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Co-MEAL: Cost-Optimal Multi-Expert Active Learning Architecture for Mobile Health Monitoring

Authors:

Assefaw H. GebremedhinAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 432 - 441

https://doi.org/10.1145/3107411.3107430

Published: 20 August 2017 Publication History

Abstract

Mobile health monitoring plays a central role in a variety of health-care applications. Using mobile technology, health-care providers can access clinical information and communicate with subjects in real-time. Due to the sensitive nature of health-care applications, these systems need to process physiological signals highly accurately. However, as mobile devices are employed in dynamic environments, the accuracy of a machine learning model drops whenever a change in configuration of the system occurs. Therefore, data mining and machine learning techniques that specifically address challenges associated with dynamic environments (e.g. different users, signal heterogeneity) are needed. In this paper, using active learning as an organizing principle, we propose a cost-optimal multiple-expert architecture to adapt a machine learning model (e.g. classifier) developed in a given context to a new context or configuration. More specifically, in our architecture, a system's machine learning model learns from experts available to the system (e.g. another mobile device, human annotator) while minimizing the cost of data labeling. Our architecture also exploits collaboration between experts to enrich their knowledge which in turn decreases both cost and uncertainty of data labeling in future steps. We demonstrate the efficacy of the architecture using a publicly available dataset on human activity. We show that the accuracy of activity recognition reaches over 85% by labeling only 15% of unlabeled data. At the same time, the number of queries from human expert is reduced by up to 82%.

References

[1]

Misha Kay, Jonathan Santos, and Marina Takane. mHealth: New horizons for health through mobile technologies. World Health Organization, 3:66--71, 2011.

[2]

Abu Saleh Mohammad Mosa, Illhoi Yoo, and Lincoln Sheets. A systematic review of healthcare applications for smartphones. BMC medical informatics and decision making, 12(1):67, 2012.

[3]

Zachary W Adams, Erin A McClure, Kevin M Gray, Carla Kmett Danielson, Frank A Treiber, and Kenneth J Ruggiero. Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research. Journal of psychiatric research, 85:1--14, 2017.

[4]

Caroline Free, Gemma Phillips, Louise Watson, Leandro Galli, Lambert Felix, Phil Edwards, Vikram Patel, and Andy Haines. The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Med, 10(1):e1001363, 2013.

[5]

Daniel F Hayes, Hugh S Markus, R David Leslie, and Eric J Topol. Personalized medicine: risk prediction, targeted therapies and mobile health technology. BMC Medicine, 12(1):37, 2014.

[6]

Armin Shahrokni, Sanam Mahmoudzadeh, Ramyar Saeedi, and Hassan Ghasemzadeh. Older people with access to hand-held devices: Who are they? Telemedicine and e-Health, 21(7):550--556, 2015.

[7]

Emanuela Marcelli, Alessandro Capucci, Gabriele Minardi, and Laura Cercenelli. Multi-sense cardiopatch: A wearable patch for remote monitoring of electro-mechanical cardiac activity. Asaio Journal, 63(1):73--79, 2017.

[8]

Philip E Eggers, Eric A Eggers, and Benjamin Z Bailey. Wearable apparatus, system and method for detection of cardiac arrest and alerting emergency response, June 23 2016. US Patent 20,160,174,857.

[9]

Javad Birjandtalab, Maziyar Baran Pouyan, Diana Cogan, Mehrdad Nourani, and Jay Harvey. Automated seizure detection using limited-channel eeg and non-linear dimension reduction. Computers in Biology and Medicine, 82:49--58, 2017.

Digital Library

[10]

David Zakim and Matthias Schwab. Data collection as a barrier to personalized medicine. Trends in pharmacological sciences, 36(2):68--71, 2015.

[11]

Ramin Fallahzadeh, Mahdi Pedram, Ramyar Saeedi, Bahman Sadeghil, Michael Ongl, and Hassan Ghasemzadeh. Smart-cuff: A wearable bio-sensing platform with activity-sensitive information quality assessment for monitoring ankle edema. 2015.

[12]

Pio Alfredo Di Tore. Situation awareness and complexity: the role of wearable technologies in sports science. Journal of Human Sport and Exercise, 10(1proc):500--506, 2015.

[13]

Rachel Lomasky, Carla E Brodley, Matthew Aernecke, David Walt, and Mark Friedl. Active class selection. In European Conference on Machine Learning, pages 640--647. Springer, 2007.

Digital Library

[14]

Burr Settles, Mark Craven, and Lewis Friedland. Active learning with real annotation costs. In Proceedings of the NIPS workshop on cost-sensitive learning, pages 1--10, 2008.

[15]

Hesam Sagha, José del R Millán, and Ricardo Chavarriaga. A probabilistic approach to handle missing data for multi-sensory activity recognition. In Workshop on Context Awareness and Information Processing in Opportunistic Ubiquitous Systems at 12th ACM International Conference on Ubiquitous Computing, number EPFL-CONF-151974, 2010.

