This page provides material from the initial meeting of the MIDAS Mobile Sensor Analytics Workgroup, April 13, 2017. Please email midas-contact@umich.edu for more information.

Attendees

Name Department and job title Email address
Allison, Mark Computer Science, Engineering and Physics, Flint, Assistant Professor markalli@umflint.edu 
Antonakos, Cathy School of Kinesiology, Research Area Specialist Senior cathya@umich.edu
Babu, Garnesh Founder & CEO pvgbabu@datasiri.com
Berona, Johnny Psychology, Doctoral Candidate jberona@umich.edu
Bodary, Peter Kinesiology, Clinical Assistant Professor pfbodary@umich.edu
Burmeister, Margit Professor, Psychiatry margit@med.umich.edu
Buu, Anne Associate Professor, Health Behavior & Biological Sciences buu@umich.edu
Byon, Eunshin IOE Assistant professor ebyon@umich.edu
Ceglarek, Peter Health Management and Policy/Intitute for Social Research, Healthy Minds Network, Administrative & Project Coordinator peterceg@umich.edu
Chen, Weiyun School of Kinesiology, Associate Professor chenwy@umich.edu
Colabianchi, Natalie Survey Research Center, ISR, Research Assistant Professor colabian@umich.edu
Dehzangi, Omid Computer and Information Science, Dearborn, Assistant Professor dehzangi@umich.edu
Denton, Brian IOE and Urology, Professor btdenton@med.umich.edu
Eldos, JP JCT j.p.eldous@daum.mnet
Elkateeb, Ali Electrical and Computer Eng., Associate Professor elkateeb@umd.umich.edu
Esquivel, Amanda UM Dearborn Mechanical Engineering aoe@umich.edu
Feng, Yiheng UMTRI (Research Fellow) yhfeng@umich.edu
Flannagan, Carol UMTRI Research Associate Professor cacf@umich.edu
Forger, Danny Professor, DCMB, Math forger@umich.edu
Gilbert, Anna Mathematics, Professor annacg@umich.edu
Goutman, Stephen Neurology, Assistant Professor sgoutman@med.umich.edu
Hallum, Jeremy ARC-TS Research Computing Manager jhallum@umich.edu
Harirchi, Farshad EECS, Post-Doctoral Fellow Harirchi@umich.edu
Harris, Marcelline Nursing, Associate Professor mrhrrs@umich.edu
Jackson, Jeannette Biosocial Methods Collaborative, ISR, Managing Director jackjean@umich.edu
Jagadish, H. CSE, Professor jag@umich.edu
Jin, Judy IOE, Professor jhjin@umich.edu
Kratz, Anna PM&R, Assistant Professor alkratz@med.umich.edu
Liu, Henry Civil and Environmental Engineering, Professor henryliu@umich.edu
Luo, Lan PhD. in Biostatistics luolsph@umich.edu
Ma, Di UM-Dearborn, CECS, Associate Professor dmadma@umich.edu
McInnis, Melvin Professor, Psychiatry mmcinnis@med.umich.edu
Meyer, Seth ARC-TS, Research Computing Lead smeyer@umich.edu
Moldwin, Mark mmoldwin@umich.edu
Mroueh, Jawad student, and industry employee awad.Mroueh@zf.com
Nahum-Shani, Billie (Inbal) ISR inbal@umich.edu
Nazir, Salman EECS, Graduate Research Assistant mdsnazir@umich.edu
Niss, Laura Statistics PhD student lniss@umich.edu
Orosz, Gabor Mechanical Engineering, Assistant Professor orosz@umich.edu
Ozay, Necmiye EECS, Assistant Professor necmiye@umich.edu
Plumlee, Matthew Assistant Professor, Industrial and Operations Engineering mplumlee@umich.edu
Prakash, Atul EECS, Professor aprakash@umich.edu
Provost, Emily Mower EECS, Assistant Professor emilykmp@umich.edu
Rabaut, Lisa Exercise & Sport Science Institute, Managing Director lisaraba@umich.edu
Scott, Clayton Assoc. Prof, EECS clayscot@umich.edu
Sen, Srijan Professor, Psychiatry srijan@umich.edu
She, Xichen Biostatistics, Postdoc Research Fellow xichens@umich.edu
Sica, Jeffrey Research Database Administrator jsica@umich.edu
Tewari, Ambuj Assistant Professor, Statistics and EECS tewaria@umich.edu
Verma, Manish CSCAR, Geospatial Lead manishve@umich.edu
Walch, Olivia Neurology, Research Fellow ojwalch@umich.edu
Ward, Kevin Professor, Emergency Medicine keward@med.umich.edu
Wu, Zhenke Biostatistics, Assistant Professor zhenkewu@umich.edu
Xu, Hongwei Research Assistant Professor at the Institute for Social Research xuhongw@umich.edu
Zernicke, Ron Kenisiology, Orthopaedic Surgery, Biomedical Engineering, Professor zernicke@umich.edu

