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Creators/Authors contains: "Rai, Vijeth"

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  1. ; ; ; ; ; ; (, Scientific Data)
    Abstract In this manuscript, we describe a unique dataset of human locomotion captured in a variety of out-of-the-laboratory environments captured using Inertial Measurement Unit (IMU) based wearable motion capture. The data contain full-body kinematics for walking, with and without stops, stair ambulation, obstacle course navigation, dynamic movements intended to test agility, and negotiating common obstacles in public spaces such as chairs. The dataset contains 24.2 total hours of movement data from a college student population with an approximately equal split of males to females. In addition, for one of the activities, we captured the egocentric field of view and gaze of the subjects using an eye tracker. Finally, we provide some examples of applications using the dataset and discuss how it might open possibilities for new studies in human gait analysis. 
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  2. ; ; ; ; (, IEEE Access)
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  3. ; ; ; (, 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob))
    null (Ed.)
    Data-driven gait prediction can provide a reference trajectory for a wide variety of simple and complex movements captured in the training data. Coordinated Movement (CM) is a data-driven approach that maps movements of the body to movements of target joints, such as the ankle and knee. We have previously shown that the performance of CM for complex activities can be improved by adding more training data. In this paper we demonstrate that performance can also be improved by 1) including a history of the target joint angles as inputs to the model and 2) dynamic reallocation of the importance of the inputs over time using a neural network technique called Attention. These modifications are applicable when additional training data is limited. We also observe that Attention can follow important events in gait over time, adding interpretability to the system. 
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