The growing need and market demand for point of care (POC) systems to improve patient’s quality of life are driving the development of wireless nanotechnology based smart systems for diagnosis and treatment of various chronic and life threatening diseases. POC diagnostics for neurological, metabolic, and cardiovascular disorders require constant long term untethered monitoring of individuals. Given the uncertainty associated with location and time at which immediate diagnosis and treatment may be required, constant vigilance and monitoring are the only practical solutions. What is needed is for a remote cyber-enabled health care smart system incorporating novel ideas from nanotechnology, low power embedded systems, wireless networking, and cloud computing to fundamentally advance. To meet this goal, we present e-Nanoflex platform, which is capable of monitoring patient health wherever they may be and communicating the data in real time to a physician or a hospital. Unlike state-of-the-art systems that are either local sensor systems or rely on custom relaying devices, e-Nanoflex is a highly nonintrusive and inexpensive end-to-end cyber-physical system. Using nanostructured sensors, e-Nanoflex provides nearly invisible monitoring of physiological conditions. It relies on smartphones to filter, compress, and relay geo-tagged data. Further, it ties to a backend cloud infrastructure for data storage, data dissemination, and abnormality detection using machine learning techniques. e-Nanoflex is a complete end-to-end system for physiological sensing and geo-tagged data dissemination to hospitals and caregivers. It is intended as a basic platform that can support any nanostructure based flexible sensor to monitor a variety of conditions such as body temperature, respiration air flow, oxygen consumption, bioelectric signals, pulse oximetry, muscle activity, and neural activity. Additionally, to address the cost of manufacturing sensors, e-Nanoflex uses a low cost production technique based on roll to roll gravure printing. We show the efficacy of our platform through a case study that involves acquiring electrocardiogram signals using gold nano-electrodes fabricated on a flexible substrate.

1.
Tabibiazar
,
R.
, and
Edelman
,
S. V.
, 2003, “
Silent Ischemia in People With Diabetes: A Condition That Must Be Heard
,”
Clinical Diabetes
,
21
(
1
), pp.
5
9
.
2.
Vespa
,
P. M.
,
Nuwer
,
M. R.
,
Juhász
,
C.
,
Alexander
,
M.
,
Nenov
,
V.
,
Martin
,
N.
, and
Becker
,
D. P.
, 1997, “
Early Detection of Vasospasm After Acute Subarachnoid Hemorrhage Using Continuous EEG ICU Monitoring
,”
Electroencephalogr. Clin. Neurophysiol.
0013-4649,
103
, pp.
607
615
.
3.
Polar Heart Rate Monitor, Polar Electro® www.polarusa.comwww.polarusa.com
4.
Home Sleep Test Device, Watermark Medical® www.watermarkmedical.comwww.watermarkmedical.com
5.
McAlpine
,
M. C.
,
Ahmad
,
H.
,
Wang
,
D.
, and
Heath
,
J. R.
, 2007, “
Highly Ordered Nanowire Arrays on Plastic Substrates for Ultrasensitive Flexible Chemical Sensors
,”
Nature Mater.
1476-1122,
6
(
5
), pp.
379
384
.
6.
Kwon
,
M.
, and
Hong
,
Y.
, 2009, “
Flexible Temperature Sensor Array of PDMS-Encapsulated Conductive CNT Thin Films Fabricated by Solution Process
,”
2009 International Semiconductor Device Research Symposium (ISDRS 2009)
, p.
2
.
7.
Yu
,
X.
,
Rajamani
,
R.
,
Stelson
,
K. A.
, and
Cui
,
T.
, 2006, “
Carbon Nanotube-Based Transparent Thin Film Acoustic Actuators and Sensors
,”
Sens. Actuators, A
0924-4247,
132
(
2
), pp.
626
631
.
8.
Jung
,
S.
,
Ji
,
T.
,
Xie
,
J.
, and
Varadan
,
V. K.
, 2007, “
Flexible Strain Sensors Based on Pentacene-Carbon Nanotube Composite Thin Films
,”
Seventh IEEE International Conference on Nanotechnology—IEEE-NANO 2007
, pp.
375
378
.
9.
Varadan
,
V. K.
,
Oh
,
S.
,
Kwon
,
H.
, and
Hankins
,
P.
, 2010, “
Wireless Point-of-Care Diagnosis for Sleep Disorder With Dry Nanowire Electrodes
,”
J. Nanotechnol. Eng. Med.
1949-2944,
1
, p.
