Xiyuan Zhang, Diyan Teng, Ranak Roy Chowdhury, Shuheng Li, Dezhi Hong, Rajesh K Gupta, and Jingbo Shang. 2024. UniMTS: Unified Pre-training for Motion Time Series. arXiv preprint arXiv:2410.19818 (2024)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2024.1358998"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2023.1284805"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","unstructured":"Tong Zhou Xuhang Chen Yanyan Shen Martin Nieuwoudt Chi-Man Pun and Shu-Qiang Wang. 2023. Generative AI Enables EEG Data Augmentation for Alzheimer's Disease Detection Via Diffusion Model. 1--6. 10.1109\/ISPCE-ASIA60405.2023.10365931","DOI":"10.1109\/ISPCE-ASIA60405.2023.10365931"}],"event":{"name":"SenSys '25: 23rd ACM Conference on Embedded Networked Sensor Systems","location":"UC Irvine Student Center. Irvine CA USA","acronym":"SenSys '25","sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture","SIGMETRICS ACM Special Interest Group on Measurement and Evaluation","SIGOPS ACM Special Interest Group on Operating Systems","SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGBED ACM Special Interest Group on Embedded Systems"]},"container-title":["Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715014.3722062","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:56:51Z","timestamp":1750298211000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715014.3722062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,6]]},"references-count":39,"alternative-id":["10.1145\/3715014.3722062","10.1145\/3715014"],"URL":"https:\/\/doi.org\/10.1145\/3715014.3722062","relation":{},"subject":[],"published":{"date-parts":[[2025,5,6]]},"assertion":[{"value":"2025-05-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}