research-article

Open access

Influenza-like symptom recognition using mobile sensing and graph neural networks

Authors:

Debajyoti Datta,

Shashwat Kumar,

Laura E. Barnes,

Mehdi BoukhechbaAuthors Info & Claims

CHIL '21: Proceedings of the Conference on Health, Inference, and Learning

Pages 291 - 300

https://doi.org/10.1145/3450439.3451880

Published: 08 April 2021 Publication History

Abstract

Early detection of influenza-like symptoms can prevent widespread flu viruses and enable timely treatments, particularly in the post-pandemic era. Mobile sensing leverages an increasingly diverse set of embedded sensors to capture fine-grained information of human behaviors and ambient contexts, and can serve as a promising solution for influenza-like symptom recognition. Traditionally, handcrafted and high level features of mobile sensing data are extracted by manual feature engineering and convolutional/recurrent neural network respectively. In this work, we apply graph representation to encode the dynamics of state transitions and internal dependencies in human behaviors, leverage graph embeddings to automatically extract the topological and spatial features from graph inputs, and propose an end-to-end graph neural network (GNN) model with multi-channel mobile sensing input for influenzalike symptom recognition based on people's daily mobility, social interactions, and physical activities. Using data generated from 448 participants, we show that GNN with GraphSAGE convolutional layers significantly outperforms baseline models with handcrafted features. Furthermore, we use GNN interpretability method to generate insights (e.g., important nodes and graph structures) about the importance of mobile sensing for recognizing Influenza-like symptoms. To the best of our knowledge, this is the first work that applies graph representation and graph neural network on mobile sensing data for graph-based human behavior modeling and health symptoms prediction.

References

[1]

Forsad Al Hossain, Andrew A Lover, George A Corey, Nicholas G Reich, and Tauhidur Rahman. 2020. FluSense: a contactless syndromic surveillance platform for influenza-like illness in hospital waiting areas. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1--28.

Digital Library

[2]

Mawulolo K Ameko, Lihua Cai, Mehdi Boukhechba, Alexander Daros, Philip I Chow, Bethany A Teachman, Matthew S Gerber, and Laura E Barnes. 2018. Cluster-based approach to improve affect recognition from passively sensed data. In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 434--437.

[3]

Apple. 2020. CMMotionActivity. https://developer.apple.com/documentation/coremotion/cmmotionactivity

[4]

Gianni Barlacchi, Christos Perentis, Abhinav Mehrotra, Mirco Musolesi, and Bruno Lepri. 2017. Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors. EPJ Data Science 6, 1 (2017), 27.

[5]

David Blumenthal, Elizabeth J Fowler, Melinda Abrams, and Sara R Collins. 2020. Covid-19---Implications for the Health Care System.

[6]

Mehdi Boukhechba, Anna N Baglione, and Laura E Barnes. 2020. Leveraging Mobile Sensing and Machine Learning for Personalized Mental Health Care. Ergonomics in Design (2020), 1064804620920494.

[7]

Mehdi Boukhechba and Laura E Barnes. 2020. Swear: Sensing using wearables. Generalized human crowdsensing on smartwatches. In 2019 IEEE 11th International Conference on Applied Human Factors and Ergonomics. IEEE.

[8]

Mehdi Boukhechba, Lihua Cai, Philip I Chow, Karl Fua, Matthew S Gerber, Bethany A Teachman, and Laura E Barnes. 2018. Contextual analysis to understand compliance with smartphone-based ecological momentary assessment. In Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare. 232--238.

Digital Library

[9]

Mehdi Boukhechba, Lihua Cai, Congyu Wu, and Laura E Barnes. 2019. ActiPPG: using deep neural networks for activity recognition from wrist-worn photoplethysmography (PPG) sensors. Smart Health 14 (2019), 100082.

[10]

Mehdi Boukhechba, Alexander R Daros, Karl Fua, Philip I Chow, Bethany A Teachman, and Laura E Barnes. 2018. DemonicSalmon: Monitoring mental health and social interactions of college students using smartphones. Smart Health 9 (2018), 192--203.

