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Sarah Ostadabbas
Northeastern University
70Publications
10H-index
496Citations
Publications 69
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#1Aya Khalaf (University of Pittsburgh)H-Index: 4
#2Mohsen Nabian (NU: Northeastern University)H-Index: 4
Last.Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
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Abstract Challenge and threat characterize distinct patterns of physiological response to a motivated performance task where the response patterns vary as a function of an individual's evaluation of task demands relative to his/her available resources to cope with the demands. Challenge and threat responses during motivated performance have been used to understand psychological, behavioral, and biological phenomena across many motivated performance domains. In this study, we aimed to investigate...
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#1Shuangjun Liu (NU: Northeastern University)H-Index: 3
#2Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
Human in-bed pose estimation has huge practical values in medical and healthcare applications yet still mainly relies on expensive pressure mapping (PM) solutions. In this paper, we introduce our novel physics inspired vision-based approach that addresses the challenging issues associated with the in-bed pose estimation problem including monitoring a fully covered person in complete darkness. We reformulated this problem using our proposed Under the Cover Imaging via Thermal Diffusion (UCITD) me...
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Objective monitoring and assessment of human motor behavior can improve the diagnosis and management of several medical conditions. Over the past decade, significant advances have been made in the use of wearable technology for continuously monitoring human motor behavior in free-living conditions. However, wearable technology remains ill-suited for applications which require monitoring and interpretation of complex motor behaviors (e.g., involving interactions with the environment). Recent adva...
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#2Shuangjun LiuH-Index: 3
Last.Swastik KarH-Index: 28
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Despite its ability to draw precise inferences from large and complex data sets, the use of data analytics in the field of condensed matter and materials sciences—where vast quantities of complex metrology data are regularly generated—has remained surprisingly limited. Specifically, such approaches could dramatically reduce the engineering complexities of devices that directly exploit the physical properties of materials. Here, we present a cyber-physical system for accurately estimating the wav...
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#1Xiaofei HuangH-Index: 1
Last.Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
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Non-nutritive sucking (NNS) is defined as the sucking action that occurs when a finger, pacifier, or other object is placed in the baby's mouth, but there is no nutrient delivered. In addition to providing a sense of safety, NNS even can be regarded as an indicator of infant's central nervous system development. The rich data, such as sucking frequency, the number of cycles, and their amplitude during baby's non-nutritive sucking is important clue for judging the brain development of infants or ...
#2Shuangjun LiuH-Index: 3
Last.Swastik KarH-Index: 28
view all 4 authors...
Despite its ability to draw precise inferences from large and complex datasets, the use of data analytics in the field of condensed matter and materials sciences -- where vast quantities of complex metrology data are regularly generated -- has remained surprisingly limited. Specifically, such approaches could dramatically reduce the engineering complexities of devices that directly exploit the physical properties of materials. Here, we present a cyber-physical system for accurately estimating th...
#1Amirreza Farnoosh (NU: Northeastern University)H-Index: 2
#2Behnaz Rezaei (NU: Northeastern University)H-Index: 3
Last.Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
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This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework. Due to the redundant nature of the backgrounds in surveillance videos, visual information of the background can be compressed into a low-dimensional subspace in the encoder part of the variational autoencoder, while the highly variant information of its moving foreground gets filtered throughout its encoding-decoding process. Our deep probabil...
2 Citations
#1Chun-An Chou (NU: Northeastern University)
#2Xiaoning Jin (NU: Northeastern University)H-Index: 12
Last.Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
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The exploding availability of new data streams now enables insights to be garnered through the integration (fusion) of multiple data sources (modalities); however, currently, it remains difficult to predict a priori which multimodal data fusion (MMDF) methods and architectures will be best suited for a novel application, leading to trial-and-error approaches that are inefficient in both time and cost. Although MMDF strategies are being applied ad hoc in many different fields (e.g., healthcare, a...
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#1Neha Dawar (UTD: University of Texas at Dallas)H-Index: 4
#2Sarah Ostadabbas (NU: Northeastern University)H-Index: 10
Last.Nasser Kehtarnavaz (UTD: University of Texas at Dallas)H-Index: 32
view all 3 authors...
This article covers a deep learning-based decision fusion approach for action or gesture recognition via simultaneous utilization of a depth camera and a wearable inertial sensor. The deep learning approach involves using a convolutional neural network (CNN) for depth images captured by a depth camera and a combination of CNN and long short–term memory network for inertial signals captured by a wearable inertial sensor, followed by a decision-level fusion. Due to the limited size of the training...
4 CitationsSource
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