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Poster
in
Workshop: Learning from Time Series for Health

Multi-modal 3D Human Pose Estimation using mmWave, RGB-D, and Inertial Sensors

Sizhe An · Yin Li · Umit Ogras


Abstract:

The ability to estimate 3D human body pose and movement, also known as human pose estimation~(HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few dataset exploits multiple modalities and focuses on home-based health monitoring. To bridge this gap, we present human pose estimation using multiple modalities with an in-house dataset. We perform extensive experiments and delineate the strength of each modality.

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