This page provides material from the initial meeting of the MIDAS Mobile Sensor Analytics Workgroup, April 13, 2017. Please email email@example.com for more information.
Mobile Sensor Analytics research mailing list
|Name||Department and job title||Email address|
|Allison, Mark||Computer Science, Engineering and Physics, Flint, Assistant Professorfirstname.lastname@example.org|
|Antonakos, Cathy||School of Kinesiology, Research Area Specialist Senioremail@example.com|
|Babu, Garnesh||Founder & CEOfirstname.lastname@example.org|
|Berona, Johnny||Psychology, Doctoral Candidateemail@example.com|
|Bodary, Peter||Kinesiology, Clinical Assistant Professorfirstname.lastname@example.org|
|Burmeister, Margit||Professor, Psychiatryemail@example.com|
|Buu, Anne||Associate Professor, Health Behavior & Biological Sciencesfirstname.lastname@example.org|
|Byon, Eunshin||IOE Assistant email@example.com|
|Ceglarek, Peter||Health Management and Policy/Intitute for Social Research, Healthy Minds Network, Administrative & Project Coordinatorfirstname.lastname@example.org|
|Chen, Weiyun||School of Kinesiology, Associate Professoremail@example.com|
|Colabianchi, Natalie||Survey Research Center, ISR, Research Assistant Professorfirstname.lastname@example.org|
|Dehzangi, Omid||Computer and Information Science, Dearborn, Assistant Professoremail@example.com|
|Denton, Brian||IOE and Urology, Professorfirstname.lastname@example.org|
|Elkateeb, Ali||Electrical and Computer Eng., Associate Professoremail@example.com|
|Esquivel, Amanda||UM Dearborn Mechanical Engineeringfirstname.lastname@example.org|
|Fan, Xudong||Biomedical Engineering, Professoremail@example.com|
|Feng, Yiheng||UMTRI (Research Fellow)||firstname.lastname@example.org|
|Flannagan, Carol||UMTRI Research Associate Professoremail@example.com|
|Forger, Danny||Professor, DCMB, Mathfirstname.lastname@example.org|
|Gilbert, Anna||Mathematics, Professoremail@example.com|
|Goldstein, Cathy||Neurology, Assistant Professorfirstname.lastname@example.org|
|Goutman, Stephen||Neurology, Assistant Professoremail@example.com|
|Guo, L. Jay||EECS, Professorfirstname.lastname@example.org|
|Hallum, Jeremy||ARC-TS Research Computing Manageremail@example.com|
|Harirchi, Farshad||EECS, Post-Doctoral Fellow||Harirchi@umich.edu|
|Harris, Marcelline||Nursing, Associate Professorfirstname.lastname@example.org|
|Jackson, Jeannette||Biosocial Methods Collaborative, ISR, Managing Directoremail@example.com|
|Jagadish, H.||CSE, Professorfirstname.lastname@example.org|
|Jin, Judy||IOE, Professoremail@example.com|
|Kratz, Anna||PM&R, Assistant Professorfirstname.lastname@example.org|
|Liu, Henry||Civil and Environmental Engineering, Professoremail@example.com|
|Luo, Lan||PhD. in Biostatisticsfirstname.lastname@example.org|
|Ma, Di||UM-Dearborn, CECS, Associate Professoremail@example.com|
|McInnis, Melvin||Professor, Psychiatryfirstname.lastname@example.org|
|Meyer, Seth||ARC-TS, Research Computing Leademail@example.com|
|Mroueh, Jawad||student, and industry employee||awad.Mroueh@zf.com|
|Nahum-Shani, Billie (Inbal)||ISRfirstname.lastname@example.org|
|Nazir, Salman||EECS, Graduate Research Assistantemail@example.com|
|Niss, Laura||Statistics PhD firstname.lastname@example.org|
|Orosz, Gabor||Mechanical Engineering, Assistant Professoremail@example.com|
|Ozay, Necmiye||EECS, Assistant Professorfirstname.lastname@example.org|
|Plumlee, Matthew||Assistant Professor, Industrial and Operations Engineeringemail@example.com|
|Prakash, Atul||EECS, Professorfirstname.lastname@example.org|
|Provost, Emily Mower||EECS, Assistant Professoremail@example.com|
|Rabaut, Lisa||Exercise & Sport Science Institute, Managing Directorfirstname.lastname@example.org|
|Remy, C. David||Mechanimcal Engineering, Assistant Professoremail@example.com|
|Scott, Clayton||Assoc. Prof, EECSfirstname.lastname@example.org|
|Sen, Srijan||Professor, Psychiatryemail@example.com|
|She, Xichen||Biostatistics, Postdoc Research Fellowfirstname.lastname@example.org|
|Sica, Jeffrey||Research Database Administratoremail@example.com|
|Tewari, Ambuj||Assistant Professor, Statistics and EECSfirstname.lastname@example.org|
|Verma, Manish||CSCAR, Geospatial Leademail@example.com|
|Walch, Olivia||Neurology, Research Fellowfirstname.lastname@example.org|
|Ward, Kevin||Professor, Emergency Medicineemail@example.com|
|Wu, Zhenke||Biostatistics, Assistant Professorfirstname.lastname@example.org|
|Xu, Hongwei||Research Assistant Professor at the Institute for Social Researchemail@example.com|
|Zernicke, Ron||Kenisiology, Orthopaedic Surgery, Biomedical Engineering, Professorfirstname.lastname@example.org|
|Zhong, Zhaohui||EECS, Associate Professoremail@example.com|
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 (10/19/2017): Information and Intelligent Systems (IIS): Core Programs
CISE’s Division of Information and Intelligent Systems (IIS) supports research and education projects that develop new knowledge in three core programs:
- The Cyber-Human Systems (CHS) program;
- The Information Integration and Informatics (III) program; and
- The Robust Intelligence (RI) program.
Proposals in the area of computer graphics and visualization may be submitted to any of the three core programs described above.
Proposers are invited to submit proposals in three project classes, which are defined as follows:
- Small Projects – up to $500,000 total budget with durations up to three years;
- Medium Projects – $500,001 to $1,200,000 total budget with durations up to four years; and
- Large Projects – $1,200,001 to $3,000,000 total budget with durations up to five years.
The Cyber-Human Systems (CHS) program specifically mentioned wearables and other sensors.
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.
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).
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.
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.
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