{"id":5604,"date":"2023-12-14T05:07:14","date_gmt":"2023-12-14T05:07:14","guid":{"rendered":"https:\/\/mdotcenter.org\/santosh\/?page_id=5604"},"modified":"2024-06-10T18:26:48","modified_gmt":"2024-06-10T18:26:48","slug":"research-at-mhealth-lab","status":"publish","type":"page","link":"https:\/\/mdotcenter.org\/santosh\/research-at-mhealth-lab\/","title":{"rendered":"Research at mHealth Lab"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"5604\" class=\"elementor elementor-5604\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9775499 elementor-section-content-middle ot-traditional elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9775499\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-extended\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-58ecbff ot-flex-column-vertical\" data-id=\"58ecbff\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-63d1f95 elementor-widget elementor-widget-iheading\" data-id=\"63d1f95\" data-element_type=\"widget\" data-widget_type=\"iheading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"ot-heading\">\r\n\t        \t            <span>\/\/ Transforming Health & Wellness<\/span>\r\n\t        <h2 class=\"main-heading\">Research at mHealth Systems Lab<\/h2>\t    <\/div>\r\n\t    \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f512699 elementor-widget elementor-widget-text-editor\" data-id=\"f512699\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.21.0 - 26-05-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>At mHealth Systems Lab, we strive to make a difference in people\u2019s lives via rigorous interdisciplinary research.\u00a0Our research involves more than twenty faculty members\u00a0from over ten universities. Our collaborators span a variety of disciplines (e.g., Computer Science, Electrical Engineering, Mathematics, Statistics, Psychology, Behavioral Science, Cardiology, Physiology, Public Health, etc.), making our projects highly transdisciplinary.<\/p><p>mHealth Systems Lab\u00a0students have access to many large-scale mobile sensor datasets. There have been a wide range of deploymentsusing data generated by MD2K&#8217;s software platforms over the last 10+ years. With over 2,200 users, these platforms have generated over 4.7 trillion data points through over 106,000 person-days, and these numbers are growing every day. These datasets provide invaluable aid to new, current, and prospective mHealth researchers.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-e73d2ae elementor-section-full_width elementor-section-content-middle ot-traditional elementor-section-height-default elementor-section-height-default\" data-id=\"e73d2ae\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-extended\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-4de478b ot-flex-column-vertical\" data-id=\"4de478b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-082b034 elementor-widget elementor-widget-icounter\" data-id=\"082b034\" data-element_type=\"widget\" data-widget_type=\"icounter.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t    \t<div class=\"icounter ot-counter\">\r\n\t\t        \t<div>\r\n\t\t        \t\t<span class=\"num\" data-to=\"2.2\" data-time= \"2000\"><\/span>\r\n\t\t        \t\t<span>K<\/span>\r\n\t\t        \t<\/div>\r\n\t\t        \t<h6>Users<\/h6>      \t\t\t\t        \r\n\t\t\t    <\/div>\r\n\t\t    \t    \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-40ea080 ot-flex-column-vertical\" data-id=\"40ea080\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0e67748 elementor-widget elementor-widget-icounter\" data-id=\"0e67748\" data-element_type=\"widget\" data-widget_type=\"icounter.