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.
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.
Adam has a first degree in Psychology from the University of London (Goldsmiths College), and a PhD in social psychology from the University of Hertfordshire (on self-esteem and motivation). Immediately after my PhD, he worked at the University of Glamorgan as a lecturer in social psychology (from 1995 until 1999). He then joined the Institute of Educational Technology at the Open University as a lecturer (and then senior lecturer) in ICT and Social Science (from 1999 until 2007). Adam joined the University of Bath, School of Management in June 2007, first as a senior lecturer, then as a Reader in ‘Information Systems’. In September 2012 he joined UWE Bristol as Professor of Behavioural Change, and returned to the University of Bath in January 2016 to become Professor of Information Systems. Adam works at the intersection between human sciences and technology – on topics such as communication, social media, wearable technology and behaviour, and privacy / surveillance / cyber-security.
Adam is particularly interested in issues of public-private space and activity, mood and use, and patterns of behaviour. He also hopes to use the CrowdSignals.io data sets in a new Masters course at Bath to help train future data scientists.
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