research-article

Open access

ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease

Authors:

Xiaomin Ouyang,

Doris Sau Fung Yu,

Guoliang XingAuthors Info & Claims

ACM MobiCom '24: Proceedings of the 30th Annual International Conference on Mobile Computing and Networking

Pages 404 - 419

https://doi.org/10.1145/3636534.3649370

Published: 29 May 2024 Publication History

Abstract

Alzheimer's Disease (AD) and related dementia are a growing global health challenge due to the aging population. In this paper, we present ADMarker, the first end-to-end system that integrates multi-modal sensors and new federated learning algorithms for detecting multidimensional AD digital biomarkers in natural living environments. ADMarker features a novel three-stage multi-modal federated learning architecture that can accurately detect digital biomarkers in a privacy-preserving manner. Our approach collectively addresses several major real-world challenges, such as limited data labels, data heterogeneity, and limited computing resources. We built a compact multi-modality hardware system and deployed it in a four-week clinical trial involving 91 elderly participants. The results indicate that ADMarker can accurately detect a comprehensive set of digital biomarkers with up to 93.8% accuracy and identify early AD with an average of 88.9% accuracy. ADMarker offers a new platform that can allow AD clinicians to characterize and track the complex correlation between multidimensional interpretable digital biomarkers, demographic factors of patients, and AD diagnosis in a longitudinal manner.

References

[1]

2021. World Alzheimer Report 2021: Journey through the diagnosis of dementia. https://www.alzint.org/resource/world-alzheimer-report-2021/. (2021).

[2]

2022. ALZHEIMER'S DIGITAL BIOMARKERS. https://www.alzdiscovery.org/research-and-grants/funding-opportunities/diagnostics-accelerator-digital-biomarkers-program. (2022).

[3]

2022. NVDIA Xavier NX. https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-xavier-nx/. (2022).

[4]

2022. Opencv MJPG. https://docs.opencv.org/4.x/dd/d43/tutorial_py_video_display.html. (2022).

[5]

2022. Systemd. https://en.wikipedia.org/wiki/Systemd. (2022).

[6]

2022. Yolov5. (2022). https://pytorch.org/hub/ultralytics_yolov5/.

[7]

2023. jetson-stats. (2023). https://github.com/rbonghi/jetson_stats.

[8]

2023. speedtest-cli. (2023). https://pypi.org/project/speedtest-cli/.

[9]

Ane Alberdi, Alyssa Weakley, Maureen Schmitter-Edgecombe, Diane J Cook, Asier Aztiria, Adrian Basarab, and Maitane Barrenechea. 2018. Smart home-based prediction of multidomain symptoms related to Alzheimer's disease. IEEE journal of biomedical and health informatics 22, 6 (2018), 1720--1731.

[10]

Manuela Altieri, Federica Garramone, and Gabriella Santangelo. 2021. Functional autonomy in dementia of the Alzheimer's type, mild cognitive impairment, and healthy aging: a meta-analysis. Neurological Sciences 42 (2021), 1773--1783.

[11]

Asuna Arai, Amartuvshin Khaltar, Takashi Ozaki, and Yuriko Katsumata. 2021. Influence of social interaction on behavioral and psychological symptoms of dementia over 1 year among long-term care facility residents. Geriatric nursing 42, 2 (2021), 509--516.

[12]

Sayeh Bayat, Ganesh M Babulal, Suzanne E Schindler, Anne M Fagan, John C Morris, Alex Mihailidis, and Catherine M Roe. 2021. GPS driving: a digital biomarker for preclinical Alzheimer disease. Alzheimer's Research & Therapy 13, 1 (2021), 1--9.

[13]

Lisa F Berkman, Thomas Glass, Ian Brissette, and Teresa E Seeman. 2000. From social integration to health: Durkheim in the new millennium. Social science & medicine 51, 6 (2000), 843--857.

[14]

Morton B Brown and Alan B Forsythe. 1974. Robust tests for the equality of variances. Journal of the American statistical association 69, 346 (1974), 364--367.

[15]

Xiangmao Chang, Cheng Peng, Guoliang Xing, Tian Hao, and Gang Zhou. 2020. ISleep: A Smartphone System for Unobtrusive Sleep Quality Monitoring. ACM Trans. Sen. Netw. 16, 3, Article 27 (jul 2020), 32 pages.