[16]

Ramin Fallahzadeh and Hassan Ghasemzadeh. Personalization without user interruption: Boosting activity recognition in new subjects using unlabeled data (an uninformed cross-subject transfer learning algorithm). In International Conference on Cyber-Physical Systems (ICCPS). ACM/IEEE, 2017.

Digital Library

[17]

Vincent Wenchen Zheng, Derek Hao Hu, and Qiang Yang. Cross-domain activity recognition. In Proceedings of the 11th international conference on Ubiquitous computing, pages 61--70. ACM, 2009.

Digital Library

[18]

Burr Settles. Active learning literature survey. Computer Science Technical Report 1648, University of Wisconsin, Madison, (1--67):67, 2010.

[19]

Wei Chu, Martin Zinkevich, Lihong Li, Achint Thomas, and Belle Tseng. Unbiased online active learning in data streams. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 195--203. ACM, 2011.

Digital Library

[20]

Bo Wang and John Tsotsos. Dynamic label propagation for semi-supervised multi-class multi-label classification. Pattern Recognition, 52:75--84, 2016.

Digital Library

[21]

Adrian Calma, Jan Marco Leimeister, Paul Lukowicz, Sarah Oeste-Reiß, Tobias Reitmaier, Albrecht Schmidt, Bernhard Sick, Gerd Stumme, and Katharina Anna Zweig. From active learning to dedicated collaborative interactive learning. In ARCS 2016, pages 1--8. VDE, 2016.

[22]

Zelun Zhang and Stefan Poslad. Improved use of foot force sensors and mobile phone gps for mobility activity recognition. IEEE Sensors Journal, 14(12):4340--4347, 2014.

[23]

Ramyar Saeedi, Navid Amini, and Hassan Ghasemzadeh. Patient-centric on-body sensor localization in smart health systems. In Signals, Systems and Computers, 2014 48th Asilomar Conference on, pages 2081--2085. IEEE, 2014.

[24]

Timo Sztyler and Heiner Stuckenschmidt. On-body localization of wearable devices: an investigation of position-aware activity recognition. In Pervasive Computing and Communications (PerCom), 2016 IEEE International Conference on, pages 1--9. IEEE, 2016.

[25]

H. Ghasemzadeh, E. Guenterberg, and R. Jafari. Energy-Efficient Information-Driven Coverage for Physical Movement Monitoring in Body Sensor Networks. IEEE Journal on Selected Areas in Communications, 27:58--69, 2009.

Digital Library

[26]

Hassan Ghasemezadeh, Sarah Ostadabbas, Eric Guenterberg, and Alexandros Pantelopoulos. Wireless medical embedded systems: A review of signal processing techniques for classification. IEEE Sensors Journal, 2012, In Press.

[27]

Yufei Chen and Chao Shen. Performance analysis of smartphone-sensor behavior for human activity recognition. IEEE Access, 5:3095--3110, 2017.

[28]

Piero Zappi, Thomas Stiefmeier, Elisabetta Farella, Daniel Roggen, Luca Benini, and Gerhard Troster. Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness. In Intelligent Sensors, Sensor Networks and Information, 2007. ISSNIP 2007. 3rd International Conference on, pages 281--286. IEEE, 2007.

[29]

Kai Kunze and Paul Lukowicz. Using acceleration signatures from everyday activities for on-body device location. In Wearable Computers, 2007 11th IEEE International Symposium on, pages 115--116. IEEE, 2007.

Digital Library

[30]

Allan Stisen, Henrik Blunck, Sourav Bhattacharya, Thor Siiger Prentow, Mikkel Baun Kjærgaard, Anind Dey, Tobias Sonne, and Mads Møller Jensen. Smart devices are different: Assessing and mitigating mobile sensing heterogeneities for activity recognition. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, pages 127--140. ACM, 2015.

Digital Library

[31]

Ramyar Saeedi, Ramin Fallahzadeh, Parastoo Alinia, and Hassan Ghasemzadeh. An energy-efficient computational model for uncertainty management in dynamically changing networked wearables. In International Symposium on Low Power Electronics and Design (ISLPED). IEEE/ACM, 2016.

Digital Library

[32]

Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10):1345--1359, 2010.

Digital Library

[33]

Sandeepkumar Satpal and Sunita Sarawagi. Domain adaptation of conditional probability models via feature subsetting. In European Conference on Principles of Data Mining and Knowledge Discovery, pages 224--235. Springer, 2007.

[34]

Kyle Dillon Feuz and Diane J. Cook. Heterogeneous transfer learning for activity recognition using heuristic search techniques. International Journal of Pervasive Computing and Communications, 10(4):393--418, 2014.

