CLUMM: Contrastive Learning for Unobtrusive Motion Monitoring

Abstract excerpt

Traditional approaches for human monitoring and motion recognition often rely on wearable sensors, which, while effective, are obtrusive and cause significant discomfort to workers. More recent approaches have employed unobtrusive, real-time sensing using cameras mounted in the manufacturing environment. While these methods generate large volumes of rich data, they require extensive labeling and analysis for machi...

Topics

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• Aged
• Case-Control Studies
• Deep Learning
• Gait
• Gait Disorders, Neurologic
• Imaging, Three-Dimensional
• Neural Networks, Computer
• Parkinson Disease

Identifiers and source

Literature Corpus work

292914b9-5394-5ed5-8240-8e06edf3d5d8

DOI

10.20944/preprints202411.2059.v1