Selected Funding Opportunities

NSF (anticipated in Dec. 2017): Smart and Connected Health

The goal of the Smart and Connected Health (SCH) Program is to accelerate the development and use of innovative approaches that would support the much needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, person-centered and focused on well-being rather than disease. Approaches that partner technology-based solutions with biobehavioral health research are supported by multiple agencies of the federal government including the National Science Foundation (NSF) and the National Institutes of Health (NIH). The purpose of this program is to develop next generation health care solutions and encourage existing and new research communities to focus on breakthrough ideas in a variety of areas of value to health, such as sensor technology, networking, information and machine learning technology, decision support systems, modeling of behavioral and cognitive processes, as well as system and process modeling. Effective solutions must satisfy a multitude of constraints arising from clinical/medical needs, social interactions, cognitive limitations, barriers to behavioral change, heterogeneity of data, semantic mismatch and limitations of current cyberphysical systems. Such solutions demand multidisciplinary teams ready to address technical, behavioral and clinical issues ranging from fundamental science to clinical practice.

NSF (11/1/2017): Communications, Circuits, and Sensing-Systems 

CCSS supports systems research in hardware, signal processing techniques, and architectures to enable the next generation of cyber-physical systems (CPS) that leverage computation, communication, and algorithms integrated with physical domains. CCSS supports innovative research and integrated educational activities in micro- and nano- electromechanical systems (MEMS/NEMS), communications and sensing systems, and cyber-physical systems. The goal is to design, develop, and implement new complex and hybrid systems at all scales, including nano and macro, that lead to innovative engineering principles and solutions for a variety of application domains including, but not limited to, healthcare, medicine, environmental and biological monitoring, communications, disaster mitigation, homeland security, intelligent transportation, manufacturing, energy, and smart buildings. CCSS also supports integration technologies at both intra- and inter- chip levels, new and advanced radio frequency (RF), millimeter wave and optical wireless and hybrid communications systems architectures, and sensing and imaging at terahertz (THz) frequencies.

 NIH (April and December each year): Wearable Alcohol Biosensors (R43/R44) 

Rapid advances are being made in wearable technology, including clothing, jewelry and other devices with broadly diverse functions that meet medical or consumer needs.  This FOA seeks applications from small businesses that propose to design and produce a non-invasive wearable device to monitor blood alcohol levels in real time.

The alcohol biosensor device should be unobtrusive, appealing to the wearer, and can take the form of jewelry, clothing, or any other format located in contact with the human body. Advances in alcohol detection that depart from measuring alcohol in sweat or sweat vapor are sought. Techniques to quantitate alcohol in blood or interstitial fluid are highly encouraged.  Applicants are encouraged to pursue any technology – including but not limited to biophysical, optical, wave, or other novel approaches- that works in a non-invasive way and can be incorporated into a wearable.

The device should be able to quantitate blood alcohol level, interpret, and store the data or transmit it to a smartphone or other device by wireless transmission.  The device should have the ability to verify standardization at regular intervals and to indicate loss of functionality.  The power source should be dependable and rechargeable.  Data storage and transmission must be completely secure in order to protect the privacy of the individual.  A form of subject identification would be an added benefit.  The device can be removable.