031012
.
10.
Kailanto
,
H.
,
Hyvarinen
,
E.
, and
Hyttinen
,
J.
, 2008, “
Mobile ECG Measurement and Analysis System Using Mobile Phone as the Base Station
,”
Second International Conference on Pervasive Computing Technologies for Healthcare
, pp.
12
14
.
11.
Kinsner
,
W.
, and
Greenfield
,
R. H.
, 1991, “
The Lempel–Ziv–Welch (LZW) Data Compression Algorithm for Packet Radio
,”
WESCANEX ‘91 “IEEE Western Canada Conference on Computer, Power and Communications Systems in a Rural Environment”
, May 29–30, pp.
225
229
.
12.
Guo
,
H.
, and
Burrus
,
C. S.
, 1997, “
Waveform and Image Compression Using the Burrows Wheeler Transform and the Wavelet Transform
,”
International Conference on Image Processing
, Oct. 26–29, Vol.
1
, pp.
65
68
.
13.
Halperin
,
D.
,
Heydt-Benjamin
,
T. S.
,
Ransford
,
B.
,
Clark
,
S. S.
,
Defend
,
B.
,
Morgan
,
W.
,
Fu
,
K.
,
Kohno
,
T.
, and
Maisel
,
W. H.
, 2008, “
Pacemakers and Implantable Cardiac Defibrillators: Software Radio Attacks and Zero-Power Defenses
,”
Proceedings of the 29th Annual IEEE Symposium on Security and Privacy
.
14.
Martin
,
T.
,
Hsiao
,
M.
,
Ha
,
D.
, and
Krishnaswami
,
J.
, 2004, “
Denial-of-Service Attacks on Battery-Powered Mobile Computers
,”
PERCOM
.
15.
Harper
,
S.
, and
Athanas
,
P.
, 2004, “
A Security Policy Based Upon Hardware Encryption
,”
Proceedings of the 37th Annual Hawaii International Conference on System Sciences
, Jan. 5–8, p.
8
.
16.
Mencer
,
O.
,
Morf
,
M.
, and
Flynn
,
M. J.
, 1998, “
Hardware Software Tri-Design of Encryption for Mobile Communication Units
,”
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing
, May 12–15, Vol.
5
, pp.
3045
3048
.
17.
Power Analysis, StatSoft.
18.
Molina
,
A. D.
,
Salajegheh
,
M.
, and
Fu
,
K.
, 2009, “
HICCUPS: Health Information Collaborative Collection Using Privacy and Security
,”
Proceedings of the Workshop on Security and Privacy in Medical and Home-Care Systems (SPIMACS)
,
ACM
,
Chicago, Illinois
, pp.
21
30
.
19.
Salajegheh
,
M.
,
Molina
,
A.
, and
Fu
,
K.
, 2009, “
Privacy of Home Telemedicine: Encryption Is Not Enough
,”
ASME J. Med. Devices
1932-6181,
3
(
2
), p.
027503
.
20.
Li
,
M.
, and
Zhou
,
Z. -H.
, 2007, “
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
,”
IEEE Trans. Syst. Man Cybern., Part A. Syst. Humans
1083-4427,
37
(
6
), pp.
1083
1098
.
21.
Searle
,
A.
, and
Kirkup
,
L.
, 2000, “
A Direct Comparison of Wet, Dry and Insulating Bioelectric Recording Electrodes
,”
Physiol. Meas
0967-3334,
21
, pp.
271
283
.
22.
Noueihed
,
J.
,
Diemer
,
R.
,
Chakraborty
,
S.
, and
Biala
,
S.
, 2010, “
Comparing Bluetooth HDP and SPP for Mobile Health Devices
,”
2010 International Conference on Body Sensor Networks (BSN)
, Jun. 7–9, pp.
222
227
.
23.
Bronzino
,
J. D.
, 2006,
Biomedical Engineering Fundamentals
, 2nd ed.,
CRC
,
Boca Raton, Florida
, pp.
26
-2–26-
3
.
24.
Bordjiba
,
T.
,
Mohamedi
,
M.
, and
Dao
,
L. H.
, 2007, “
Binderless Carbon Nanotube/Carbon Fibre Composites for Electrochemical Micropower Sources
,”
Nanotechnology
0957-4484,
18
(
3
), p.
035202
.
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