[11]

Mehdi Boukhechba, Yu Huang, Philip Chow, Karl Fua, Bethany A Teachman, and Laura E Barnes. 2017. Monitoring social anxiety from mobility and communication patterns. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. 749--753.

Digital Library

[12]

Lihua Cai, Mehdi Boukhechba, Matthew S Gerber, Laura E Barnes, Shayna L Showalter, Wendy F Cohn, and Philip I Chow. 2020. An integrated framework for using mobile sensing to understand response to mobile interventions among breast cancer patients. Smart Health 15 (2020), 100086.

[13]

Lihua Cai, Mehdi Boukhechba, Congyu Wu, Philip I Chow, Bethany A Teachman, Laura E Barnes, and Matthew S Gerber. 2018. State affect recognition using smartphone sensing data. In Proceedings of the 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies. 120--125.

Digital Library

[14]

Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, and Pietro Liò. 2018. Towards sparse hierarchical graph classifiers. arXiv preprint arXiv:1811.01287 (2018).

[15]

Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785--794.

Digital Library

[16]

Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli. 2019. A fair comparison of graph neural networks for graph classification. arXiv preprint arXiv:1912.09893 (2019).

[17]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, Vol. 96. 226--231.

Digital Library

[18]

Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph Neural Networks for Social Recommendation. In The World Wide Web Conference (San Francisco, CA, USA) (WWW '19). Association for Computing Machinery, New York, NY, USA, 417--426.

Digital Library

[19]

Matt W Gardner and SR Dorling. 1998. Artificial neural networks (the multilayer perceptron)---a review of applications in the atmospheric sciences. Atmospheric environment 32, 14-15 (1998), 2627--2636.

[20]

Michele Girolami, Fabio Mavilia, and Franca Delmastro. 2020. Sensing social interactions through ble beacons and commercial mobile devices. Pervasive and Mobile Computing 67 (2020), 101198.

[21]

Jiaqi Gong, Yu Huang, Philip I Chow, Karl Fua, Matthew S Gerber, Bethany A Teachman, and Laura E Barnes. 2019. Understanding behavioral dynamics of social anxiety among college students through smartphone sensors. Information Fusion 49 (2019), 57--68.

Digital Library

[22]

Google. 2020. Google Activity Recognition API. https://developers.google.com/location-context/activity-recognition

[23]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 855--864.

Digital Library

[24]

Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in neural information processing systems. 1024--1034.

Digital Library

[25]

William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017).

[26]

Hongping Hu, Haiyan Wang, Feng Wang, Daniel Langley, Adrian Avram, and Maoxing Liu. 2018. Prediction of influenza-like illness based on the improved artificial tree algorithm and artificial neural network. Scientific reports 8, 1 (2018), 1--8.

[27]

Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).

[28]

Nicholas D Lane and Petko Georgiev. 2015. Can deep learning revolutionize mobile sensing?. In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications. 117--122.

Digital Library

[29]

Francisco Laport-López, Emilio Serrano, Javier Bajo, and Andrew T Campbell. 2020. A review of mobile sensing systems, applications, and opportunities. Knowledge and Information Systems 62, 1 (2020), 145--174.

[30]

Oscar D Lara and Miguel A Labrador. 2012. A survey on human activity recognition using wearable sensors. IEEE communications surveys & tutorials 15, 3 (2012), 1192--1209.

[31]

Junhyun Lee, Inyeop Lee, and Jaewoo Kang. 2019. Self-attention graph pooling. arXiv preprint arXiv:1904.08082 (2019).

[32]

Jundong Li, Liang Wu, Ruocheng Guo, Chenghao Liu, and Huan Liu. 2019. Multilevel network embedding with boosted low-rank matrix approximation. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 49--56.

Digital Library

[33]

Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015).

[34]

Andy Liaw, Matthew Wiener, et al. 2002. Classification and regression by randomForest. R news 2, 3 (2002), 18--22.

[35]

Xin Liu, Tsuyoshi Murata, Kyoung-Sook Kim, Chatchawan Kotarasu, and Chenyi Zhuang. 2019. A general view for network embedding as matrix factorization. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. 375--383.