[35]

Ramyar Saeedi, Hassan Ghasemzadeh, and Assefaw H. Gebremedhin. Transfer learning algorithms for autonomous reconfiguration of wearable systems. In IEEE International Conference on Big Data. IEEE, 2016.

[36]

Seyed Ali Rokni and Hassan Ghasemzadeh. Plug-n-learn: automatic learning of computational algorithms in human-centered internet-of-things applications. In Proceedings of the 53rd Annual Design Automation Conference, page 139. ACM, 2016.

Digital Library

[37]

Akshay Gadde, Aamir Anis, and Antonio Ortega. Active semi-supervised learning using sampling theory for graph signals. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 492--501. ACM, 2014.

Digital Library

[38]

Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, and Alexander G Hauptmann. Multi-class active learning by uncertainty sampling with diversity maximization. International Journal of Computer Vision, 113(2):113--127, 2015.

Digital Library

[39]

Andreas Holzinger. Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Informatics, 3(2):119--131, 2016.

[40]

Brent Longstaff, Sasank Reddy, and Deborah Estrin. Improving activity classification for health applications on mobile devices using active and semi-supervised learning. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2010 4th International Conference on-NO PERMISSIONS, pages 1--7. IEEE, 2010.

[41]

Salikh Bagaveyev and Diane J Cook. Designing and evaluating active learning methods for activity recognition. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, pages 469--478. ACM, 2014.

Digital Library

[42]

Enamul Hoque and John Stankovic. AALO: Activity recognition in smart homes using active learning in the presence of overlapped activities. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012 6th International Conference on, pages 139--146. IEEE, 2012.

Cited By

Hernandez-Cruz NNugent CZhang SMcChesney I(2021)The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous SensingApplied Sciences10.3390/app1116766011:16(7660)Online publication date: 20-Aug-2021
https://doi.org/10.3390/app11167660
Saeedi RSadanandan SSrivastava ADavies KGebremedhin A(2021)An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV DisaggregationIEEE Transactions on Industrial Informatics10.1109/TII.2021.306089817:10(7060-7069)Online publication date: Oct-2021
https://doi.org/10.1109/TII.2021.3060898
Alawadi SKebande VDong YBugeja JPersson JOlsson C(2021)A Federated Interactive Learning IoT-Based Health Monitoring PlatformNew Trends in Database and Information Systems10.1007/978-3-030-85082-1_21(235-246)Online publication date: 17-Jul-2021
https://doi.org/10.1007/978-3-030-85082-1_21
Show More Cited By

Index Terms

Co-MEAL: Cost-Optimal Multi-Expert Active Learning Architecture for Mobile Health Monitoring

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cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 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]

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Published: 20 August 2017

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BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

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ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Hernandez-Cruz NNugent CZhang SMcChesney I(2021)The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous SensingApplied Sciences10.3390/app1116766011:16(7660)Online publication date: 20-Aug-2021
https://doi.org/10.3390/app11167660
Saeedi RSadanandan SSrivastava ADavies KGebremedhin A(2021)An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV DisaggregationIEEE Transactions on Industrial Informatics10.1109/TII.2021.306089817:10(7060-7069)Online publication date: Oct-2021
https://doi.org/10.1109/TII.2021.3060898
Alawadi SKebande VDong YBugeja JPersson JOlsson C(2021)A Federated Interactive Learning IoT-Based Health Monitoring PlatformNew Trends in Database and Information Systems10.1007/978-3-030-85082-1_21(235-246)Online publication date: 17-Jul-2021
https://doi.org/10.1007/978-3-030-85082-1_21
Saeedi RSasani KGebremedhin A(2020)Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and EvaluationSensors10.3390/s2007193220:7(1932)Online publication date: 30-Mar-2020
https://doi.org/10.3390/s20071932
Saeedi RGebremedhin A(2020)A Signal-Level Transfer Learning Framework for Autonomous Reconfiguration of Wearable SystemsIEEE Transactions on Mobile Computing10.1109/TMC.2018.287867319:3(513-527)Online publication date: 1-Mar-2020
https://doi.org/10.1109/TMC.2018.2878673
Natarajan AGanesan DMarlin B(2019)Hierarchical Active Learning for Model Personalization in the Presence of Label Scarcity2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)10.1109/BSN.2019.8771081(1-4)Online publication date: May-2019
https://doi.org/10.1109/BSN.2019.8771081
Sztyler TMeilicke CStuckenschmidt H(2018)Towards Systematic Benchmarking of Activity Recognition Algorithms2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PERCOMW.2018.8480409(15-20)Online publication date: Mar-2018
https://doi.org/10.1109/PERCOMW.2018.8480409
Saeedi RNorgaard SGebremedhin A(2017)A closed-loop deep learning architecture for robust activity recognition using wearable sensors2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8257960(473-479)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8257960

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