What Does Big Data Mean for Wearable Sensor Systems?

S. J. Redmond; N. H. Lovell; G. Z. Yang; A. Horsch; P. Lukowicz; L. Murrugarra; M. Marschollek

doi:10.15265/IY-2014-0019

Yearbook of Medical Informatics, Table of Contents

Yearb Med Inform 2014; 23(01): 135-142
DOI: 10.15265/IY-2014-0019

Original Article

Georg Thieme Verlag KG Stuttgart

What Does Big Data Mean for Wearable Sensor Systems?

Contribution of the IMIA Wearable Sensors in Healthcare WG

Authors

S. J. Redmond

¹Graduate School of Biomedical Engineering, UNSW Australia, Sydney, Australia
N. H. Lovell

¹Graduate School of Biomedical Engineering, UNSW Australia, Sydney, Australia
G. Z. Yang

²Imperial College London, London, United Kingdom
A. Horsch

³Department of Medical Statistics and Epidemiology, Technische Universität München, Munich, Germany

⁴Department of Clinical Medicine, Telemedicine Working Group, University of Tromsø, Tromsø, Norway

⁵Department of Computer Science, MI&T Group, University of Tromsø, Tromsø, Norway
P. Lukowicz

⁶German Research Center for Artificial Intelligence, Kaiserslautern, Germany
L. Murrugarra

⁷Alexander von Humboldt Institute for Tropical Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
M. Marschollek

⁸Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig-Institute of Technology and Hanover Medical School, Hanover, Germany

Abstract

Summary

Objectives:The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare.

Methods: The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Health-care Working Group.

Results: The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring.

Conclusions: Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals.