Digital Library

[36]

Zheng Lou, Lili Wang, and Guozhen Shen. 2018. Recent advances in smart wearable sensing systems. Advanced Materials Technologies 3, 12 (2018), 1800444.

[37]

Fenglong Ma, Shiran Zhong, Jing Gao, and Ling Bian. 2019. Influenza-Like Symptom Prediction by Analyzing Self-Reported Health Status and Human Mobility Behaviors. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 233--242.

Digital Library

[38]

Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed, and Amir Saffari. 2018. Interpretable graph convolutional neural networks for inference on noisy knowledge graphs. arXiv preprint arXiv:1812.00279 (2018).

[39]

Donald R Olson, Kevin J Konty, Marc Paladini, Cecile Viboud, and Lone Simonsen. 2013. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLoS Comput Biol 9, 10 (2013), e1003256.

[40]

Fred C Pampel. 2020. Logistic regression: A primer. Vol. 132. SAGE Publications, Incorporated.

[41]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online Learning of Social Representations. CoRR abs/1403.6652 (2014). arXiv:1403.6652 http://arxiv.org/abs/1403.6652

Digital Library

[42]

Bryan Perozzi, Vivek Kulkarni, and Steven Skiena. 2016. Walklets: Multi-scale graph embeddings for interpretable network classification. arXiv preprint arXiv:1605.02115 (2016).

[43]

Piero Poletti, Roberto Visintainer, Bruno Lepri, and Stefano Merler. 2017. The interplay between individual social behavior and clinical symptoms in small clustered groups. BMC infectious diseases 17, 1 (2017), 521.

[44]

Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann. 2019. Explainability methods for graph convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 10772--10781.

[45]

Johan AK Suykens and Joos Vandewalle. 1999. Least squares support vector machine classifiers. Neural processing letters 9, 3 (1999), 293--300.

Digital Library

[46]

Wen Torng and Russ B Altman. 2019. Graph convolutional neural networks for predicting drug-target interactions. Journal of Chemical Information and Modeling 59, 10 (2019), 4131--4149.

[47]

Kristin van Barneveld, Michael Quinlan, Peter Kriesler, Anne Junor, Fran Baum, Anis Chowdhury, Pramod N Junankar, Stephen Clibborn, Frances Flanagan, Chris F Wright, et al. 2020. The COVID-19 pandemic: Lessons on building more equal and sustainable societies. The Economic and Labour Relations Review 31, 2 (2020), 133--157.

[48]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).

[49]

Svitlana Volkova, Ellyn Ayton, Katherine Porterfield, and Courtney D Corley. 2017. Forecasting influenza-like illness dynamics for military populations using neural networks and social media. PloS one 12, 12 (2017), e0188941.

[50]

Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. 2020. Traffic Flow Prediction via Spatial Temporal Graph Neural Network. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW '20). Association for Computing Machinery, New York, NY, USA, 1082--1092.

Digital Library

[51]

Congyu Wu, Mehdi Boukhechba, Lihua Cai, Laura E Barnes, and Matthew S Gerber. 2018. Improving momentary stress measurement and prediction with bluetooth encounter networks. Smart Health 9 (2018), 219--231.

[52]

Congyu Wu, Lihua Cai, Matthew S Gerber, Mehdi Boukhechba, and Laura E Barnes. 2018. Vector Space Representation of Bluetooth Encounters for Mental Health Inference. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. 1691--1699.

Digital Library

[53]

Ning Wu, Xin Wayne Zhao, Jingyuan Wang, and Dayan Pan. 2020. Learning Effective Road Network Representation with Hierarchical Graph Neural Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Virtual Event, CA, USA) (KDD '20). Association for Computing Machinery, New York, NY, USA, 6--14.

Digital Library

[54]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems (2020).

[55]

Haoyi Xiong, Yu Huang, Laura E. Barnes, and Matthew S. Gerber. 2016. Sensus: A Cross-Platform, General-Purpose System for Mobile Crowdsensing in Human-Subject Studies. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Heidelberg, Germany) (UbiComp '16). Association for Computing Machinery, New York, NY, USA, 415--426.