NIH (standard dates): Development and/or Validation of Devices or Electronic Systems to Monitor or Enhance Mind and Body Interventions (R43/R44) 

This Funding Opportunity Announcement (FOA) supports Small Business Innovation Research (SBIR) grant applications from small business concerns (SBCs) that will develop and/or validate devices or electronic systems that can: 1) monitor biologically- or behaviorally-based processes applicable to mind and body interventions or 2) be used to assist in optimizing the practice or increasing the efficacy of mind and body interventions.  The applications should: 1) lead to the development of new technologies, 2) adapt existing innovative technologies, devices and/or electronic systems, 3) repurpose existing devices and electronic systems, or 4) conduct testing of single or combined components of an integrated, long term, automated, wearable monitoring, stimulation device or electronic system in order to monitor or enhance the mechanistic processes or functional outcomes of mind and body interventions. For the purposes of this FOA, mind and body interventions are defined as non-pharmacological approaches that include mind/brain focused interventions (e.g., meditation, hypnosis), body-based approaches (e.g., acupuncture, massage, spinal manipulation/mobilization), or combined mind and body meditative movement approaches (e.g., yoga, tai-chi, qigong).

NIH (standard dates): Revision Applications for Validation of Mobile/Wireless Health Tools for Measurement and Intervention (R01) 

This Funding Opportunity Announcement (FOA) invites revision applications from investigators and institutions/organizations with active NIH-supported research project awards to support an expansion of the scope of approved and funded projects to incorporate recent advances in mobile/wireless tools to validate these tools for measurement and intervention delivery. Revision applications for projects that do not currently employ mobile/wireless tools are welcome provided that the applicant team has the requisite scientific and technical expertise to employ and validate these tools.

Research efforts have failed to keep pace with the rapid and exponential advancements in mobile/wireless health technologies in recent years.  By supporting revisions to existing R01 projects, this FOA encourages the rapid validation of mobile/wireless tools for health measurement and intervention delivery.  The focus of this FOA is on recently developed mobile/wireless health tools including sensor technologies and smartphone applications.  While some additional programming may be required to customize or integrate the technology into the existing project, this FOA is not intended to support new technology development, but instead to clinically validate recently developed but not yet validated tools.

NIH (standard dates): Intensive Longitudinal Analysis of Health Behaviors: Leveraging New Technologies to Understand Health Behaviors (U01)

NCI is interested in supporting research in the following specific content areas:

  • Projects that incorporate geospatial factors into data collection and analysis plans to account for data collection across diverse places
  • Incorporate data from multiple levels (e.g., biological, intrapersonal, interpersonal, community, policy) and types (e.g., self-report; sensor data) to explain and understand risky behaviors such as alcohol use, sedentary behavior, smoking, and poor diet.

NIAAA is interested in supporting research in the following specific content areas:

  • Use of wearable sensors to measure abstinence and drinking behavior as outcomes in clinical trials of either behavioral or pharmacological therapies.
  • Use of EMA designs to identify emotional, attitudinal, contextual, and social triggers of relapse following alcohol treatment.
  • Epidemiological studies of the influence of mood states, environmental cues, and social contexts in the occurrence of heavy drinking occasions.

NIDA is interested in supporting research in the following specific content areas:

  • Applications that use longitudinal data collection methods in the context of NIH’s HIV/AIDS high research priority areashttps://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-137.html among drug using populations.
  • Applications that conduct pharmacological and non-pharmacological interventions for individuals with co-morbid substance use disorders and HIV/AIDS that make use of longitudinal monitoring of treatment adherence, including potential for drug-drug interactions in the case of pharmacologic interventions.
  • Studies in the context of addressing NIH high priority HIV/AIDS research that address new technologies and their applications to social phenomena such as natural language processing of structure and content of naturally occurring material on social media networks, and analysis of the functioning and impact of social networks.
  • Studies in the context of addressing NIH high priority HIV/AIDS research that address new technologies and their applications to make more reliable and valid predictions of patient-level risk or treatment adherence.

