Nima is currently the Viterbi Fellow of Digital Medicine at the Scripps Translational Science Institute, where he is kick-starting new research and engineering efforts related to the use of wearable and passive sensing devices in healthcare. As the first engineer to be working on mHealth related efforts at STSI, he works closely with a team of physicians and statisticians across STSI and the Scripps Health hospital system to develop new technologies that address the needs of patients and physicians. He believes that patient collected data, from outside the clinical setting, will play a major role in ensuring that the future of medical care is both scalable and affordable. Reaching that point will require advances in sensing technologies, user-context recognition, energy-management, and machine learning. His research focuses on the development of new sensing devices (particularly those related to air quality and respiratory health monitoring) and analytical techniques for turning the data collected by phones, wearables, and in-home devices into actionable information for physicians and researchers.
There is a great deal of variance in how individuals interact with technology and the world around them, making the evaluation of context-recognition systems, energy-saving optimizations, and other systems research extremely challenging. The large and diverse CrowdSignals.io dataset, collected by real users in the field, will help us develop more accurate models of how people interact with technology, one another, and the world around them. In particular, the data will be extremely valuable in our dynamic energy-management work, which adjusts sensor and application behavior based on user-context, interaction, and device state.
Thomas Ploetz is a Reader at the Open Lab at Newcastle University. His research is focused on computational behaviour analysis, that is building and deploying (statistical) models that capture behavior and enable quantitative assessments of it. Behavior is thereby captured opportunistically, e.g., through wearable or ubiquitous sensing. He considers himself working as an applied machine learning researcher.
The central theme of Thomas’ research is to develop techniques and systems that actually have an impact on people’s life. Therefore, his research is almost always connected to some practical application (in contrast to purely theoretical work) and he is keen on deploying systems he develops in the “wild”, i.e., in real-world settings. The most prominent domain for this kind of work is health where he is working on computational assessments of behavioral phenotypes of, for example, Parkinson’s, Dementia, or Autism. Within the Digital Interaction group at Newcastle University he is involved in a number of projects that address these research themes from different angles.
Thomas intends to use CrowdSignals.io mainly for unsupervised learning of behavior representations.
Michael is an Associate Professor of Psychosomatic Medicine and Psychotherapy in the Department of Psychosomatic Medicine, Psychotherapy and Medical Psychology at the University of Ulm, Germany. As senior physician and head of the psychosomatic outpatient clinic he works especially with patients and their “significant others” that suffer from mental disorders like somatic symptom disorders, chronic pain disorder, personality disorders, and eating disorders. Michael Noll-Hussong is medical specialist, certified psychotherapist, certified physiologist and neuroscientist. His research focuses on – amongst others – the link between psychological stress and bodily symptoms, affective meaning construction, empathy and social neuroscience. Methods include clinical, phenomenological, psychometric, cognitive psychology, psychophysiology, and neuroimaging (fMRI).
He will use the CrowdSignals.io data to begin a study of how personal device data may be used to study mental disorders like pain, depressive and eating disorders in more detail and in combination with other methods, especially of social neuroscience.
Jonathan is a Research Scientist at PARC, where his research focuses on the use of machine learning and deep learning in biomedical and physiological data. He is interested in the analysis of data from mobile and wearable devices for pervasive and ubiquitous health management purposes. He has investigated how mobile and wearable technology can be used within health condition management systems for disorders such as panic disorder, post-traumatic stress disorder and depressive disorder. He is particularly interested in the analysis of physiological data in order to infer information about psychological state. His research interests include affective computing, ubiquitous computing, artificial intelligence, machine learning, deep learning, and physiological data analysis. Jonathan holds a Ph.D. from the University of Auckland where his research focused on the use of artificial intelligence for strategy generation in computer games.
Jonathan will use the data that CrowdSignals.io provides to improve and broaden the types of activity recognition that currently exist. In addition, he will use physiological data from wearable devices for affective computing purposes.