Digital Library

[56]

Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018).

[57]

Mohammad Yamin. 2020. Counting the cost of COVID-19. International Journal of Information Technology (2020), 1--7.

[58]

Chao-Tung Yang, Yuan-An Chen, Yu-Wei Chan, Chia-Lin Lee, Yu-Tse Tsan, WeiCheng Chan, and Po-Yu Liu. 2020. Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources. The Journal of Supercomputing (2020), 1--27.

[59]

Dingqi Yang, Paolo Rosso, Bin Li, and Philippe Cudre-Mauroux. 2019. Nodesketch: Highly-efficient graph embeddings via recursive sketching. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1162--1172.

Digital Library

[60]

Alice Zheng and Amanda Casari. 2018. Feature engineering for machine learning: principles and techniques for data scientists. " O'Reilly Media, Inc.".

Digital Library

[61]

Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. 2016. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2921--2929.

Cited By

Boukhechba MReynoso EPandis IMosca KHristov RYue SAi YRahul HKatabi DMorris MAvey S(2023)Digital Phenotyping of Autoimmune Diseases Using Non-Contact Radio Frequency Sensing: A Longitudinal Study Comparing Systemic Lupus Erythematosus and Healthy ParticipantsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3611328(615-619)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3611328
Dong GTang MWang ZGao JGuo SCai LGutierrez RCampbel BBarnes LBoukhechba M(2023)Graph Neural Networks in IoT: A SurveyACM Transactions on Sensor Networks10.1145/356597319:2(1-50)Online publication date: 5-Apr-2023
https://dl.acm.org/doi/10.1145/3565973

Index Terms

Influenza-like symptom recognition using mobile sensing and graph neural networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing design and evaluation methods

Recommendations

Graph Neural Networks: Foundation, Frontiers and Applications
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning, have become one of the fastest-...
Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

Continuously monitoring schizophrenia patients’ psychiatric symptoms is crucial for in-time intervention and treatment adjustment. The Brief Psychiatric Rating Scale (BPRS) is a survey administered by clinicians to evaluate symptom severity in ...
Continuous-time generative graph neural network for attributed dynamic graphs: student research abstract
SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

The history of neural networks dates back to the early 1940s and has not only evolved rapidly over time but they are now amongst one of the most popular and powerful machine learning techniques¹. In this area, graph representation learning (GRL) using ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHIL '21: Proceedings of the Conference on Health, Inference, and Learning

April 2021

309 pages

ISBN:9781450383592

DOI:10.1145/3450439

General Chair:
Marzyeh Ghassemi
University of Toronto and Vector Institute
,
Program Chairs:
Tristan Naumann
Microsoft Research Redmond
,
Emma Pierson
Stanford University and Microsoft Research New England

Copyright © 2021 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 ACM 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

ACM: Association for Computing Machinery

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

DARPA

Conference

ACM CHIL '21

Sponsor:

ACM

ACM CHIL '21: ACM Conference on Health, Inference, and Learning

April 8 - 10, 2021

Virtual Event, USA

Acceptance Rates

CHIL '21 Paper Acceptance Rate 27 of 110 submissions, 25%;

Overall Acceptance Rate 27 of 110 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
879
Total Downloads

Downloads (Last 12 months)171
Downloads (Last 6 weeks)24

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Boukhechba MReynoso EPandis IMosca KHristov RYue SAi YRahul HKatabi DMorris MAvey S(2023)Digital Phenotyping of Autoimmune Diseases Using Non-Contact Radio Frequency Sensing: A Longitudinal Study Comparing Systemic Lupus Erythematosus and Healthy ParticipantsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3611328(615-619)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3611328
Dong GTang MWang ZGao JGuo SCai LGutierrez RCampbel BBarnes LBoukhechba M(2023)Graph Neural Networks in IoT: A SurveyACM Transactions on Sensor Networks10.1145/356597319:2(1-50)Online publication date: 5-Apr-2023
https://dl.acm.org/doi/10.1145/3565973

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