The "Robust Motion Recognition Based on Sensor Technology" Special Issue aims to bring together recent research on motion recognition using sensor technology. Sensor-based motion recognition has become increasingly important in many domains, such as healthcare, sports, and robotics, as it enables the collection and analysis of accurate and reliable motion data. In fact, it is possible to model motion through different sensor technologies, including signals from inertial and physiological sensors embedded in wearables or smart devices, or images and video frames from cameras.
This Special Issue covers a wide range of topics, including signal processing techniques, architectures, learning algorithms, intelligent sensing systems, wearable sensors, machine/deep learning and artificial intelligence in sensing and imaging, and their application for motion modeling and recognition.
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Hardcover. Condition: new. Hardcover. The "Robust Motion Recognition Based on Sensor Technology" Special Issue aims to bring together recent research on motion recognition using sensor technology. Sensor-based motion recognition has become increasingly important in many domains, such as healthcare, sports, and robotics, as it enables the collection and analysis of accurate and reliable motion data. In fact, it is possible to model motion through different sensor technologies, including signals from inertial and physiological sensors embedded in wearables or smart devices, or images and video frames from cameras.This Special Issue covers a wide range of topics, including signal processing techniques, architectures, learning algorithms, intelligent sensing systems, wearable sensors, machine/deep learning and artificial intelligence in sensing and imaging, and their application for motion modeling and recognition. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9783725836758
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Hardcover. Condition: new. Hardcover. The "Robust Motion Recognition Based on Sensor Technology" Special Issue aims to bring together recent research on motion recognition using sensor technology. Sensor-based motion recognition has become increasingly important in many domains, such as healthcare, sports, and robotics, as it enables the collection and analysis of accurate and reliable motion data. In fact, it is possible to model motion through different sensor technologies, including signals from inertial and physiological sensors embedded in wearables or smart devices, or images and video frames from cameras.This Special Issue covers a wide range of topics, including signal processing techniques, architectures, learning algorithms, intelligent sensing systems, wearable sensors, machine/deep learning and artificial intelligence in sensing and imaging, and their application for motion modeling and recognition. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9783725836758
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The 'Robust Motion Recognition Based on Sensor Technology' Special Issue aims to bring together recent research on motion recognition using sensor technology. Sensor-based motion recognition has become increasingly important in many domains, such as healthcare, sports, and robotics, as it enables the collection and analysis of accurate and reliable motion data. In fact, it is possible to model motion through different sensor technologies, including signals from inertial and physiological sensors embedded in wearables or smart devices, or images and video frames from cameras.This Special Issue covers a wide range of topics, including signal processing techniques, architectures, learning algorithms, intelligent sensing systems, wearable sensors, machine/deep learning and artificial intelligence in sensing and imaging, and their application for motion modeling and recognition. Seller Inventory # 9783725836758
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