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t    \t<div class=\"icounter ot-counter\">\r\n\t\t        \t<div>\r\n\t\t        \t\t<span class=\"num\" data-to=\"4.7\" data-time= \"2000\"><\/span>\r\n\t\t        \t\t<span>T<\/span>\r\n\t\t        \t<\/div>\r\n\t\t        \t<h6>Data Points<\/h6>      \t\t\t\t        \r\n\t\t\t    <\/div>\r\n\t\t    \t    \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-f2ed4f7 ot-flex-column-vertical\" data-id=\"f2ed4f7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cbf3006 elementor-widget elementor-widget-icounter\" data-id=\"cbf3006\" data-element_type=\"widget\" data-widget_type=\"icounter.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t    \t<div class=\"icounter ot-counter\">\r\n\t\t        \t<div>\r\n\t\t        \t\t<span class=\"num\" data-to=\"106\" data-time= \"2000\"><\/span>\r\n\t\t        \t\t<span>K<\/span>\r\n\t\t        \t<\/div>\r\n\t\t        \t<h6>Person-Days<\/h6>      \t\t\t\t        \r\n\t\t\t    <\/div>\r\n\t\t    \t    \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-3afe880 ot-flex-column-vertical\" data-id=\"3afe880\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-083687f elementor-section-full_width ot-layout_block elementor-section-height-default elementor-section-height-default\" data-id=\"083687f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-extended\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-2d938e3 ot-flex-column-vertical\" data-id=\"2d938e3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-36ce802 elementor-widget elementor-widget-iimagebox1\" data-id=\"36ce802\" data-element_type=\"widget\" data-widget_type=\"iimagebox1.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<a class=\"ot-image-box\" href=\"https:\/\/mdotcenter.org\/santosh\/research-at-mhealth-lab\/research-projects\/\">\r\n\t\t\t<div class=\"overlay\">\r\n\t        \t<h4>Research <br> Projects<\/h4>\r\n\t\t\t<\/div>\r\n\t\t\t<img decoding=\"async\" src=\"https:\/\/engitech.s3.amazonaws.com\/images\/image-box1.jpg\" alt=\"Research &lt;br&gt; Projects\">\r\n\t\t<\/a>\r\n\t    \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-61aabef elementor-widget elementor-widget-iimagebox1\" data-id=\"61aabef\" data-element_type=\"widget\" data-widget_type=\"iimagebox1.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<a class=\"ot-image-box\" href=\"https:\/\/mdotcenter.org\/santosh\/research-at-mhealth-lab\/prospective-students\/\">\r\n\t\t\t<div class=\"overlay\">\r\n\t        \t<h4>Prospective<br> Students<\/h4>\r\n\t\t\t<\/div>\r\n\t\t\t<img decoding=\"async\" src=\"https:\/\/engitech.s3.amazonaws.com\/images\/image-box3.jpg\" alt=\"Prospective&lt;br&gt; Students\">\r\n\t\t<\/a>\r\n\t    \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-113c7a8 ot-flex-column-vertical\" data-id=\"113c7a8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-53146c4 elementor-widget elementor-widget-iimagebox1\" data-id=\"53146c4\" data-element_type=\"widget\" data-widget_type=\"iimagebox1.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<a class=\"ot-image-box\" href=\"https:\/\/mdotcenter.org\/santosh\/research-at-mhealth-lab\/collaborative-research\/\">\r\n\t\t\t<div class=\"overlay\">\r\n\t        \t<h4>Collaborative<br> Research<\/h4>\r\n\t\t\t<\/div>\r\n\t\t\t<img decoding=\"async\" src=\"https:\/\/engitech.s3.amazonaws.com\/images\/image-box2.jpg\" alt=\"Collaborative&lt;br&gt; Research\">\r\n\t\t<\/a>\r\n\t    \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cccc771 elementor-widget elementor-widget-iimagebox1\" data-id=\"cccc771\" data-element_type=\"widget\" data-widget_type=\"iimagebox1.