Digital Library

[16]

Richard Chen, Filip Jankovic, Nikki Marinsek, Luca Foschini, Lampros Kourtis, Alessio Signorini, Melissa Pugh, Jie Shen, Roy Yaari, Vera Maljkovic, et al. 2019. Developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 2145--2155.

Digital Library

[17]

Prafulla Nath Dawadi, Diane Joyce Cook, and Maureen Schmitter-Edgecombe. 2015. Automated cognitive health assessment from smart home-based behavior data. IEEE journal of biomedical and health informatics 20, 4 (2015), 1188--1194.

[18]

SF Doris, Timothy Kwok, Jacky Choy, and David J Kavanagh. 2016. Measuring the expressed emotion in Chinese family caregivers of persons with dementia: Validation of a Chinese version of the Family Attitude Scale. International journal of nursing studies 55 (2016), 50--59.

[19]

Kirsten M Fiest, Nathalie Jette, Jodie I Roberts, Colleen J Maxwell, Eric E Smith, Sandra E Black, Laura Blaikie, Adrienne Cohen, Lundy Day, Jayna Holroyd-Leduc, et al. 2016. The prevalence and incidence of dementia: a systematic review and meta-analysis. Canadian Journal of Neurological Sciences 43, S1 (2016), S3--S50.

[20]

Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. 2021. Knowledge distillation: A survey. International Journal of Computer Vision 129 (2021), 1789--1819.

Digital Library

[21]

Guoliang Xing. 2022. Machine Learning Technologies for Advancing Digital Biomarkers for Alzheimer's Disease, Alzheimer's Drug Discovery Foundation. https://www.alzdiscovery.org/research-and-grants/portfolio-details/21130887. (2022).

[22]

Margrét Gústafsdóttir. 2015. Is watching television a realistic leisure option for people with dementia. Dementia and Geriatric Cognitive Disorders Extra 5, 1 (2015), 116--122.

[23]

Robert Istepanian, Swamy Laxminarayan, and Constantinos S Pattichis. 2007. M-health: Emerging mobile health systems. Springer Science & Business Media.

[24]

Chadawan Ittichaichareon, Siwat Suksri, and Thaweesak Yingthawornsuk. 2012. Speech recognition using MFCC. In International conference on computer graphics, simulation and modeling, Vol. 9.

[25]

Jeya Vikranth Jeyakumar, Ankur Sarker, Luis Antonio Garcia, and Mani Srivastava. 2023. X-CHAR: A Concept-based Explainable Complex Human Activity Recognition Model. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, 1 (2023), 1--28.

Digital Library

[26]

Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shuochao Yao, Yaqing Wang, Ye Yuan, Hongfei Xue, Chen Song, Xin Ma, Dimitrios Koutsonikolas, et al. 2018. Towards environment independent device free human activity recognition. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. 289--304.

Digital Library

[27]

Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik. 2016. Federated optimization: Distributed machine learning for on-device intelligence. (2016). arXiv:1610.02527

[28]

Lampros C Kourtis, Oliver B Regele, Justin M Wright, and Graham B Jones. 2019. Digital biomarkers for Alzheimer's disease: the mobile/wearable devices opportunity. NPJ digital medicine 2, 1 (2019), 1--9.

[29]

Huining Li, Huan Chen, Chenhan Xu, Zhengxiong Li, Hanbin Zhang, Xiaoye Qian, Dongmei Li, Ming-chun Huang, and Wenyao Xu. 2023. NeuralGait: Assessing Brain Health Using Your Smartphone. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 4, Article 169 (jan 2023), 28 pages.

Digital Library

[30]

Jia Li, Yu Rong, Helen Meng, Zhihui Lu, Timothy Kwok, and Hong Cheng. 2018. TATC: predicting Alzheimer's disease with actigraphy data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 509--518.

Digital Library

[31]

Xu-Ying Liu, Jianxin Wu, and Zhi-Hua Zhou. 2008. Exploratory under-sampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, 2 (2008), 539--550.

