Themis is a professor in LIPADE, the computer science department of the Paris Descartes University and a director of the Data Intensive and Knowledge Oriented Systems (diNo) group. He is also very active in the data management and engineering community as Research Track Associate Editor for VLDB, Editor in Chief for the Big Data Research (BDR) journal, and Associate Editor for the Transactions on Knowledge and Data Engineering (TKDE) journal.
Professor Palpanas’ research is on problems related to online and offline data analytics (focusing on fast streams and massive collections). His group has world-leading expertise on time series management, indexing, and mining. Together they have developed the current state-of-the-art data series indexes, iSAX2+ (bulk loading) and ADS+ (adaptive indexing), the only data series query workload benchmark, as well as DSStat, a toolset for data series preprocessing and visualization.
They have applied their techniques on streaming and uncertain data series, and have worked with data from diverse domains, such as home networks, road tunnels, and manufacturing. The CrowdSignals.io data will significantly help their efforts in building efficient tools for the management and analysis of such data, as well as in delivering effective applications to end users for capitalizing on the data they produce.
The main focus of the group is to facilitate and improve the effectiveness of key tasks that rely on the quality of the data, such as: data integration, schema integration, data mining and decision making. The group is formed by students, faculty, and staff from the Department of Computing Systems of the Federal University of Campina Grande (DSC/UFCG).
Ming Zeng is a Ph.D. student at Carnegie Mellon University. His research interests include machine learning, deep learning, human behavior modeling, and natural language processing. His work is primarily focused on deep neural networks (DNNs) for human activity recognition. The deep learning models used in his work mainly include convolutional neural networks (CNNs) and Long Short Term Memory (LSTM) with different architectures according to different applications. In addition, instead of treating the deep neural network as a black box, he is trying to interpret the features extracted from DNNs.
Ming will explore the CrowdSignals.io data in the human activity recognition related areas. Deep neural networks can take advantage of the large dataset to train a good model. In addition, because the CrowSignals.io dataset involves a number of sensors, it is worth trying neural networks with a sensor fusion approach.
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.
Rijurekha Sen is a post-doctoral researcher at Max Planck Institute for Software Systems, Germany. She generally works with mobiles and sensors to build applications related to road traffic monitoring, energy measurement and targeted advertising in the retail sector. The technical skills involved in her kind of work are smartphone programming and embedded systems design, sensor data processing using applied machine learning, wireless networking to connect sensors and backend servers and data visualizations on static and interactive maps. There are some logistic skills involved in deploying the systems that she implements, in collaboration with government organizations or startups. Recently, she is working on mobile data privacy to see if cool mobile applications can be supported, even after ensuring data privacy! She is also interested in auditing mobile services and apps created by third parties to measure their functionality and privacy properties as an independent researcher.
She will explore the CrowdSignals.io data primarily in the context of mobile privacy related research. Whether interesting mobile apps can be supported using encrypted data, needs real datasets to be evaluated. She is hoping that the CrowdSignals.io dataset will fill that gap of easily available mobile and sensor data, which can be used to test such research hypotheses or prototype systems.
R Venkatesha Prasad is an Assistant Professor at TU Delft where he has been supervising Ph.D. and M.Sc. students and teaching courses. His work at TUDelft has resulted in 180+ publications and he also contributes to the academic community by reviewing and organizing conferences and workshops as well as by leading technical committees and memberships for standards boards. He is a senior member of ACM & IEEE.
Prof. Prasad completed his Ph.D. from IISc Bangalore, India in 2003 where he designed a scalable VoIP conferencing platform. The work involved understanding of network protocols, application design, and human computer interface. Part of the thesis lead to a startup venture, Esqube, where he lead the engineering of real-time applications including bridging anonymous VoIP calls called Click-to-Talk for Ebay.com, and filed both patents and PCT applications with his colleagues. Esqube was selected as top 100 IT innovators in India in 2006 by NASSCOM and top 100 in promising companies in Asia by RedHerring in 2008.
Since joining TUDelft as a PostDoc in 2005, he has worked on the EU FP7 Magnet Project and the Dutch project PNP-2008 on Personal Networks (PNs). He also started working on Cognitive Radio Networks (CRNs) and 60GHz networks for future homes. He is contributing to IEEE standards on CRNs. Now, his work involves the Internet of Things (IoT), Cyber Physical systems (CPS) and energy harvesting networks and he is working on EU funded iCore project on IoTs.
Data is everywhere but they are all fragmented. Venkatesha expects to use CrowdSignals.io data to deliver cohesive new insights. He hopes to use the data from CrowdSignals.io for his research on developing “knowledge” from this data, going one step above “information processing”.
Our next guest post is by Vinayak Naik, Associate Professor and Founding Coordinator of the MTech in CSE with specialization in Mobile Computing at IIIT-Delhi, New Delhi, India. He writes about his group’s research on using smartphone sensors to measure metro traffic in New Delhi.