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<a class=\"ot-image-box\" href=\"https:\/\/mdotcenter.org\/santosh\/research-at-mhealth-lab\/research-datasets\/\">\r\n\t\t\t<div class=\"overlay\">\r\n\t        \t<h4>Research<br> Datasets<\/h4>\r\n\t\t\t<\/div>\r\n\t\t\t<img decoding=\"async\" src=\"https:\/\/engitech.s3.amazonaws.com\/images\/image-box4.jpg\" alt=\"Research&lt;br&gt; Datasets\">\r\n\t\t<\/a>\r\n\t    \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<div class=\"elementor-element elementor-element-327c49e elementor-widget elementor-widget-text-editor\" data-id=\"327c49e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p style=\"text-align: right;\">Last updated: 6\/10\/24<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t<div class=\"elementor-element elementor-element-0c84bc0 e-flex e-con-boxed e-con e-parent\" data-id=\"0c84bc0\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-f8547a6 e-con-full e-flex e-con e-child\" data-id=\"f8547a6\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7adf9e9 elementor-widget elementor-widget-text-editor\" data-id=\"7adf9e9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h3 style=\"text-align: center;\">Research Areas<\/h3><p style=\"text-align: center;\"><strong><a href=\"https:\/\/mdotcenter.org\/santosh\/research-at-mhealth-lab\/?preview_id=5604&amp;preview_nonce=e5e7d9bcb0&amp;preview=true#:~:text=mHealth%20Sensor%20Platforms-,Sensor%2DBased%20mHealth%20Markers,-%3A%C2%A0The\">Sensor-Based mHealth Markers<\/a>\u00a0|\u00a0<\/strong><b><a href=\"https:\/\/mdotcenter.org\/santosh\/research-at-mhealth-lab\/?preview_id=5604&amp;preview_nonce=e5e7d9bcb0&amp;preview=true#:~:text=2016.%20(12%20pages)-,Sensor%2DTriggered%20Just%2Din%2DTime%20mHealth%20Interventions,-%3A%C2%A0The%20sensing\">Sensor-Triggered Temporally-Precise mHealth Interventions<\/a><br \/><a href=\"https:\/\/mdotcenter.org\/santosh\/research-at-mhealth-lab\/?preview_id=5604&amp;preview_nonce=e5e7d9bcb0&amp;preview=true#:~:text=Ubiquitous%20Technologies%20(IMWUT).-,mHealth%20Privacy,-%3A%C2%A0mHealth\">mHealth Privacy<\/a>\u00a0|\u00a0<strong><a href=\"https:\/\/mdotcenter.org\/santosh\/research-at-mhealth-lab\/?preview_id=5604&amp;preview_nonce=e5e7d9bcb0&amp;preview=true#:~:text=pp.%20706%2D717.-,mHealth%20Sensor%20Platforms,-%3A%C2%A0With%20support\">mHealth Sensor Platforms<\/a><\/strong><\/b><\/p><p><b><a id=\"b\"><\/a><span style=\"color: #2ba4b8;\">Sensor-Based mHealth Markers: <\/span><\/b>The promise of mHealth is to provide unprecedented visibility into the physical, physiological, psychological, social, and environment state of an individual so as to discover the causes of various diseases which can be used in the development of treatments, interventions, and prevention programs. With support from NSF, we have used the real-life sensor data collected from AutoSense to develop computationally models for sensor-based continuous detection of risk for smoking lapse [4], speaking [3], stress [2], and smoking [1] from wearables. These works have established the wide utility of physiological monitoring in the field for automated detection of human behaviors and enabled sensor-triggered temporally-precise interventions.<\/p><ol><li style=\"list-style-type: none;\"><ol><li>Ullah MA, Chatterjee S, Fagundes CP, Lam C, Nahum-Shani I, Rehg JM, Wetter DW, Kumar S. mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2022 Sep;6(3) PubMed Central PMCID: PMC9979627.<\/li><li>Bari R, Adams RJ, Rahman M, Parsons MB, Buder EH, Kumar S. rConverse: Moment by Moment Conversation Detection Using a Mobile Respiration Sensor. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2018 Mar;2(1) PubMed Central PMCID: PMC6223316.<\/li><li>Saleheen N, Ali AA, Hossain SM, Sarker H, Chatterjee S, Marlin B, Ertin E, al&#8217;Absi M, Kumar S. puffMarker: A Multi-Sensor Approach for Pinpointing the Timing of First Lapse in Smoking Cessation. Proc ACM Int Conf Ubiquitous Comput. 2015 Sep;2015:999-1010. PubMed Central PMCID: PMC4631252.<\/li><li>Hovsepian K, al&#8217;Absi M, Ertin E, Kamarck T, Nakajima M, Kumar S. cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment. Proc ACM Int Conf Ubiquitous Comput. 