[32]

Gill Livingston, Jonathan Huntley, Andrew Sommerlad, David Ames, Clive Ballard, Sube Banerjee, Carol Brayne, Alistair Burns, Jiska Cohen-Mansfield, Claudia Cooper, et al. 2020. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet 396, 10248 (2020), 413--446.

[33]

Gill Livingston, Andrew Sommerlad, Vasiliki Orgeta, Sergi G Costafreda, Jonathan Huntley, David Ames, Clive Ballard, Sube Banerjee, Alistair Burns, Jiska Cohen-Mansfield, et al. 2017. Dementia prevention, intervention, and care. The Lancet 390, 10113 (2017), 2673--2734.

[34]

Hong Lu, Denise Frauendorfer, Mashfiqui Rabbi, Marianne Schmid Mast, Gokul T. Chittaranjan, Andrew T. Campbell, Daniel Gatica-Perez, and Tanzeem Choudhury. 2012. StressSense: Detecting Stress in Unconstrained Acoustic Environments Using Smartphones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp '12). Association for Computing Machinery, New York, NY, USA, 351--360.

Digital Library

[35]

Saturnino Luz, Fasih Haider, Sofia de la Fuente, Davida Fromm, and Brian MacWhinney. 2020. Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge. In Interspeech 2020. ISCA, 2172--2176.

[36]

P Marique, F Peeters, K Herholz, D Perani, V Holthoff, E Kalbe, D Anchisi, S Adam, F Collette, G Garraux, et al. 2005. Cerebral metabolic correlates of four dementia scales in Alzheimer's disease. Journal of neurology 252, 3 (2005), 283--290.

[37]

Gad A Marshall, Lynn A Fairbanks, Sibel Tekin, Harry V Vinters, and Jeffrey L Cummings. 2006. Neuropathologic correlates of activities of daily living in Alzheimer disease. Alzheimer Disease & Associated Disorders 20, 1 (2006), 56--59.

[38]

Gad A Marshall, Dorene M Rentz, Meghan T Frey, Joseph J Locascio, Keith A Johnson, Reisa A Sperling, and Alzheimer's Disease Neuroimaging Initiative. 2011. Executive function and instrumental activities of daily living in mild cognitive impairment and Alzheimer's disease. Alzheimer's & Dementia 7, 3 (2011), 300--308.

[39]

H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al. 2016. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016).

[40]

Patrick McNamara, Loraine K Obler, Rhoda Au, Raymon Durso, and Martin L Albert. 1992. Speech monitoring skills in Alzheimer's disease, Parkinson's disease, and normal aging. Brain and language 42, 1 (1992), 38--51.

[41]

D Miu, SL Szeto, YF Mak, et al. 2008. A randomised controlled trial on the effect of exercise on physical, cognitive and affective function in dementia subjects. Asian J Gerontol Geriatr 3, 1 (2008), 8--16.

[42]

Manuel Montero-Odasso, Joe Verghese, Olivier Beauchet, and Jeffrey M Hausdorff. 2012. Gait and cognition: a complementary approach to understanding brain function and the risk of falling. Journal of the American Geriatrics Society 60, 11 (2012), 2127--2136.

[43]

Ziad S Nasreddine, Natalie A Phillips, Valérie Bédirian, Simon Charbonneau, Victor Whitehead, Isabelle Collin, Jeffrey L Cummings, and Howard Chertkow. 2005. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society 53, 4 (2005), 695--699.

[44]

Shamila Nasreen, Julian Hough, Matthew Purver, et al. 2021. Detecting Alzheimer's Disease using Interactional and Acoustic features from Spontaneous Speech. Interspeech.

[45]

Xiaomin Ouyang, Xian Shuai, Jiayu Zhou, Ivy Wang Shi, Zhiyuan Xie, Guoliang Xing, and Jianwei Huang. 2022. Cosmo: contrastive fusion learning with small data for multimodal human activity recognition. In Proceedings of the 28th Annual International Conference on Mobile Computing And Networking. 324--337.

Digital Library

[46]

Xiaomin Ouyang, Zhiyuan Xie, Heming Fu, Sitong Cheng, Li Pan, Neiwen Ling, Guoliang Xing, Jiayu Zhou, and Jianwei Huang. 2023. Harmony: Heterogeneous Multi-Modal Federated Learning through Disentangled Model Training. In Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services. 530--543.

