I am a fifth year Ph.D. Student (enrolled January'14) working in Data Science (Department of Mathematics) at Shiv Nadar University, India. I have an undergraduate major in Computer Science, and my research interests lie in two primary areas - (i) developing systems for event detection and pattern recognition using smartphone sensor data, and (ii) leveraging micro-events for deep context mining including human-human interactions and behavior. Having been an active part of the ubiquitous computing research community during my undergraduate as well as during my Ph.D. (expected completion Summer'19), my future aspirations are to join as a post-doctoral researcher in the same field, and continue to build upon my skills and contribute both as an active researcher as well as a mentor for budding undergraduate students. © Copyright held by the owner/author(s).

Majethia R.

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Deep Context Mining of Individuals and Groups Using Smartphone Sensor and Usage Data

UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers

In the world of smart objects, device-ranging and localization using Bluetooth Low Energy (BLE) is becoming popular due to its attractive energy performance, wide platform support and low costs. There has been sufficient motivation on statistical analysis of Channel State Information of Received Signal Strength Indicator (RSSI) data for more effective ranging-based models. However, there has been no ubiquitous solution which is both receiver-agnostic and does not require alteration in the advertisement protocol or packet structure of BLE. In this paper, we propose a truly unsupervised approach for channel-annotation of RSSI data received by a stationary receiver object. Given a sequence of RSSI observations and a discoverable receiver channel-switching policy, we determine the period and hence the time spent by the receiver in an individual channel. Then, we propose a sliding-window based algorithm which utilizes two well-established Likelihood-Ratio algorithms - KLIEP and uLSIF - for extracting Channel State Information of retrospective RSSI observation data. We believe this work lays the foundation of motivating future work in completely unsupervised methods for object-to-object ranging and localization. © 2018 ACM.

ACM International Conference Proceeding Series

Mining channel state information from bluetooth low energy RSSI for robust object-to-object ranging

With the evolution of nuclear families and diverse career options, families as social groups are spending lesser time together than in the past decades. In this work, we study both quantitative as well as qualitative aspects of time spent with family members through a smartphone-based pervasive study on a sample of 12 families over 14 days. Further, we also examine the perception of 78 millennials on what they feel about, and expect from, the time they spend with their families, however long it may be. We aim to identify the key parameters that shape family life in this day and age, along with examining the participation of individuals of various roles within the family in activities such as conversations, workout sessions, eating together and other social interactions. Among all activities detected to be performed by families reporting high satisfaction with familial life, Eating Together and Using Smartphones Together emerged as the most prominent ones. We discover a greater disparity among the habits of family members, especially millennials, staying away from each other as compared to those staying together. © 2017 Association for Computing Machinery.

HumanSys 2017 - Proceedings of the 1st International Workshop on Human-Centered Sensing, Networking, and Systems, Part of SenSys 2017

Smartphone-based Qualitative Analyses of Social Activities During Family Time

In this work, we provide motivation for a zero-effort crowd-sensing task: auto-annotated ground truth collection for physical activity recognition. Data obtained through Smartphones for classification of human activities is prone to discrepancies, which reiterates the need for better and larger activity datasets. Artificial data generation algorithms fail to efficiently generate quality instances for minority data. In the proposed model, crowd-sourced sensor data is classified by a robust classifier built by researchers ground up. We nominate a Generic Classifier with ≥ 95\% accuracy for this purpose. Data collection and distribution models which ensure that the crowd client receives non-skewed, quality data from locations with higher degree of activity occurrence are elucidated upon. Also integrated within our proposed model are Location-Specific Classifiers, which can be utilized by developers to optimize on location-specific tasks. Effective validation of classified activities using diverse sensor data streams improves the proposed classifier systems and boosts ground-truth accuracy. © 2016 Copyright held by the owner/author(s).