2015 Sep;2015:493-504. PubMed Central PMCID: PMC4631393.<\/li><\/ol><\/li><\/ol><p><b><a id=\"c\"><\/a><span style=\"color: #2ba4b8;\">Sensor-Triggered Temporally-Precise mHealth Interventions: <\/span><\/b>The sensing and inferencing of human states becomes practically useful when used in the design, development, and delivery of an intervention to improve health. As a first step towards development of sensor-triggered just-in-time interventions, we have developed methods to determine (from sensor data) when a user may be physically, cognitively, and socially available to be engaged in an intervention [2]. We find that users are least available at work and during driving, and most available when walking outside. Subsequently, we have identified from continuous measurement of stress in the natural environment that users are most stressed during driving. We have also developed visualizations of continuous stress data [1]. Our spatio-temporal visualizations help identify and explain between- and withinperson variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. We developed a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data for delivering post-stress intervention [3]. Using 4 weeks of physiological, GPS, and activity data collected from 38 users in their natural environment, we developed a model to predict stressful episodes in advance for proactive stress intervention. We also developed a model to assess from GPS traces whether the current surrounding represents a vulnerable smoking situation, including the detection of high-risk microlocations, e.g., personal smoking spots [4].<\/p><ol><li style=\"list-style-type: none;\"><ol><li>Chatterjee S, Moreno A, Lizotte SL, Akther S, Ertin E, Fagundes CP, Lam C, Rehg JM, Wan N, Wetter DW, Kumar S. SmokingOpp: Detecting the Smoking &#8216;Opportunity&#8217; Context Using Mobile Sensors. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2020 Mar;4(1) PubMed Central PMCID: PMC8513752.<\/li><li>Sarker H, Tyburski M, Rahman MM, Hovsepian K, Sharmin M, Epstein DH, Preston KL, Furr-Holden CD, Milam A, Nahum-Shani I, al&#8217;Absi M, Kumar S. Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data. Proc SIGCHI Conf Hum Factor Comput Syst. 2016 May;2016:4489-4501. PubMed Central PMCID: PMC5207658.<\/li><li>Sharmin M, Raij A, Epstien D, Nahum-Shani I, Beck JG, Vhaduri S, Preston K, Kumar S. Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions. Proc ACM Int Conf Ubiquitous Comput. 2015 Sep;2015:505-516. PubMed Central PMCID: PMC4629803.<\/li><li>Sarker H, Sharmin M, Ali AA, Rahman MM, Bari R, Hossain SM, Kumar S. Assessing the\u00a0Availability of Users to Engage in Just-in-Time Intervention in the Natural Environment. Proc ACM Int Conf Ubiquitous Comput. 2014;2014:909-920. PubMed Central PMCID: PMC4365928.<\/li><\/ol><\/li><\/ol><p><b><a id=\"d\"><\/a><span style=\"color: #2ba4b8;\">mHealth Privacy:\u00a0<\/span><\/b><span style=\"color: var( --e-global-color-text ); text-align: var(--text-align);\">mHealth systems have deep privacy implications. Our work on mHealth inferencing\u00a0<\/span><span style=\"color: var( --e-global-color-text ); text-align: var(--text-align);\">in the FieldStream project revealed that sensors once considered innocuous, such as respiration, can reveal potentially private behaviors and psychological states such as stress, conversation, smoking, or drug use events. This raises new privacy issues for sharing of mHealth data [4], since the focus of privacy research has traditionally been on protecting the identity of individuals in a group, and not on protecting the revelation of private behaviors. We highlighted several new research challenges in mHealth privacy [1]. To address behavioral privacy, we proposed a selective replacements corresponding to any behaviorally-private event with plausible sensor trace from the same user [2]. We recently showed that wrist-worn accelerometry has high potential to re-identify the wearer, especially when performing daily physical activities (e.g., walk) [3].