Sensors for Human Activity Recognition

Human activity recognition (HAR) and human behavior recognition (HBR) play increasingly important roles in the digital age. High-quality sensory observations applicable to recognizing users' activities and behaviors, including electrical, magnetic, mechanical (kinetic), optical, acoustic, therm...

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Bibliographic Details
Other Authors: Liu, Hui (Editor), Gamboa, Hugo (Editor), Schultz, Tanja (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
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Online Access:DOAB: download the publication
DOAB: description of the publication
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520 |a Human activity recognition (HAR) and human behavior recognition (HBR) play increasingly important roles in the digital age. High-quality sensory observations applicable to recognizing users' activities and behaviors, including electrical, magnetic, mechanical (kinetic), optical, acoustic, thermal, and chemical biosignals, are inseparable from sensors' sophisticated design and appropriate application. Traditional sensors suitable for HAR and HBR, including external sensors for smart homes, optical sensors such as cameras for capturing video signals, and bioelectrical, biomagnetic, and biomechanical sensors for wearable applications, have been studied and verified adequately. They continue to be researched in depth for more effective and efficient usage, and brand-new areas facilitated by sensor-based HAR/HBR are emerging, such as interactive edutainment, single-motion duration analysis, time series information retrieval, handcrafted and high-level feature design, and fall detection. Meanwhile, innovative sensor research for HAR or HBR is also very active in the academic community, including new sensors appropriate for HAR/HBR, new designs and applications of the above-mentioned traditional sensors, and the usage of non-traditional HAR-/HBR-related sensor types, among others. 
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650 7 |a Medical equipment & techniques  |2 bicssc 
653 |a human activity recognition 
653 |a validation methodology 
653 |a leave-one-subject-out cross-validation 
653 |a explainable methods 
653 |a Shapley additive explanations 
653 |a machine learning 
653 |a camera calibration 
653 |a point-to-point camera distortion calibration 
653 |a vision-based human activity recognition 
653 |a speckle pattern 
653 |a digital image correlation 
653 |a sensing technique 
653 |a HAR 
653 |a semi-supervised learning 
653 |a adversarial learning 
653 |a syn-LSTM 
653 |a smart home 
653 |a Wi-Fi sensing 
653 |a multi-location 
653 |a deep complex network 
653 |a ankle-foot orthoses 
653 |a energy consumption 
653 |a functional electrical stimulation 
653 |a human in the loop 
653 |a smart retail 
653 |a in-store camera 
653 |a customer behavior recognition 
653 |a behavior reconstruction 
653 |a indoor localization 
653 |a pedestrian dead reckoning 
653 |a deep learning 
653 |a GPS 
653 |a domain generalization 
653 |a accelerometer 
653 |a psychological stress 
653 |a electrocardiogram 
653 |a heart rate variability 
653 |a gated recurrent unit 
653 |a VR high-altitude experiment 
653 |a wearable devices 
653 |a n/a 
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