Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called "MobiAct", which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with mbientlabs wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.Comment: 7 page

Assessing collaborative learning: big data, analytics and university futures

Traditionally, assessment in higher education has focused on the performance of individual students. This focus has been a practical as well as an epistemic one: methods of assessment are constrained by the technology of the day, and in the past they required the completion by individuals under controlled conditions, of set-piece academic exercises. Recent advances in learning analytics, drawing upon vast sets of digitally-stored student activity data, open new practical and epistemic possibilities for assessment and carry the potential to transform higher education. It is becoming practicable to assess the individual and collective performance of team members working on complex projects that closely simulate the professional contexts that graduates will encounter. In addition to academic knowledge this authentic assessment can include a diverse range of personal qualities and dispositions that are key to the computer-supported cooperative working of professionals in the knowledge economy. This paper explores the implications of such opportunities for the purpose and practices of assessment in higher education, as universities adapt their institutional missions to address 21st Century needs. The paper concludes with a strong recommendation for university leaders to deploy analytics to support and evaluate the collaborative learning of students working in realistic contexts

Autonomous real-time surveillance system with distributed IP cameras

An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator

Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities.

We describe a controlled experiment, aiming to study productivity and stress effects of email interruptions and activity interactions in the modern office. The measurement set includes multimodal data for n = 63 knowledge workers who volunteered for this experiment and were randomly assigned into four groups: (G1/G2) Batch email interruptions with/without exogenous stress. (G3/G4) Continual email interruptions with/without exogenous stress. To provide context, the experiment's email treatments were surrounded by typical office tasks. The captured variables include physiological indicators of stress, measures of report writing quality and keystroke dynamics, as well as psychometric scores and biographic information detailing participants' profiles. Investigations powered by this dataset are expected to lead to personalized recommendations for handling email interruptions and a deeper understanding of synergistic and antagonistic office activities. Given the centrality of email in the modern office, and the importance of office work to people's lives and the economy, the present data have a valuable role to play

Classification of changes in API evolution

Applications typically communicate with each other, accessing and exposing data and features by using Application Programming Interfaces (APIs). Even though API consumers expect APIs to be steady and well established, APIs are prone to continuous changes, experiencing different evolutive phases through their lifecycle. These changes are of different types, caused by different needs and are affecting consumers in different ways. In this paper, we identify and classify the changes that often happen to APIs, and investigate how all these changes are reflected in the documentation, release notes, issue tracker and API usage logs. The analysis of each step of a change, from its implementation to the impact that it has on API consumers, will help us to have a bigger picture of API evolution. Thus, we review the current state of the art in API evolution and, as a result, we define a classification framework considering both the changes that may occur to APIs and the reasons behind them. In addition, we exemplify the framework using a software platform offering a Web API, called District Health Information System (DHIS2), used collaboratively by several departments of World Health Organization (WHO).Peer ReviewedPostprint (author's final draft

Geometric Cross-Modal Comparison of Heterogeneous Sensor Data

In this work, we address the problem of cross-modal comparison of aerial data streams. A variety of simulated automobile trajectories are sensed using two different modalities: full-motion video, and radio-frequency (RF) signals received by detectors at various locations. The information represented by the two modalities is compared using self-similarity matrices (SSMs) corresponding to time-ordered point clouds in feature spaces of each of these data sources; we note that these feature spaces can be of entirely different scale and dimensionality. Several metrics for comparing SSMs are explored, including a cutting-edge time-warping technique that can simultaneously handle local time warping and partial matches, while also controlling for the change in geometry between feature spaces of the two modalities. We note that this technique is quite general, and does not depend on the choice of modalities. In this particular setting, we demonstrate that the cross-modal distance between SSMs corresponding to the same trajectory type is smaller than the cross-modal distance between SSMs corresponding to distinct trajectory types, and we formalize this observation via precision-recall metrics in experiments. Finally, we comment on promising implications of these ideas for future integration into multiple-hypothesis tracking systems.Comment: 10 pages, 13 figures, Proceedings of IEEE Aeroconf 201