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Tanvi Banerjee

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3 papers

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IS Journal 2017 Journal Article

IoT Quality Control for Data and Application Needs

Tanvi Banerjee
Amit Sheth

The amount of Internet of Things (IoT) data is growing rapidly. Although there is a growing understanding of the quality of such data at the device and network level, important challenges in interpreting and evaluating the quality at informational and application levels remain to be explored. This article discusses some of these challenges and solutions of IoT systems at the different OSI layers to understand the factors affecting the quality of the overall system. With the help of two IoT-enabled digital health applications, the authors investigate the role of semantics in measuring the data quality of the system, as well as integrating multimodal data for clinical decision support. They also discuss the extension of IoT to the Internet of Everything by including human-in-the-loop to enhance the system accuracy. This paradigm shift through the confluence of sensors and data analytics can lead to accelerated innovation in applications by overcoming the limitations of the current systems, leading to unprecedented opportunities in healthcare.

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AAAI Conference 2016 Conference Paper

Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations

Pramod Anantharam
Krishnaprasad Thirunarayan
Surendra Marupudi
Amit Sheth
Tanvi Banerjee

Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road link can be interpreted in terms of trafﬁc related incident reports from city authorities and social media data (textual), providing a complementary understanding of trafﬁc dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Speciﬁcally, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city trafﬁc events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world trafﬁc and twitter dataset collected over a year with promising results.

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JBHI Journal 2014 Journal Article

Sit-to-Stand Measurement for In-Home Monitoring Using Voxel Analysis

Tanvi Banerjee
Marjorie Skubic
James M. Keller
Carmen Abbott

We present algorithms to segment the activities of sitting and standing, and identify the regions of sit-to-stand (STS) transitions in a given image sequence. As a means of fall risk assessment, we propose methods to measure STS time using the 3-D modeling of a human body in voxel space as well as ellipse fitting algorithms and image features to capture orientation of the body. The proposed algorithms were tested on ten older adults with ages ranging from 83 to 97. Two techniques in combination yielded the best results, namely the voxel height in conjunction with the ellipse fit. Accurate STS time was computed on various STSs and verified using a marker-based motion capture system. This application can be used as part of a continuous video monitoring system in the homes of older adults and can provide valuable information to help detect fall risk and enable early interventions.

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