The Smart Data Innovation Lab (SDIL) offers big data researchers a unique access to a large variety of Big Data and In-Memory predictive analytics technologies (e.g. SAP HANA, IBM WATSON, Software AG Terracotta). Industry and science collaborate closely in order to find hidden value in big data and generate smart data. Projects are focused on the strategic research areas of Industrie 4.0, Smart Energy, Smart Cities and Personalized Medicine. In order to close today’s gap between academic research and industry problems through a data driven innovation cycle the SDIL provides extensive support to all collaborative research projects free of charge (applications are accepted via the web site).
SDIL provides access to experts and domain-specific skills within Data Innovation Communities fostering the exchange of project results. They further provide the possibility for open-innovation and bilateral matchmaking between industrial partners and academic institutions. Template agreements and processes ensure fast project initiation at maximum legal security fit to the common technological platform. A standardized process allows anyone to set up a new collaborative project at SDIL within 2 weeks. Furthermore, it actively lists data sources such as CrowdSignals.io and lists relevant code artifacts to augment the unique industrial grade solutions provided within the platform.
SDIL is a community effort from both industry and academia in Germany coordinated by Prof. Michael Beigl’s team at TECO. The Karlsruhe Institute of Technology runs the platform.
One use case for CrowdSignals.io data that will be investigated by KIT TECO in personalized medicine is investigating correlations between interruptibility of a user and their context for field research and surveying. They expect to be able to infer interruptibility rules to implement a smart notification management system. It shall handle notifications with respect to the user’s interruptibility with the objective to improve user experience.
Christian is an Associate Professor at the University of Notre Dame Department of Computer Science and Engineering where he directs the Mobile Computing Lab (M-Lab). His team studies systems and methods that combine various types and sources of information to develop new or improved context-aware applications and services. Examples of such research challenges include: how can we reduce localization and tracking errors using opportunistic sharing of data between mobile devices; how can we detect and assess health problems using information collected from mobile device and wearables; and what can we learn about the relationships between behaviors, health and wellbeing, social ties, etc., using data collected from smartphones?
The key to building context-aware services is access to large-scale and longitudinal data sets, which are difficult to collect. The CrowdSignals.io data will provide contextual information that can help Christian’s team to answer various questions about human behavior and correlations between various forms of activities, traits, and preferences.
Our second in a series of expert guest posts comes from Bilgin Kosucu, a doctoral student in the WiSe group at Bogazici University Computer Engineering Department. He provides a brief history of their unique and diverse group and writes about their work with wireless sensors and wearables in Turkey.
WiSe was established by a computer networks researcher who then extended the work to wireless sensors. Eventually, the research on wireless sensor networks expanded to include wearable/ambient sensors and mobile phones. Currently, we are involved with the analysis of daily activities, ambient assisted living and elderly healthcare including but not limited to the UBI-HEALTH Project, supported by the EC Marie Curie IRSES Program.
As a group we are experienced of designing and developing mobile data collection platforms (e.g. on Android phones and Samsung Galaxy Gear S smartwatches), but we believe that CrowdSignals.io will build a unique database, having dedicated data annotators and considering the immense difficulty of gathering the ground truth data. This database will serve as common platform, owing its popularity to being a crowd funded collaboration, for researchers to test and compare their algorithms.
In addition to the opportunity of applying our previous research of activity recognition to a well defined and standardized dataset, we hope to reach out to the wearable computing community and build the pavement for finer research through advanced collaboration.
Lumme Inc is developing a personalized quit program for smokers by combining wearable sensors, data analytics, and behavioral psychology. They are developing the technology to automate and scale personalized care. With data from a phone and wristband, the Lumme platform can automatically detect when the user is smoking and identify triggers associated with smoking behavior. This information is then used to predict high risk situations and prevent a relapse by offering just-in-time intervention, specifically designed to induce lasting behavioral change. This technology pushes the envelope of traditional therapy to make this the last quit attempt of every smoker.
With data from CrowdSignals.io, Lumme will test their smoking-detection algorithms and expand the technology to include detection of eating behavior as a possible trigger for smoking behavior. This can further enable our technology to detect and treat eating disorders such as binge-eating disorder, anorexia nervosa, bulimia nervosa, and obesity.