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Motivation and Background

More than 25 million people in the U.S. are diagnosed with asthma, out of which 7 million are children [1]. Asthma related healthcare costs alone are around $50 billion a year [2]. Current reactive healthcare costs more than 17% of GDP in the US [3, 4]. Specifically, with the current reactive care for asthma, there were 155,000 hospital admissions and 593,000 ER visits in 2006 [16]. It is estimated that, by 2025, over 400 million people will be affected by asthma worldwide. With increasing adoption of mobile devices and low-cost sensors, an unprecedented amount of data is being collected by people [5]. This data collection has exacerbated the problem of understanding the data and making sense of it. In this project, we explore the role of knowledge empowered algorithms in making sense of this data deluge in the context of asthma assessment and management.

Asthma: Challenges and Opportunities

Asthma is a good example of a problem that spans Physical-Cyber-Social (PCS) systems. The health signals related to asthma spans Physical (environmental), Cyber (CDC reports), and Social (asthma/symptom reports on social media) modalities. Specifically, for asthma, we group health signals as personal (wheezing level, exhaled Nitric Oxide), population (asthma reports on social media), and public health signals (CDC asthma reports).

Asthma health signals spanning personal, public, and population level observations

kHealth for Dementia

We tackle this important problem by a combination of active and passive sensing. Active sensing involves the patient in the loop (obtrusive) while the passive sensing does not involve patient involvement (unobtrusive). Using a novel approach of utilizes low-cost sensors for continuous monitoring (active and passive sensing), we propose to develop algorithms that can take this multi-modal data and translate them to practical and actionable information for asthma patients and their healthcare provider. Specifically, provide information on asthma control level based on symptoms and their severity, asthma triggers and early alerts for increasing asthma symptoms.

kHealth kit for Asthma

kHealth Observations

Asthma is a multi-faceted problem and we propose a holistic solution for
Physiological: Wheezometer [6], Nitric Oxide [7], Accelerometer, Microphone, Contextual Questions
Environmental: Sensordrone [8], Dust Sensor [9], Location
Public Health
CDC [10], DCHC’s EMR Records (periodic manual review)
Population Level
Everyaware [11], AirQuality Egg [12], Allergy Alerts [13,14], Social Observations (e.g., tweets), Air Quality Index[15]

Preliminary Data Analysis

kHealth kit could be used to collect observations (both sensor and patients questionnaire response) in the patient home environment (which was never accessible in a quantitative form to doctors). These observations when collected based on expert guidance, prove valuable for clinical decision support. These observations when interpreted by a doctor, lead to some interesting insights:

  • Medication (Albuterol) use possibly leading to decreasing exhaled Nitric Oxide

kHealth kit for Asthma

  • Activity limitation is likely related to high exhaled Nitric Oxide

kHealth kit for Asthma

  • Low exhaled Nitric Oxide observed with absence of coughing

kHealth kit for Asthma

  • Activity limitation observed with high pollen activity

kHealth kit for Asthma


Dataset Size

We collect observations from three sensors (temperature, humidity, Carbon monoxide) on Sensordrone at the rate of 1 Hz (1 observation / second). Nitric Oxide observations from the NODE sensor are collected at the rate of 2 observations / day. Patients answer a questionnaire which has 5 questions resulting in 5 observations / day. For a single patient, we collect over 250,000 observations / day. In our study of three patients, we have collected over 9 million data points.


Dayton Children's Hospital Institutional Review Board (IRB) approved the pilot study in October 2013 which began enrolling pediatric patients and their parents to use the kHealth kit for Asthma. IRB continuation was approved in October 2014. Please contact Prof. Amit Sheth [amit at] or Dr. Shalini Forbis [ForbisS at] to obtain the exact copy of IRB.

kHealth User Manual

kHealth Asthma user guide

kHealth Video Introduction

kHealth Asthma Application Overview

kHealth Vision

Turning information into meaning: Dr. Amit Sheth

Digital health and mobile health applications are benefitting from semantic web research from Wright State's Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis). Director of Kno.e.sis and Professor of Computer Science and Engineering Dr. Amit Sheth describes development of mobile health applications with sensor technology to monitor patient health, mobile computational support, and clear feedback to the patient and physician.

Related Talks and Presentations

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[1] Alzheimer’s Association description of Alzheimer’s statistics, Available online at:

  1. quickFacts

[2] Dementia related facts, Available online at: illness/dementia.htm

[3] D. A. Squires, “The U.S. Health System in Perspective: A Comparison of Twelve Industrialized Nations,” June 2011, Available online at:

[4] Health Costs: How the U.S. Compares With Other Countries, Available online at:

[5] Quantified Self [6] G. K. Vincent, V. A. Velkof, “The next four decades: The older population in the United States: 2010 to 2050.” Washington, D.C.: U.S. Census Bureau; 2010.

