The Community for Technology Leaders
RSS Icon
Issue No.01 - Jan. (2013 vol.46)
pp: 28-35
Santosh Kumar , University of Memphis
Wendy Nilsen , US National Institutes of Health
Misha Pavel , Oregon Health and Science University
Mani Srivastava , University of California, Los Angeles
Mobile health (mHealth) seeks to improve individuals' health and well-being by continuously monitoring their status, rapidly diagnosing medical conditions, recognizing behaviors, and delivering just-in-time interventions, all in the user's natural mobile environment. The Web extra at is an audio interview in which Santosh Kumar, Wendy Nilsen, and Mani Srivastava discuss the path toward realizing mobile health systems.
Sensors, Mobile communication, Biomedical imaging, Medical services, Mobile handsets, Medical services, Biomedical monitoring, computing in medicine, mHealth, sensor networks, regulation
Santosh Kumar, Wendy Nilsen, Misha Pavel, Mani Srivastava, "Mobile Health: Revolutionizing Healthcare Through Transdisciplinary Research", Computer, vol.46, no. 1, pp. 28-35, Jan. 2013, doi:10.1109/MC.2012.392
1. United Nations General Assembly, “, Prevention and Control of Non-Communicable Diseases,” Report of the Secretary-General, A/66/83, 19 May 2011.
2. R.S.H. Istepanian, E. Jovanov, and Y.T. Zhang, “Guest Editorial Introduction to the Special Section on m-Health: Beyond Seamless Mobility and Global Wireless Health-Care Connectivity,” IEEE Trans. Information Technology in Biomedicine, vol. 8, no. 4, 2004, pp. 405-414.
3. F. Patolsky and C.M. Lieber, “Nanowire Nanosensors,” Materials Today, Apr. 2005, pp. 20-28.
4. H. Zhu et al., “Optical Imaging Techniques for Point-of-Care Diagnostics,” Lab on a Chip, no. 1, 2013, pp. 51-67.
5. V. Pop et al., “Human++: Wireless Autonomous Sensor Technology for Body Area Networks,” Proc. 16th Asia and South Pacific Design Automation Conf. (ASPDAC 11), IEEE, 2011, pp. 561-566.
6. H. Mamaghanian et al., “Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes,” IEEE Trans. Biomedical Eng., vol. 58, no. 9, 2011, pp. 2456-2466.
7. M. Rabbi et al., “Passive and In-Situ Assessment of Mental and Physical Well-Being Using Mobile Sensors,” Proc. 13th Int'l Conf. Ubiquitous Computing (UbiComp 11), ACM, 2011, pp. 385-394.
8. K. Plarre et al., “Continuous Inference of Psychological Stress from Sensory Measurements Collected in the Natural Environment,” Proc. Conf. Information Processing in Sensor Networks (IPSN 11), ACM, 2011, pp. 97-108.
9. M. Rahman et al., “mConverse: Inferring Conversation Episodes from Respiratory Measurements Collected in the Field,” Proc. ACM Conf. Wireless Health (WH 11), ACM, 2011; doi:10.1145/2077546.2077557.
10. A.D. Kuo, “An Optimal Control Model for Analyzing Human Postural Balance,” IEEE Trans. Biomedical Eng., vol. 42, no. 1, 1995, pp. 87-101.
11. J. Gall et al., “Hough Forests for Object Detection, Tracking, and Action Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 11, 2011, pp. 2188-2202.
12. S. Kumar et al., “mHealth Evidence Workshop: Exploring Innovative Methods to Evaluate the Efficacy and Safety of Mobile Health,” Am. J. Preventive Medicine, 2013, in press.
13. A. Raij et al., “Privacy Risks Emerging from the Adoption of Innocuous Wearable Sensors in the Mobile Environment,” Proc. Conf. Human Factors in Computing Systems (CHI 11), ACM, 2011, pp. 11-20.
14. W. Burleson et al., “Design Challenges for Secure Implantable Medical Devices,” Proc. Design Automation Conf. (DAC 12), ACM, 2012, pp. 12-17.
15. US Food and Drug Administration, “Draft Guidance for Industry and Food and Drug Administrative Staff—Mobile Medical Applications,”21 July 2011; GuidanceDocumentsucm263280.htm .
16. Office of the National Coordinator for Health Information Technology, “Mobile Devices Roundtable: Safeguarding Health Information,”16 Mar. 2012; community/healthit_hhs_gov__mobile_devices_roundtable_agenda 3846.
24 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool