Browsing by Author "Saha, Deba Pratim"

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    Affective Feedback in a Virtual Reality based Intelligent Supermarket
    Saha, Deba Pratim; Martin, Thomas L.; Knapp, R. Benjamin (ACM, 2017)
    The probabilistic nature of the inferences in a context-aware intelligent environment (CAIE) renders them vulnerable to erroneous decisions resulting in wrong services. Learning to recognize a user’s negative reactions to such wrong services will enable a CAIE to anticipate a service’s appropriateness. We propose a framework for continuous measurement of physiology to infer a user’s negative-emotions arising from receiving wrong services, thereby implementing an implicit-feedback loop in the CAIE system. To induce such negative-emotions, in this paper, we present a virtualreality (VR) based experimental platform while collecting real-time physiological data from ambulatory wearable sensors. Results from the electrodermal activity (EDA) data analysis reveal patterns that correlate with known features of negative-emotions, indicating the possibility to infer service appropriateness from user’s reactions to a service, thereby closing an implicit-feedback loop for the CAIE.
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    Design of a Wearable Two-Dimensional Joystick as a Muscle-Machine Interface Using Mechanomyographic Signals
    Saha, Deba Pratim (Virginia Tech, 2013-11-12)
    Finger gesture recognition using glove-like interfaces are very accurate for sensing individual finger positions by employing a gamut of sensors. However, for the same reason, they are also very costly, cumbersome and unaesthetic for use in artistic scenarios such as gesture based music composition platforms like Virginia Tech's Linux Laptop Orchestra. Wearable computing has shown promising results in increasing portability as well as enhancing proprioceptive perception of the wearers' body. In this thesis, we present the proof-of-concept for designing a novel muscle-machine interface for interpreting human thumb motion as a 2-dimensional joystick employing mechanomyographic signals. Infrared camera based systems such as Microsoft Digits and ultrasound sensor based systems such as Chirp Microsystems' Chirp gesture recognizers are elegant solutions, but have line-of-sight sensing limitations. Here, we present a low-cost and wearable joystick designed as a wristband which captures muscle sounds, also called mechanomyographic signals. The interface learns from user's thumb gestures and finally interprets these motions as one of the four kinds of thumb movements. We obtained an overall classification accuracy of 81.5% for all motions and 90.5% on a modified metric. Results obtained from the user study indicate that mechanomyography based wearable thumb-joystick is a feasible design idea worthy of further study.
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    A Study of Methods in Computational Psychophysiology for Incorporating Implicit Affective Feedback in Intelligent Environments
    Saha, Deba Pratim (Virginia Tech, 2018-08-01)
    Technological advancements in sensor miniaturization, processing power and faster networks has broadened the scope of our contemporary compute-infrastructure to an extent that Context-Aware Intelligent Environment (CAIE)--physical spaces with computing systems embedded in it--are increasingly commonplace. With the widespread adoption of intelligent personal agents proliferating as close to us as our living rooms, there is a need to rethink the human-computer interface to accommodate some of their inherent properties such as multiple focus of interaction with a dynamic set of devices and limitations such as lack of a continuous coherent medium of interaction. A CAIE provides context-aware services to aid in achieving user's goals by inferring their instantaneous context. However, often due to lack of complete understanding of a user's context and goals, these services may be inappropriate or at times even pose hindrance in achieving user's goals. Determining service appropriateness is a critical step in implementing a reliable and robust CAIE. Explicitly querying the user to gather such feedback comes at the cost of user's cognitive resources in addition to defeating the purpose of designing a CAIE to provide automated services. The CAIE may, however, infer this appropriateness implicitly from the user, by observing and sensing various behavioral cues and affective reactions from the user, thereby seamlessly gathering such user-feedback. In this dissertation, we have studied the design space for incorporating user's affective reactions to the intelligent services, as a mode of implicit communication between the user and the CAIE. As a result, we have introduced a framework named CAfFEINE, acronym for Context-aware Affective Feedback in Engineering Intelligent Naturalistic Environments. The CAfFEINE framework encompasses models, methods and algorithms establishing the validity of the idea of using a physiological-signal based affective feedback loop in conveying service appropriateness in a CAIE. In doing so, we have identified methods of learning ground-truth about an individual user's affective reactions as well as introducing a novel algorithm of estimating a physiological signal based quality-metric for our inferences. To evaluate the models and methods presented in the CAfFEINE framework, we have designed a set of experiments in laboratory-mockups and virtual-reality setup, providing context aware services to the users, while collecting their physiological signals from wearable sensors. Our results provide empirical validation for our CAfFEINE framework, as well as point towards certain guidelines for conducting future research extending this novel idea. Overall, this dissertation contributes by highlighting the symbiotic nature of the subfields of Affective Computing and Context-aware Computing and by identifying models, proposing methods and designing algorithms that may help accentuate this relationship making future intelligent environments more human-centric.