[7] Hexoskin.

[8] A. Pasolini, "Sensor-packed Hexoskin shirt measures performance in real time". Available at: Gizmag. September 19, 2013.

[9] kHealth: A knowledge-enabled semantic platform to enhance decision making and improve health, fitness, and well-being, Available online at: (Accessed May 27, 2013).

[10] V. Santhisagar, T. Ioannis, B. Diane, J. C. Faquir, P. Fotios, "Emerging synergy between nanotechnology and implantable biosensors: A review." Biosensors and Bioelectronics 25.7: 1553-1565, 2010.

[11] J. S. Karlsson, U. Wiklund, L. Berglin, N. Östlund, M. Karlsson, T. Bäcklund, & L. Sandsjö, “Wireless monitoring of heart rate and electromyographic signals using a smart T-shirt.” In Proceedings of International Workshop on Wearable Micro and Nanosystems for Personalised Health, 2008.

[12] M. Chan, E. Campo, D. Estève, & J. Y. Fourniols, "Smart homes—current features and future perspectives. " Maturitas, 64(2), 90-97, 2009.

[13] E. Topol, “The creative destruction of medicine: How the digital revolution will create better health care.” Basic Books (AZ), 2012.

[14] T. Banerjee, M. Skubic, J. M. Keller & C. C. Abbott, "Sit-To-Stand Measurement For In Home Monitoring Using Voxel Analysis," IEEE Journal of Biomedical and Health Informatics, 18(4):1502-1509, 2014.

[15] T. Banerjee, M. Rantz, M. Li, M. Popescu, E. Stone & M. Skubic, "Monitoring Hospital Rooms for Safety Using Depth Images," Proceedings, AAAI Fall Symposium Series - AI for Gerontechnology, Washington DC, November 2-4, 2012.

[16] M. Gietzelt, K. H. Wolf, M. Kohlmann, M. Marschollek, and R. Haux. "Measurement of Accelerometry-based Gait Parameters in People with and without Dementia in the Field." Methods Inf. Med 52, no. 4: 319-325, 2013.

[17] E. Stone & M. Skubic, "Evaluation of an Inexpensive Depth Camera for In-Home Gait Assessment," Journal of Ambient Intelligence and Smart Environments, 3(4):349-361, 2011.

[18] A. L. Bleda, R. Maestre, A. J. Jara, & A. G. Skarmeta, “Ambient Assisted Living Tools for a Sustanaible Aging Society.” In Resource Management in Mobile Computing Environments pp. 193-220. Springer International Publishing, 2014.

[19] J. L. Cummings, “The Neuropsychiatric Inventory: Assessing psychopathology in dementia patients.” Neurology 48 (Supple 6): S10-S16,1997.

[20] W. E. Haley, E. G. Levine, S. L. Brown, and A. A. Bartolucci. "Stress, appraisal, coping, and social support as predictors of adaptational outcome among dementia caregivers." Psychology and aging 2, no. 4: 323, 1987.

[21] Fitbit sensor.

[22] S. H. Zarit, K. E Reever, J. Bach-Peterson, “Relatives of the impaired elderly: correlates of feelings of burden.” Gerontologist. 20:649–55, 1980.

[23] D. Tobon Vallejo, T. Falk, M. Maier, "MS-QI: A Modulation Spectrum-Based ECG Quality Index for Telehealth Applications," Biomedical Engineering, IEEE Transactions on, 2015.

[24] R. T. Warne, "A primer on multivariate analysis of variance (MANOVA) for behavioral scientists". Practical Assessment, Research & Evaluation 19 (17): 1–10, 2014.

[25] K. V. Mardia, J. T. Kent, J. M. Bibby, “Multivariate Analysis.” Academic Press, 1979.

[26] J. Schmid Jr., "The Relationship between the Coefficient of Correlation and the Angle Included between Regression Lines". The Journal of Educational Research 41 (4), 1947.

[27] Hexoskin data validation. Available at: skin_Poster_-_University_of_Waterloo.pdf?11148

[28] A. Sheth, P. Anantharam, K. Thirunarayan, “kHealth: Proactive Personalized Actionable Information for Better Healthcare,” Workshop on Personal Data Analytics in the Internet of Things (PDA@IOT 2014), collocated at VLDB 2014, Hangzhou, China, September 5th 2014.

[29] C.R. Rao. “Estimation of variance and covariance components in linear models.” Journal of the American Statistical Association, 67(337), 112-115, 1972.

[30] P. Anantharam, T. Banerjee, A. Sheth, K. Thirunarayan, S. Marupudi, V. Sridharan, S. G. Forbis, "Knowledge-driven