In recent years, mobile sensing has been used in myriad of interesting problems that rely on collection of real-time data from various sources. The field of crowd-sourcing or participatory sensing is extensively being used for data collection to find solutions to day-to-day problems. One such problem of growing importance is analytics for smart cities. As per Wikipedia, a smart city is defined as an urban development vision to integrate multiple information and communication technology (ICT) solutions in a secure fashion to manage a city’s assets and notable research is being conducted in this direction. One of the important assets is transportation. With the growing number of people and shrinking land space, it is important to come up with solutions to reduce pollution caused by the vehicles and ease commuting between any two locations within the city.
Today, cities in developed countries, such as New York, London, etc, have millions of bytes of data being logged on a diurnal basis using sensors installed at highway and metro stations. This data is typically leveraged to carry out an extensive analysis to understand usage of roads and metro across the city’s layout and aid in the process of better and uniform city planning in long term. However, a common challenge faced in developing countries is the paucity of such detailed data due to lack of sensors in the infrastructure. In the absence of these statistics, the most credible solution is to collect data via participatory sensing using low-cost sensors like accelerometer, gyroscope, magnetometer, GPS, and WiFi that come packaged with modern day smartphones. These sensors can be used to collect data on behalf of users, which upon analysis can be leveraged in the same way as data made available through infrastructure-based.
In short term, this data can be leveraged to guide commuters about expected rush at the stations. This is important as in some extreme cases wait time at stations could be more than the travel time itself. Aged people, those with disabilities, and children can possibly avoid traveling if there is rush at stations. We show two snapshots of platform at Rajiv Chowk metro station in Delhi, in Figure 1(a) and Figure 1(b). These figures show variation in the amount of rush that can happen in a real life scenario. In the long run information from our solution can also be integrated with route planning tools (e.g., Google Maps, see Figure 1(c)), to give an estimated waiting time at the stations. This will help those who want to minimize or avoid waiting at the stations.
About a decade ago, even cities in developed countries had less infrastructural support to get sensory data. The author was the lead integrator of the Personal Environmental Impact Report (PEIR) project in 2008. The objective of the project was to estimate emission and exposure to pollution for the city of Los Angeles  in USA. PEIR used data from smartphones to address the problem of lack of data. Today, the same approach is applicable in developing countries, where reach of smartphones is more than that of infrastructure-based sensors. At IIIT-Delhi, master’s student Megha Vij, co-advised by Prof. Viswanath Gunturi and the author, worked her thesis  on the problem to build a model, which can predict the metro station activity in the city of New Delhi, India using accelerometer logs collected from a smartphone app.
Our approach is to detect commuter’s entry into metro station and thereon measure time spent by the commuter till he/she boards the train. Similarly, we measure the time spent from disembarking the train until exiting the metro station. These two time-durations are indicative of the rush at metro stations, i.e., more the rush, more the time spent. We leverage geo-fencing APIs on the smartphones to detect entry into and exit from the metro stations. These APIs efficiently use GPS to detect whether the user has crossed a boundary, in our case perimeter around the metro station. For detecting boarding and disembarking, we use accelerometers on the smartphones to detect whether the commuter is in a train or not. Unlike geo-fencing, the latter is an unsolved problem. We treat this problem as a two class classification, where the goal is to detect whether a person is traveling in a train or is at the metro station. Furthermore, the “in-metro-station” activity is a collection of many micro-activities including walking, climbing stairs, queuing, etc and therefore, needs to be combined into one single category for the classification. We map this problem to machine learning and explore an array of classification algorithms to solve it. We attempt to use discriminating, both probabilistic and non-probabilistic, classification methods to effectively distinguish such patterns into “in-train” and “in-metro-station” classes.
Figure 2 illustrates “in-train” and “in-metro-station” classes on sample accelerometer data that we collected. One may observe a stark difference in accelerometer values across the two classes. The values for the”in-train” class have less variance, whereas for the “in-metro-station” class, we observe heavy fluctuations (and thus much greater variance). This was expected since a train typically moves at a uniform speed, whereas in a station we would witness several small activities such as walking, waiting, climbing, sprinting, etc.
It is important to note that this problem is not limited to only analytics for smart cities, e.g., our work is applicable for personal health. Smart bands and watches are used these days to detect users’ activities, such as sitting, walking, and running. At IIIT-Delhi, we are working on these problems. A standardized data collection and sharing app for smartphones would leapfrog our research. CrowdSignals.io fills this need aptly.
Takeshi Okadome is a Professor in the Department of Informatics at Kwansei Gakuin University. His group works on environmental media design, using various sensors to design a new media that represents a physical or “mind” state of a physical object in an environment, and using the media, they create various contents, in particular, web contents together with each person’s favorite objects.
They use sensor network, physical computing, and machine learning technologies to attempt to: (1) to establish a method for interpreting sensor data and verbalizing or visualizing them, (2) to interpolate human actions or real-world events that cannot be directly inferred from sensor data by mining the information on the web, (3) to design “environment media” that represent the verbalized or visualized data, and (4) to create web contents using the environment media. The group also works on recognition of daily living activities from sensor data using machine learning techniques.
Prof. Okadome will use the CrowdSignals.io data to construct better recognition methods for daily living activities using CrowdSignals.io Data and also test the methods using them.
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.