Keywords

Big data - wearable sensors - ambulatory monitoring - standards - privacy

Full Text

References

References
1 Shany T, Redmond SJ, Narayanan MR, Lovell NH. Sensors-Based Wearable Systems for Monitoring of Human Movement and Falls. IEEE Sens J 2012; 12 (03) 658-70.
2 Hilbel T, Helms TM, Mikus G, Katus HA, Zugck C. Telemetrie. Herzschr Elektrophys 2008; 19 (03) 146-54.
3 Oswald A. At the Heart of the Invention: The development of the Holter Monitor. National Museum of American History; [08/01/2014]; Available from: http://blog.americanhistory.si.edu/osaycanyousee/2011/11/at-the-heart-of-the-invention-the-development-of-the-holter-monitor-1.html.
4 Ludwig W, Wolf K-H, Duwenkamp C, Gusew N, Hellrung N, Marschollek M. et al. Health-enabling technologies for the elderly – An overview of services based on a literature review. Comput Methods Programs Biomed 2012; 106 (02) 70-8.
5 Shaw FE, Bond J, Richardson DA, Dawson P, Steen IN, McKeith IG. et al. Multifactorial intervention after a fall in older people with cognitive impairment and dementia presenting to the accident and emergency department: randomised controlled trial. BMJ 2003; 326 7380 73.
6 Schwickert L, Becker C, Lindemann U, Maréchal C, Bourke A, Chiari L. et al. Fall detection with body-worn sensors. Z Gerontol Geriat 2013; 46 (08) 706-19.
7 Bagalà F, Becker C, Cappello A, Chiari L, Aminian K, Hausdorff JM. et al. Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls. PLoS One 2012; 7 (05) e37062.
8 Kohlmann M, Haux R, Marschollek M, Wolf KH, Gietzelt M, Song B. High intensity, multimodality and incoherence: grand challenges in the analysis of data for health-enabling technologies. Stud Health Technol Inform 2013; 192: 967.
9 Narayanan MR, Redmond SJ, Scalzi ME, Lord SR, Celler BG, Lovell NH. Longitudinal falls-risk estimation using triaxial accelerometry. IEEE Trans Biomed Eng 2010; 57 (03) 534-41.
10 Marschollek M, Rehwald A, Wolf KH, Gietzelt M, Nemitz G, Meyer Zu Schwabedissen H. et al. Sensor-based fall risk assessment--an expert ‘to go’. Methods Inf Med 2011; 50 (05) 420-6.
11 Peters SG, Buntrock JD. Big Data and the Electronic Health Record. J Ambul Care Manage 2014; 37 (03) 206-10.
12 Bellazzi R. Big Data and Biomedical Informatics: A Challenging Opportunity. Yearb Med Inform 2014; 8-13.
13 Science [Internet].. Dealing with data. 2011; Available from: http://www.sciencemag.org/sitespecial/data.
14 Nature [Internet].. Big data: science in the petabyte era. 2008; Available from: http://www.nature.com nature/journal/v455/n7209/full/455001a.html.
15 Bianchi F, Redmond SJ, Narayanan MR, Cerutti S, Lovell NH. Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2010; 18 (06) 619-27.
16 Gari DC, George BM. Signal quality in cardiorespiratory monitoring. Physiol Meas 2012 33(9).
17 Redmond SJ, Xie Y, Chang D, Basilakis J, Lovell NH. Electrocardiogram Signal Quality Measures for Unsupervised Telehealth Environments. Physiol Meas 2012; 33: 1517-33.
18 Sukor JA, Redmond SJ, Chan GSH, Lovell NH. Signal quality measures for unsupervised blood pressure measurement. Physiol Meas 2012; 33 (03) 465-86.
19 Sukor JA, Redmond SJ, Lovell NH. Signal quality measures for pulse oximetry through waveform morphology analysis. Physiol Meas 2011; 32 (03) 369-84.
20 The HDF Group.. HFD5. 2014 Available from http://www.hdfgroup.org/HDF5.
21 Medical Imaging & Technology Alliance.. The DICOM Standard. 2014 Available from: medical.nema.org/standard.html.
22 Klenk J, Chiari L, Helbostad JL, Zijlstra W, Aminian K, Todd C. et al. Development of a standard fall data format for signals from body-worn sensors. Z Gerontol Geriat. 2013; 46 (08) 720-6.
23 Hunter P, Bradley C, Britten R, Brooks D, Carotenuto L, Christie R. et al. The VPH-Physiome Project: Standards, tools and databases for multi-scale physiological modelling. In: Ambrosi D, Quarteroni A, Rozza G. editors. Modeling of Physiological Flows. Springer Milan; 2012. p. 205-50
24 Brooks DJ, Hunter PJ, Smaill BH, Titchener MR. editors. BioSignalML - A meta-model for biosignals. Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2011 Aug. 30 2011-Sept. 3 2011
25 Cleveland WS. Robust Locally Weighted Regression and Smoothing Scatterplots. J Am Stat Assoc 1979; 74 (368) 829-36.
26 Chatfield C. Model Uncertainty, Data Mining and Statistical Inference. Journal of the J R Stat Soc Ser A Stat Soc 1995; 158 (03) 419-66.
27 Altman DG, Royston P. What do we mean by validating a prognostic model?. Statistics in Medicine 2000; 19 (04) 453-73.
28 Editorial.. Data’s shameful neglect. Nature 2009; 461 7261 145.
29 Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG. et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 2000; 101 (23) e215-e20.
30 Charles RG, Heemels JP, Westrum BL. European Excel™ Study G. Accelerometer-Based Adaptive-Rate Pacing: A Multicenter Study. Pacing Clin Electrophysiol 1993; 16 (03) 418-25.
31 Pfenniger A, Jonsson M, Zurbuchen A, Koch V, Vogel R. Energy Harvesting from the Cardiovascular System, or How to Get a Little Help from Yourself. Ann Biomed Eng 2013; 41 (11) 2248-63.
32 Demiris G, Hensel BK. Technologies for an Aging Society: A Systematic Review of “Smart Home” Applications. Yearb Med Infom 2008: Access to Health Information. 2008; 3 (01) 33-40.

Supplementary Material

Online Supplementary Material(PDF) (PDF) (opens in new window)