Digital Library

[47]

Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Jianwei Huang, and Guoliang Xing. 2021. Clusterfl: a similarity-aware federated learning system for human activity recognition. In Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services. 54--66.

Digital Library

[48]

Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Guoliang Xing, and Jianwei Huang. 2022. ClusterFL: A Clustering-based Federated Learning System for Human Activity Recognition. ACM Transactions on Sensor Networks 19, 1 (2022), 1--32.

Digital Library

[49]

Marian B Patterson, James L Mack, Marcia M Neundorfer, Richard J Martin, Kathleen A Smyth, and Peter J Whitehouse. 1992. Assessment of functional ability in Alzheimer disease: a review and a preliminary report on the Cleveland Scale for Activities of Daily Living. Alzheimer Disease & Associated Disorders 6, 3 (1992), 145--163.

[50]

Catherine Reed, Mark Belger, Bruno Vellas, Jeffrey Scott Andrews, Josep M Argimon, Giuseppe Bruno, Richard Dodel, Roy W Jones, Anders Wimo, and Josep Maria Haro. 2016. Identifying factors of activities of daily living important for cost and caregiver outcomes in Alzheimer's disease. International psychogeriatrics 28, 2 (2016), 247--259.

[51]

Remi M Sakia. 1992. The Box-Cox transformation technique: a review. Journal of the Royal Statistical Society Series D: The Statistician 41, 2 (1992), 169--178.

[52]

Xian Shuai, Yulin Shen, Siyang Jiang, Zhihe Zhao, Zhenyu Yan, and Guoliang Xing. 2022. BalanceFL: Addressing class imbalance in long-tail federated learning. In 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 271--284.

[53]

Xian Shuai, Yulin Shen, Yi Tang, Shuyao Shi, Luping Ji, and Guoliang Xing. 2021. millieye: A lightweight mmwave radar and camera fusion system for robust object detection. In Proceedings of the International Conference on Internet-of-Things Design and Implementation. 145--157.

Digital Library

[54]

Bruno MC Silva, Joel JPC Rodrigues, Isabel de la Torre Díez, Miguel López-Coronado, and Kashif Saleem. 2015. Mobile-health: A review of current state in 2015. Journal of biomedical informatics 56 (2015), 265--272.

Digital Library

[55]

Lars St, Svante Wold, et al. 1989. Analysis of variance (ANOVA). Chemometrics and intelligent laboratory systems 6, 4 (1989), 259--272.

[56]

Allan Stisen, Henrik Blunck, Sourav Bhattacharya, Thor Siiger Prentow, Mikkel Baun Kjærgaard, Anind Dey, Tobias Sonne, and Mads Møller Jensen. 2015. Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition. In Proceedings of the 13th ACM conference on embedded networked sensor systems. 127--140.

Digital Library

[57]

Sibel Tekin, Lynn A Fairbanks, Susan O'Connor, Susan Rosenberg, and Jeffrey L Cummings. 2001. Activities of daily living in Alzheimer's disease: neuropsychiatric, cognitive, and medical illness influences. The American Journal of Geriatric Psychiatry 9, 1 (2001), 81--86.

[58]

Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. 2020. What makes for good views for contrastive learning. arXiv preprint arXiv:2005.10243 (2020).

[59]

Linlin Tu, Xiaomin Ouyang, Jiayu Zhou, Yuze He, and Guoliang Xing. 2021. Feddl: Federated learning via dynamic layer sharing for human activity recognition. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems. 15--28.

Digital Library

[60]

Yancheng Wang, Yang Xiao, Fu Xiong, Wenxiang Jiang, Zhiguo Cao, Joey Tianyi Zhou, and Junsong Yuan. 2020. 3dv: 3d dynamic voxel for action recognition in depth video. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 511--520.

[61]

Lu Xia and JK Aggarwal. 2013. Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2834--2841.

Digital Library

[62]

Huatao Xu, Pengfei Zhou, Rui Tan, Mo Li, and Guobin Shen. 2021. LIMU-BERT: Unleashing the Potential of Unlabeled Data for IMU Sensing Applications. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems. 220--233.

