The 1st Workshop on Digital Biomarkers 2017


The 1st Workshop on Digital Biomarkers, collocated with MobiSys 2017 offers a unified forum that brings academics, industry researchers and medical practitioners together and seeks novel, innovative and exciting submissions broadly related to the modeling, testing, and validation of new digital biomarkers for predicting incidence of diseases, health conditions, effects of treatments, and interventions.

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About Digital Biomarkers


A biological marker (biomarker) can be defined by any substance, structure, or process of the body, its actions or products that can influence or predict the incidence of disease, health conditions, effects of treatments, and interventions. Digital biomarkers are the user generated physiological and behavioral measures collected via connected digital devices or wearable and mobile computing systems that can be used to explain, influence or predict the health related outcomes. The digital biomarkers do not include genetic information or data collected through traditional medical instruments. Examples of digital biomarkers include everything from geo-location and physical activity traces through internal physiological processes like vital signs to chemical makeups of skin, blood and other tissues collected by IoT devices, smartphones, wearables or novel digital computational platforms.

A set accurately and reproducibly measurable digital biomarkers can be used to predict various health conditions, outcomes. These can also be used to generate actionable insights. The rich data collected with the built-in sensors and processing units of our smartphones and wearable devices have already shown a lot of promise for passive and continuous measurement of several digital biomarkers which can be used to develop all sorts of new health sensing apps and interventions. However, there are a few major bottlenecks in digital biomarker computation, which include (i) the high level of initial setup cost and difficulty for a long-term data collection from a large population, (ii) the l in our existing smartphones, wearables or other digital tools capturing internal states and processes of the human body, (iii) various confounding factors compromising the accuracy and reproducibility of the digital biomarker, (iv) high cost and intrusiveness. The workshop aims to identify a set of solutions to these problems by bringing in key technological innovations from the areas of mobile computing, machine learning, health sciences and medicine.

Call For Papers



The 1st Workshop on Digital Biomarkers, collocated with MobiSys 2017 offers a unified forum that brings academics, industry researchers and medical practitioners together and seeks novel, innovative and exciting submissions broadly related to the modeling, testing, and validation of new digital biomarkers for predicting incidence of diseases, health conditions, effects of treatments, and interventions. The workshop aims to facilitate a systematic discussion among experts from different knowledge domains including mobile sensing, systems, machine learning, medicine and health sciences in order to (i) identify new digital biomarkers capturing behavioral health, chronic and degenerative diseases, (ii) identify the key shortcomings of the existing mobile and wearable sensor systems, and research study software platforms (e.g., ResearchKit and ResearchStack) for digital biomarker inference in terms of scalability, customizability, and sensing affordances, (iii) find realistic solutions towards building new digital biomarker evidence engine leveraging sensor data from a variety of mobile systems (e.g., smartphones, wearables, IoT devices, or any novel sensor systems), (iv) identify key data collection, labeling, testing and validation methodologies of the new biomarker evidence engine.

Topics of interest (NOT an exhaustive list):

  • Predicting the incidence of disease, health conditions, effects of treatments, and interventions with digital biomarkers.

  • Design and implementation of mobile phone, wearable and/or novel embedded systems based computational platforms.

  • Integration of multimodal data from different sensor streams for digital biomarker modeling.

  • Using existing IoT infrastructure for new digital biomarker modeling.

  • Improved data collection, labeling, testing and Validation methodologies for digital biomarker modeling.

  • Novel signal processing or machine learning techniques for digital biomarker modeling.

  • Developing robust biomarker models that can handle data sparsity and mis-labeling issues.

  • Energy and resource efficient implementation of biomarker models.

  • Designing and implementing data feedback and visualization for both participants and caregivers.

  • Development of smartphone based automated health interventions with digital biomarkers.

A PDF version of the Call for Papers can be found here. If you have any questions, please feel free to contact Tauhidur Rahman (tr266@cornell.edu).

Important Dates

    Due to numerous requests, we have extended the deadlines.
  • Submission deadline: April 20 2017

  • Notification deadline: April 26 2017

  • Camera-ready due: May 1 2017

  • Workshop Date: June 23 2017

Paper Format

All submissions must be original work not under review at any other workshop, conference, or journal. Submissions can be 3 to 6 pages in length including references and must be a PDF file and anonymized. Submissions must follow the ACM MobiSys 2017 formatting guidelines.

