Toward ubiquitous wearable robots enabled through data-driven AI systems

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  • Date
    5 March 2025
    Timeframe
    16:00 - 17:00
    Duration
    60 minutes
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    New advanced wearable exosuits are capable of restoring function to individuals in the older adult population by making it easier to walk and restoring normative biomechanics. An important function of these devices is to timely and accurately recognize user intent and optimize the control to provide biomechanically appropriate assistance across diverse human activities. Key challenges in the wearable robotics control community include generalizing control systems across a rich variety of real-world tasks and diverse individuals while simultaneously personalizing control systems to each individual’s specific set of biomechanical needs. Our research has focused on data-driven approaches using deep learning to tackle these challenges with applications in lower limb wearable robotics. This talk will examine approaches for AI-driven personalization of controllers to unique subjects and generalizing controllers across a rich variety of real-world tasks. New open-source datasets that we have generated to facilitate research in this area will also be briefly discussed.

    Learning Objectives:

    1. Give examples of the utility of wearable robotics for different societal needs
    2. Contrast different approaches for the successful integration of wearable lower limb exoskeletons
    3. Describe the value added of AI systems for enabling human capability

    Level of Mastery:

    Much of the talk will be accessible to anyone, but there are a few parts that will get more technical and require some reasonable background in CS or engineering to fully understand as some of the AI methods and device parameters will be explained in brief

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