Digital Library

[63]

Yasunori Yamada, Kaoru Shinkawa, Masatomo Kobayashi, Vittorio Caggiano, Miyuki Nemoto, Kiyotaka Nemoto, and Tetsuaki Arai. 2021. Combining multimodal behavioral data of gait, speech, and drawing for classification of Alzheimer's disease and mild cognitive impairment. Journal of Alzheimer's Disease 84, 1 (2021), 315--327.

[64]

Hanbin Zhang, Gabriel Guo, Chen Song, Chenhan Xu, Kevin Cheung, Jasleen Alexis, Huining Li, Dongmei Li, Kun Wang, and Wenyao Xu. 2020. PDLens: Smartphone Knows Drug Effectiveness among Parkinson's via Daily-Life Activity Fusion. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (Mobi-Com '20). Association for Computing Machinery, New York, NY, USA, Article 12, 14 pages.

Digital Library

[65]

Yuchen Zhao, Payam Barnaghi, and Hamed Haddadi. 2022. Multimodal Federated Learning on IoT Data. In 2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI). IEEE, 43--54.

[66]

Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018).

Cited By

Fu HChen HXing G(2024)Demo Abstract: AD-CLIP: Privacy-Preserving, Low-Cost Synthetic Human Action Dataset for Alzheimer’s Patients via CLIP-based Models2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00029(257-258)Online publication date: 13-May-2024
https://doi.org/10.1109/IPSN61024.2024.00029
Wu HLiu KJiang SZhao ZYan ZXing G(2024)Demo Abstract: CaringFM: An Interactive In-home Healthcare System Empowered by Large Foundation Models2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00028(255-256)Online publication date: 13-May-2024
https://doi.org/10.1109/IPSN61024.2024.00028

Index Terms

ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing

Recommendations

Design and Deployment of Multi-Modal Federated Learning Systems for Alzheimer's Disease Monitoring
MobiSys '23: Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services

Alzheimer's Disease (AD) and related dementia are a growing global health challenge due to the aging population. The prominence of mobile devices and recent breakthroughs in machine learning have enabled an emerging class of new AI-powered health ...
Demo: EmoMarker: A Privacy-Preserving, Multi-Modal Sensing System for Dyadic Digital Biomarkers of Expressed Emotions for Patients with Dementia
MOBISYS '24: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services

Alzheimer's disease and related dementia has emerged as a global health challenge due to aging population. Expressed Emotion (EE) is a widely-used medical measure of family emotional environment of patients with caregivers. We present EmoMarker, a multi-...
Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease
Abstract
Alzheimer’s disease (AD) is one of the most difficult to cure diseases. Alzheimer’s disease seriously affects the normal lives of the elderly and their families. The mild cognitive impairment (MCI) is a transitional state between the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ACM MobiCom '24: Proceedings of the 30th Annual International Conference on Mobile Computing and Networking

May 2024

733 pages

ISBN:9798400704895

DOI:10.1145/3636534

Chair:
Weisong Shi,
Program Chairs:
Deepak Ganesan,
Nicholas Lane

Copyright © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2024

Check for updates

Badges

Author Tags

Qualifiers

Research-article

Funding Sources

Research Grants Council of Hong Kong
Alzheimer's Drug Discovery Foundation

Conference

ACM MobiCom '24

Sponsor:

SIGMOBILE

ACM MobiCom '24: 30th Annual International Conference on Mobile Computing and Networking

November 18 - 22, 2024

DC, Washington D.C., USA

Acceptance Rates

Overall Acceptance Rate 440 of 2,972 submissions, 15%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
1,205
Total Downloads

Downloads (Last 12 months)1,205
Downloads (Last 6 weeks)391

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Fu HChen HXing G(2024)Demo Abstract: AD-CLIP: Privacy-Preserving, Low-Cost Synthetic Human Action Dataset for Alzheimer’s Patients via CLIP-based Models2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00029(257-258)Online publication date: 13-May-2024
https://doi.org/10.1109/IPSN61024.2024.00029
Wu HLiu KJiang SZhao ZYan ZXing G(2024)Demo Abstract: CaringFM: An Interactive In-home Healthcare System Empowered by Large Foundation Models2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00028(255-256)Online publication date: 13-May-2024
https://doi.org/10.1109/IPSN61024.2024.00028

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents