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Piecewise Linear Dynamical Model for Actions Clustering from Inertial Body Sensors with Considerations of Human Factors Jiaqi Gong1, Philip Asare1,2, John Lach1, Yanjun Qi2 Charles L. Brown Department of Electrical and C
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Document Date: 2014-12-26 15:05:01


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File Size: 1,11 MB

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City

Motion Primitive / San Diego / /

Company

Accelerometer-Based Real-Time Activity Recognition Systems / Inertial Body Sensor Networks / Shimizu / Body Sensor Networks / Intelligent Transportation Systems / Body Area Networks / /

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Event

Labor Issues / /

Facility

Computer Science University of Virginia / /

IndustryTerm

non-linear dynamical systems / potential applications / Wireless Health / healthcare professionals / sensor networks / activity analysis algorithms / signal processing methods / long-term health monitoring systems / motion recognition systems / Typical processing pipeline / neural networks / short-term data processing / temporal-scale applications / nonlinear signal processing methods / motion stimulus detection algorithm / signal processing pipeline / dynamical systems / activity recognition systems / /

MarketIndex

set 1000 / /

Organization

Yanjun Qi2 Charles L. Brown Department of Electrical and Computer Engineering / University of Virginia / Pattern Analysis and Machine Intelligence / /

Person

Myla D. Goldman / Asma Qureshi / Dawei Fan / Shanshan Chen / Charles L. Brown / /

ProgrammingLanguage

C / /

PublishedMedium

IEEE Transactions on Pattern Analysis and Machine Intelligence / /

Technology

MEMS / t-1 / Neural Network / motion stimulus EM Algorithm / GPS / activity analysis algorithms / Gesture Recognition / EM algorithm / Terms Algorithms / motion stimulus detection algorithm / /

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