Submission

The workshop accepts papers describing complete as well as novel work-in-progress studies. Submission link is here.

Program


Venue: Room Hawk B in the Seneca Niagara Resort and Casino (310 4th St. Niagara Falls, NY 14303).
Time Event
8:00–9:00 Breakfast (LaCascata Restaurant) and Registration (Meeting Room Entrance Area)
9:00–9:15 Welcome and Introduction
9:15–10:00 Keynote title: A Quantum of Solace: Digital Traces and Mental Health
keynote speaker: Vincent M. B. Silenzio, MD, MPH, University of Rochester School of Medicine and Dentistry
Abstract: What does the Higgs Boson have to do with measuring the digital traces of mental health phenomena, such as depression, anxiety, or suicidal thoughts or behaviors? As it turns out, plenty. In this session, we will explore a useful metaphor for understanding physicists’ discovery of the Higgs Boson after many decades. We examine the implications of this metaphor as a tool to develop a conceptual framework for the study of digital biomarkers, particularly with respect to those digital traces (and absences) that may reflect the range of biological, psychological, and social dimensions in mental health. However, even beyond the narrow confines of mental health, this conceptual framework underscores the need to deeply examine the ways in which we model the health phenomena being studied.
10:00–10:30 Coffee Break (Meeting Room Hallway)
10:30–11:15 Exercise 1: Identifying key problems
11:15–12:00 Session 1: Digital Biomarkers for Behavioral and Cognitive Health

  • 11:15-11:30 "Designing Effective Movement Digital Biomarkers for Unobtrusive Emotional State Mobile Monitoring" Abhinav Mehrotra (University College London), Mirco Musolesi (University College London)
  • 11:30-11:45 "Discovery of Behavioral Markers of Social Anxiety from Smartphone Sensor Data" Yu Huang (University of Virginia), Jiaqi Gong (University of Virginia), Mark Rucker (University of Virginia), Philip Chow (University of Virginia), Karl Fua (University of Virginia), Matthew Gerber (University of Virginia), Bethany Teachman (University of Virginia), Laura Barnes (University of Virginia)
  • 11:45-12:00 "Motion Biomarkers for Early Detection of Dementia-Related Agitation" Ridwan Alam (University of Virginia), Jiaqi Gong (University of Virginia), Mark Hanson (George Washington University), Azziza Bankole (Virginia Tech Carilion School of Medicine), Martha Anderson (Virginia Tech Carilion School of Medicine), Tonya Smith-Jackson (North Carolina A&T State University), John Lach (University of Virginia)
12:00–12:30 Session 2: Sensors

  • 12:00-12:15 "Exploring Symmetric and Asymmetric Bimanual Eating Detection with Inertial Sensors on the Wrist" Edison Thomaz (University of Texas at Austin), Abdelkareem Bedri (Carnegie Mellon University), Temiloluwa Prioleau (Rice University), Irfan Essa (Georgia Institute of Technology), Gregory D. Abowd (Georgia Institute of Technology)
  • 12:15-12:30 "MyoBuddy: Detecting Barbell Weight Using Electromyogram Sensors" Renju Liu (UCLA), Bo-Jhang Ho (UCLA), Hsiao-Yun Tseng (UCLA), Mani Srivastava (UCLA)
12:30–1:30 Lunch (LaCascata Restaurant)
1:30–1:45 Session 3: Methodology

  • 1:30-1:45 "Observation Time vs. Performance in Digital Phenotyping" Tom Quisel (Evidation Health), Wei-Nchih Lee (Evidation Health), Luca Foschini (Evidation Health)
1:45–2:30 Exercise 2: Identifying key solutions
2:30–3:00 Coffee Break (Meeting Room Hallway)
3:00–4:00 Panel: Designing studies for feasibility testing, refinement and validation of digital biomarkers
Participants: Michael L Birnbaum (Northwell Health), Graeme Rimmer (Google Fit), Tanzeem Choudhury (Cornell University and HealthRhythms), Mirco Musolesi (University College London), James Niels Rosenquist (Massachusetts General Hospital and Harvard Medical School)
4:00–4:30 Exercise 3: Identifying next steps and Wrap up

Organizers


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Deborah Estrin

Professor in Computer Science

Cornell NYC Tech

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JP Pollak

Senior Researcher in Residence

Cornell NYC Tech

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Tauhidur Rahman

Ph.D. Candidate in Information Science

Cornell University

Program Committee