2017 Proceeding- General Chairs:
- Deborah Estrin,
- JP Pollak,
- Tauhidur Rahman
- Sponsor:
- sigmobile
It is our great pleasure to welcome you to the ACM 1st workshop on Digital Biomarkers 2017 (DigitalBioMarkers'17). The workshop will bring academics, industry researchers and medical researchers together to address the modeling, testing, and validation of new digital biomarkers for evaluating and predicting onset of diseases/health conditions, response to treatments, and effects of 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 relevant to behavioral, chronic, and degenerative conditions, (ii) identify the key shortcomings of the existing mobile and wearable sensor systems, and research 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, and other relevant digital traces), (iv) identify key data collection, labeling, testing and validation methodologies for development of digital biomarkers.
The call for papers attracted highly relevant submissions from around the world. The program committee accepted 6 short papers out of 9 submissions. In addition to the presentations of the 6 accepted papers, the workshop will feature one a morning keynote and an afternoon panel session.
Keynote: "A Quantum of Solace: Digital Traces and Mental Health", Prof. Vincent M. B. Silenzio, University of Rochester School of Medicine & Dentistry
Designing studies for feasibility testing, refinement and validation of digital biomarkers Panel Session
Proceeding Downloads
A Quantum of Solace: Digital Traces and Mental Health
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 ...
Designing Effective Movement Digital Biomarkers for Unobtrusive Emotional State Mobile Monitoring
Mobile sensing technologies and machine learning techniques have been successfully exploited to build effective systems for mental health monitoring and intervention. Various approaches have recently been proposed to effectively exploit contextual ...
Discovery of Behavioral Markers of Social Anxiety from Smartphone Sensor Data
- Yu Huang,
- Jiaqi Gong,
- Mark Rucker,
- Philip Chow,
- Karl Fua,
- Matthew S. Gerber,
- Bethany Teachman,
- Laura E. Barnes
Better understanding of an individual's smartphone use can help researchers to understand the relationship between behaviors and mental health, and ultimately improve methods for early detection, evaluation, and intervention. This relationship may be ...
Motion Biomarkers for Early Detection of Dementia-Related Agitation
Agitation in dementia poses a major health risk for both the patients and their caregivers and induces a huge caregiving burden. Early detection of agitation can facilitate timely intervention and prevent escalation of critical episodes. Sensing ...
Exploring Symmetric and Asymmetric Bimanual Eating Detection with Inertial Sensors on the Wrist
Motivated by health applications, eating detection with off-the-shelf devices has been an active area of research. A common approach has been to recognize and model individual intake gestures with wrist-mounted inertial sensors. Despite promising ...
MyoBuddy: Detecting Barbell Weight Using Electromyogram Sensors
Muscular dystrophy is a group of genetic diseases that cause the loss of muscles and hence weakening the muscle strength. A typical treatment for muscular dystrophy patients is routinely performing weight exercise to slow down the loss in muscles. Thus, ...
Observation Time vs. Performance in Digital Phenotyping
Mobile health (mHealth) technologies enable frequent sampling of physiological and psychological signals over time. In our recent work we used a convolutional neural network (CNN) model to predict self-reported phenotypes of chronic conditions from step ...
Index Terms
- Proceedings of the 1st Workshop on Digital Biomarkers
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| DigiBiom '21 | 10 | 8 | 80% |
| DigitalBiomarkers '17 | 9 | 6 | 67% |
| Overall | 19 | 14 | 74% |