Browsing by Subject "Feature extraction"
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Item RAM: Radar-based Activity Monitor(IEEE, 2016-07-20) Khan, Md Abdullah Al Hafiz; Kukkapalli, Ruthvik; Waradpande, Piyush; Kulandaivel, Sekar; Banerjee, Nilanjan; Roy, Nirmalya; Robucc, RyanActivity recognition has applications in a variety of human-in-the-loop settings such as smart home health monitoring, green building energy and occupancy management, intelligent transportation, and participatory sensing. While fine-grained activity recognition systems and approaches help enable a multitude of novel applications, discovering them with non-intrusive ambient sensor systems pose challenging design, as well as data processing, mining, and activity recognition issues. In this paper, we develop a low-cost heterogeneous Radar based Activity Monitoring (RAM) system for recognizing fine-grained activities. We exploit the feasibility of using an array of heterogeneous micro-doppler radars to recognize low-level activities. We prototype a short-range and a long-range radar system and evaluate the feasibility of using the system for fine-grained activity recognition. In our evaluation, using real data traces, we show that our system can detect fine-grained user activities with 92.84% accuracy.Item Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation(IEEE, 2018-08-23) Khan, Md Abdullah Al Hafiz; Roy, Nirmalya; Misra, ArchanWe investigate the problem of making human activity recognition (AR) scalable-i.e., allowing AR classifiers trained in one context to be readily adapted to a different contextual domain. This is important because AR technologies can achieve high accuracy if the classifiers are trained for a specific individual or device, but show significant degradation when the same classifier is applied context-e.g., to a different device located at a different on-body position. To allow such adaptation without requiring the onerous step of collecting large volumes of labeled training data in the target domain, we proposed a transductive transfer learning model that is specifically tuned to the properties of convolutional neural networks (CNNs). Our model, called HDCNN, assumes that the relative distribution of weights in the different CNN layers will remain invariant, as long as the set of activities being monitored does not change. Evaluation on real-world data shows that HDCNN is able to achieve high accuracy even without any labeled training data in the target domain, and offers even higher accuracy (significantly outperforming competitive shallow and deep classifiers) when even a modest amount of labeled training data is available.Item Sleep Well: A Sound Sleep Monitoring Framework for Community Scaling(IEEE, 2015-09-14) Hossain, H M Sajjad; Roy, Nirmalya; Khan, MD Abdullah Al HafizFollowing healthy lifestyle is a key for active living. Regular exercise, controlled diet and sound sleep play an invisible role on the well being and independent living of the people. Sleep being the most durative activities of daily living (ADL) has a major synergistic influence on people's mental, physical and cognitive health. Understanding the sleep behavior longitudinally and its underpinning clausal relationships with physiological signals and contexts (such as eye or body movement etc.) horizontally responsible for a sound or disruptive sleep pattern help provide meaningful information for promoting healthy lifestyle and designing appropriate intervention strategy. In this paper we propose to detect the microscopic states of the sleep which fundamentally constitute the components of a good or bad sleeping behavior and help shape the formative assessment of sleep quality. We initially investigate several classification techniques to identify and correlate the relationship of microscopic sleep states with the overall sleep behavior. Subsequently we propose an online algorithm based on change point detection to better process and classify the microscopic sleep states and then test a lightweight version of this algorithm for real time sleep monitoring activity recognition and assessment at scale. For a larger deployment of our proposed model across a community of individuals we propose an active learning based methodology by reducing the effort of ground truth data collection. We evaluate the performance of our proposed algorithms on real data traces, and demonstrate the efficacy of our models for detecting and assessing fine-grained sleep states beyond an individual.Item Zero-Day Attack Identification in Streaming Data Using Semantics and Spark(IEEE, 2017-09-11) Pallaprolu, Sai C.; Sankineni, Rishi; Thevar, Muthukumar; Karabatis, George; Wang, JianwuIntrusion Detection Systems (IDS) have been in existence for many years now, but they fall short in efficiently detecting zero-day attacks. This paper presents an organic combination of Semantic Link Networks (SLN) and dynamic semantic graph generation for the on the fly discovery of zero-day attacks using the Spark Streaming platform for parallel detection. In addition, a minimum redundancy maximum relevance (MRMR) feature selection algorithm is deployed to determine the most discriminating features of the dataset. Compared to previous studies on zero-day attack identification, the described method yields better results due to the semantic learning and reasoning on top of the training data and due to the use of collaborative classification methods. We also verified the scalability of our method in a distributed environment.