![](bg7.png)
audio, video etc. through a cell phone to a cen-
tral server for archiving and incorporation into an
electronic medical record, and to a remote specialist
for real-time decision support. The prime focus of
SANA tool is to transmit the medical information to
the reach of various stakeholders in the healthcare do-
main. However, there is no support in SANA for uti-
lizing electronic sensors within the mHealth system,
and the user is required to feed the data manually into
the Android application making the application prone
to human errors.
Open Data Kit (ODK, 2009) is a framework for
building user interfaces, collecting data on mobile
devices, and aggregating them onto a server. The
ODK framework supports generic interfaces for a
class of sensors, both internal and external to an An-
droid device, although at the time of implementa-
tion of mDROID, these were not released for pro-
duction. While ODK has been used in some health-
related projects, it is not clear to what extent the type
classes of discrete and continuous medical data can be
supported in the systematic manner we have outlined
above.
This comparative analysis suggests that mDROID
promises to be a more effective tool for health work-
ers when collecting data, with authentication, at the
bottom of the pyramid in developing countries. While
the data authenticity is not foolproof, it offers some
level of confidence that the data was genuinely cap-
tured and not forged.
6 FUTURE WORK
The mDROID system described in this paper relies on
external sensors for obtaining clinical data and is tar-
geted to be operated by non-experts. The next steps
involve providing a framework that can attest to the
provenance of the data collected. The current system
captures the biometric data in the form of patient im-
age, which is not being cross-verified. Future work
involves implementation of sub-systems for acquisi-
tion and verification of biometric information of the
patient as well as the health worker. Further analy-
sis of the system reveals that the clinical data is sent
over a Bluetooth connection to the Android device
and then over a network to the server, both of which
can act as points of network vulnerabilities. This re-
quires investigation of ways to safeguard the privacy
of the medical data over any transmissions. Addi-
tionally, the current system supports a limited range
of medical sensors which are being extended to sup-
port more varieties of sensors, thereby providing a
complete low cost and efficient mHealth data acquisi-
tion system. Further, the current system works using
Bluetooth connectivity. Future scope involves sup-
porting more connectivity modes, viz., Wi-Fi, Zig-
Bee, USB, etc. Currently, the uploaded records are
sent to a small server designed for storage purposes
in XML format. The future work involves support-
ing the server with openEHR standards, and adding
functionalities for extraction and exchange of medi-
cal data from the server. Also, cloud-based solutions
can be used for storing and analyzing medical data
collected.
ACKNOWLEDGEMENTS
This work has been partially supported from the
DeitY (MCIT, Government of India) funded project
RP02597 “Foundations of Trusted and Scalable ‘Last
Mile’ Healthcare”. The fourth author also acknowl-
edges support from a Microsoft Research grant for
precursors to this work. The authors would like to
thank Mr Vijay Kumar for assistance with configur-
ing the hardware. Dr Shelly Dutta of Sahyog pro-
vided valuable information about the work routines
of healthcare workers in rural Rajasthan.
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