NIMH will prioritize research in the following specific content areas:

  • Studies utilizing sensor technology in real world settings to identify imminent risk for suicidal (ideation or attempt) or self-injurious behavior. Applicants are encouraged to refer to “A Prioritized Research Agenda for Suicide Prevention” and Short-term Research Objective 2C (http://actionallianceforsuicideprevention.org/sites/actionallianceforsuicideprevention.org/files/Agenda.pdf)
  • Incorporation of wearable sensors into studies of eating disorders to identify factors that predict variation in clinical symptoms and/or relapse following treatment (e.g., binge eating, purging, and social withdrawal).
  • Technology that can identify, with a high degree of probability, environmental, behavioral, and biological triggers of psychotic or manic episodes.
  • Use of sensor technology to measure trajectories of irritability and emotional dysregulation in youth and that can be used for early prediction of psychopathology.
  • EMA assessments that measure real-time fluctuation (episodic) and intensity of emotional states in children.

DoD: Human Performance Sensing 

This BAA employs the Sense-Assess-Augment paradigm to accelerate research and development of technologies capable of detecting/assessing human performance. This BAA focuses on identifying, developing, characterizing, and accelerating sensing technologies that can be utilized to assess the physiological, cognitive, and psychological states of human operators. It is also anticipated that these technologies will be implemented into fieldable systems. Research will have an emphasis on developing technologies capable of detecting & sensing physiological, biomarker, and behavioral metrics which are or can be correlated with human state/performance. An emphasis will also be placed upon the development, integration, miniaturization, initial operational test and evaluation, and verification and validation of human-centric multi-sensor suite designs. Research focusing on the manufacturing of nano-biomaterial sensors are of particular interest. Research may also focus on developing and implementing empirically-based models, frameworks, and novel evaluation capabilities, to identify assessment linkages to performance. Initial testing & evaluation and verification and validation of the developed technologies is vital to ensure appropriate and proper performance in laboratory and operational-type settings. Relevant USAF application domains include Air, Special Operations, Intelligence, Surveillance, and Reconnaissance, Remotely Piloted Aircraft, and Cyber Operations, as well as training applications as afforded by Live, Virtual, and Constructive (LVC) environments.

 USDA (6/5/2017): Mobile Technology for Child Nutrition Innovation Laboratory

This is an announcement is subject to the availability of funds for one new cooperative agreement for FY 2017-2020 with a public or private Academic or Research Institution. The USDA anticipates awarding up to $1.5 million in grant funding to support the creation of a Mobile Technology for Child Nutrition Research Innovation Laboratory. This “lab” will support the development, testing and implementation of innovative mobile technology-based solutions to improve services, effectiveness, participation, and customer satisfaction in the Child Nutrition (CN) programs through subgrants. Subgrantees may include partnerships between State and local programs, researchers, and small businesses that are well-positioned to develop and test the effectiveness of novel mobile applications for consideration and eventual use by FNS and State agencies. FNS has a particular interest in mobile technology solutions that integrate behavioral economic approaches in their design. Mobile applications developed under this grant should support the activities of those who administer Child Nutrition programs and/or improve service delivery for program participants.

Gates Foundation (5/3/2017): Grand Challenges Exploration: Wearables and Technology for Maternal, Neonatal and Child Health Behavior Change

We seek wearable and/or sensor technologies that will improve the health of mothers and newborns by 1) increasing uptake of healthy behaviors and/or 2) facilitating research on maternal and neonatal interventions in low-resource settings.  What we will consider for funding:

  • Tracker of temperature and position that can stimulate KMC practice
  • Sensor for newborn that measures heart rate, respiratory rate, temperature, apnea or more
  • Sensor on the infant that can alert the mother to infant sleep/wake state, hunger and activity
  • Wearable on an expectant mother that could measure and transmit data on blood pressure, heart rate, temperature, activity, sleep state and fetal heart rate
  • Patch that can measure metabolites in an infant transcutaneously including glucose, bilirubin, Na and Hb.
  • Wearable that will reinforce positive behavior in the mother in breastfeeding, language interaction, soothing behaviors
  • Sensor to facilitate handwashing or other infection prevention measures