<\/span><\/p><ol><li style=\"list-style-type: none;\"><ol><li>Saleheen N, Ullah MA, Chakraborty S, Ones DS, Srivastava M, Kumar S. WristPrint:\u00a0Characterizing User Re-identification Risks from Wrist-worn Accelerometry Data. Conf Comput Commun Secur. 2021 Nov;2021:2807-2823. PubMed Central PMCID: PMC9988376.<\/li><li>Saleheen N, Chakraborty S, Ali N, Mahbubur Rahman M, Hossain SM, Bari R, Buder E, Srivastava M, Kumar S. mSieve: Differential Behavioral Privacy in Time Series of Mobile Sensor Data. Proc ACM Int Conf Ubiquitous Comput. 2016 Sep;2016:706-717. PubMed Central PMCID: PMC5207660.<\/li><li>Kotz D, Gunter CA, Kumar S, Weiner JP. Privacy and Security in Mobile Health: A Research Agenda. Computer (Long Beach Calif). 2016 Jun;49(6):22-30. PubMed Central PMCID: PMC5362254.<\/li><li>Raij A, Ghosh A, Kumar S, Srivastava M. Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2011 May 07; :11. Available from: https:\/\/dl.acm.org\/doi\/10.1145\/1978942.1978945 DOI: 10.1145\/1978942.1978945; NIHMSID: NIHMS1984197<\/li><\/ol><\/li><\/ol><p><b><a id=\"a\"><\/a><span style=\"color: #2ba4b8;\">mHealth Sensor Platforms: <\/span><\/b>With support from NIH\u2019s Genes Environment &amp; Health Initiative (GEI), my team developed the AutoSense sensor suite [4] that hosts ten sensors (ECG, respiration, skin conductance, accelerometry, temperature, alcohol, etc.), is ultra-low-power, is optimized for on-body sensing in the mobile environment, and convenient for long-term wearing. AutoSense is complemented by a robust software framework on the mobile phone that collects continuous measurements from wearable wireless sensors, processes them to make health inferences, and solicits self-reports on the phone, all in real-time. This system has been worn by 2,500+ human volunteers (including daily smokers, drinkers, and drug users) for 200,000+ hours in their natural environments as part of various field studies, producing 300 terabytes of sensor data for research [2,3]. These sensor devices are complemented by a novel app ecosystem consisting of tens of interconnected apps for smartphones that are used to collect sensor data from wearable devices and time-synchronized self-reports from participants [1].<\/p><ol><li style=\"list-style-type: none;\"><ol><li>Hossain SM, Hnat T, Saleheen N, Nasrin NJ, Noor J, Ho BJ, Condie T, Srivastava M, Kumar S. mCerebrum: A Mobile Sensing Software Platform for Development and Validation of Digital Biomarkers and Interventions. Proc Int Conf Embed Netw Sens Syst. 2017 Nov;2017 PubMed Central PMCID: PMC6168216.<\/li><li>Kennedy AP, Epstein DH, Jobes ML, Agage D, Tyburski M, Phillips KA, Ali AA, Bari R, Hossain SM, Hovsepian K, Rahman MM, Ertin E, Kumar S, Preston KL. Continuous in-the-field measurement of heart rate: Correlates of drug use, craving, stress, and mood in polydrug users. Drug Alcohol Depend. 2015 Jun 1;151:159-66. PubMed Central PMCID: PMC4447529.<\/li><li>Rahman M, Bari R, Ali AA, Sharmin M, Raij A, Hovsepian K, Hossain SM, Ertin E, Kennedy A, Epstein DH, Preston KL, Jobes M, Beck JG, Kedia S, Ward KD, al&#8217;Absi M, Kumar S. Are We There Yet? Feasibility of Continuous Stress Assessment via Wireless Physiological Sensors. ACM BCB. 2014;2014:479-488. PubMed Central PMCID: PMC4374173.<\/li><li>Ertin E, Stohs N, Kumar S, Raij A, al&#8221;Absi M, Shah S. AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. SenSys &#8217;11: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems. 2011 November; :274. 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