A Real-Time Human Action Recognition System Using Depth and Inertial Sensor Fusion

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RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, background): “The data modalities (the fifth column) include color, depth, skeleton and accelerometer, which are helpful for researchers to quickly identify the datasets especially when they work on multi-modal fusion [15, 16, 58].”
Human Action Recognition From Various Data Modalities: A Review (2020, background): “In their subsequent work [384], the depth image features and acceleration signal”
Enhanced skeleton visualization for view invariant human action recognition (2017, methodology): “With rapid advances of imaging technology in capturing depth information in real-time, there has been a growing interest in solving these problems by using depth data generated from depth sensors [6–11] , particularly the cost-effective Microsoft Kinect sensor [12] .”
3D action recognition using data visualization and convolutional neural networks (2017, background): “The development of RGB-D cameras, in particular the Kinect [5], opens up opportunities in addressing above problems [6, 7, 8, 9, 10].”
Fusion of depth, skeleton, and inertial data for human action recognition (2016, background): “A database consisting of 10 actions performed by 6 subjects is put together to carry out two types of testing of the developed fusion approach: subject-generic and subject-specific.”