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  • PhD Defense - Jens Windau

    Thu, Apr 18, 2019 @ 03:00 PM - 05:00 PM

    Computer Science

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    Ph.D. Defense - Jens Windau
    Thu, April 18, 2019
    3:00 PM - 4:30 PM
    Location: MCB 102

    Title:
    Smart Monitoring and Autonomous Situation Classification of Humans and Machines

    PhD Candidate: Jens Windau
    Date, Time, and Location: Thursday, April 18, 2019 at 3:00 pm in MCB 102
    Committee: Prof. Laurent Itti (chair), Prof. Bartlett Mel, and Prof. Hao Li

    Abstract:

    Emerging wearable and cloud-connected sensor technologies offer new sensor placement options on the human body and machines. This opens new opportunities to explore cyber robotics algorithms (sensors and human motor plant) and smart manufacturing algorithms (sensors and manufacturing equipment). These algorithms process motion sensor data and provide situation awareness for a wide range of applications. Smart management and training systems assist humans in day-to-day living routines, healthcare and sports. Machines benefit from smart monitoring in manufacturing, retail machinery, transportation, and construction safety. During my PhD Research, I have developed several approaches for motion analysis and classification. (1) A situation awareness system (SAS) for head-mounted smartphones to respond to user activities (e.g., disable incoming phone calls in elevators, activate video recording while car driving), (2) a filter for head-mounted sensors (HOS) to allow full-body motion capturing by removing interfering head-motions, (3) an Inertial Machine Monitoring System (IMMS) to detect equipment failure or degraded states of a 3D-Printer, and (4) a "Smart Teaching System" (STS) for targeted motion feedback to refine physical tasks. To capture real-world sensor data, we designed hardware prototypes or used state-of-the-art wearable technology. We developed novel sensor fusion algorithms, implemented feature extraction methods based on gist, statistics, physics, frequency diagrams and validated classifiers: SAS achieved high accuracy (81.5%) when distinguishing between 20 real-world activities. HOS reduced the positional error of a traveled distance below 2.5 % with head-mounted sensors for pedestrian dead reckoning applications. IMMS yielded 11-way classification accuracy over 99% when distinguishing between normal operation vs. 10 types of real-world abnormal equipment behavior. STS demonstrated that combining motion sensors and provide targeted feedback yield significantly improved golf swing training (3.7x increased performance score).

    Location: 102

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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