Bharti Bansal

Bio: Bharti Bansal is an academic researcher. The author has contributed to research in topics: Interface (computing) & Gesture recognition. The author has an hindex of 1, co-authored 1 publications receiving 303 citations.

Gesture Recognition: A Survey

Abstract: With increasing use of computers in our daily lives, lately there has been a rapid increase in the efforts to develop a better human computer interaction interface. The need of easy to use and advance types of human-computer interaction with natural interfaces is more than ever. In the present framework, the UI (User Interface) of a computer allows user to interact with electronic devices with graphical icons and visual indicators, which is still inconvenient and not suitable for working in virtual environments. An interface which allow user to communicate through gestures is the next step in the direction of advance human computer interface. In the present paper author explore different aspects of gesture recognition techniques.

A Tutorial on Human Activity Recognition Using Body-Worn

Abstract: The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.

Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks

Abstract: Automatic detection and classification of dynamic hand gestures in real-world systems intended for human computer interaction is challenging as: 1) there is a large diversity in how people perform gestures, making detection and classification difficult, 2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification, in fact, a negative lag (classification before the gesture is finished) is desirable, as feedback to the user can then be truly instantaneous. In this paper, we address these challenges with a recurrent three-dimensional convolutional neural network that performs simultaneous detection and classification of dynamic hand gestures from multi-modal data. We employ connectionist temporal classification to train the network to predict class labels from inprogress gestures in unsegmented input streams. In order to validate our method, we introduce a new challenging multimodal dynamic hand gesture dataset captured with depth, color and stereo-IR sensors. On this challenging dataset, our gesture recognition system achieves an accuracy of 83:8%, outperforms competing state-of-the-art algorithms, and approaches human accuracy of 88:4%. Moreover, our method achieves state-of-the-art performance on SKIG and ChaLearn2014 benchmarks.

Industry use of virtual reality in product design and manufacturing: a survey

Abstract: In 1999, Fred Brooks, virtual reality pioneer and Professor of Computer Science at the University of North Carolina at Chapel Hill, published a seminal paper describing the current state of virtual reality (VR) technologies and applications (Brooks in IEEE Comput Graph Appl 19(6):16, 1999). Through his extensive survey of industry, Brooks concluded that virtual reality had finally arrived and "barely works". His report included a variety of industries which leveraged these technologies to support industry-level innovation. Virtual reality was being employed to empower decision making in design, evaluation, and training processes across multiple disciplines. Over the past two decades, both industrial and academic communities have contributed to a large knowledge base on numerous virtual reality topics. Technical advances have enabled designers and engineers to explore and interact with data in increasingly natural ways. Sixteen years have passed since Brooks original survey. Where are we now? The research presented here seeks to describe the current state of the art of virtual reality as it is used as a decision-making tool in product design, particularly in engineering-focused businesses. To this end, a survey of industry was conducted over several months spanning fall 2014 and spring 2015. Data on virtual reality applications across a variety of industries was gathered through a series of on-site visits. In total, on-site visits with 18 companies using virtual reality were conducted as well as remote conference calls with two others. The authors interviewed 62 people across numerous companies from varying disciplines and perspectives. Success stories and existing challenges were highlighted. While virtual reality hardware has made considerable strides, unique attention was given to applications and the associated decisions that they support. Results suggest that virtual reality has arrived: it works! It is mature, stable, and, most importantly, usable. VR is actively being used in a number of industries to support decision making and enable innovation. Insights from this survey can be leveraged to help guide future research directions in virtual reality technology and applications.

Advances in Spatial and Temporal Databases

Abstract: Spatiotemporal reachability queries arise naturally when determining how diseases, information, physical items can propagate through a collection of moving objects; such queries are significant for many important domains like epidemiology, public health, security monitoring, surveillance, and social networks. While traditional reachability queries have been studied in graphs extensively, what makes spatiotemporal reachability queries different and challenging is that the associated graph is dynamic and space-time dependent. As the spatiotemporal dataset becomes very large over time, a solution needs to be I/O-efficient. Previous work assumes an ‘instant exchange’ scenario (where information can be instantly transferred and retransmitted between objects), which may not be the case in many real world applications. In this paper we propose the RICC (Reachability Index Construction by Contraction) approach for processing spatiotemporal reachability queries without the instant exchange assumption. We tested our algorithm on two types of realistic datasets using queries of various temporal lengths and different types (with single and multiple sources and targets). The results of our experiments show that RICC can be efficiently used for answering a wide range of spatiotemporal reachability queries on disk-resident datasets.