Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition

By Kui Liu, Cheng Chen, Roozbeh Jafari, Nasser Kehtarnavaz. 2014

159 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Control and Systems Engineering, Human-Computer Interaction

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Citations in the corpus

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].”
Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors (2015, methodology): “In [18], a Hidden Markov Model (HMM) classifier was used for hand gesture recognition with raw data from both a Kinect depth camera and an inertial body sensor (position data of the hand joint from a Kinect depth camera, as well as acceleration data and angular velocity data from an inertial body se”
UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor (2015, methodology): “In [11, 12], data from a depth camera and an inertial sensor were fused within the framework of an HMM for robust hand gesture recognition.”
Time synchronization and data fusion for RGB-Depth cameras and inertial sensors in AAL applications (2015, methodology): “[6] implement a hand gesture recognition system using a similar setup, provided as input to an hidden Markov model.”
A Convolutional Neural Network-Based Sensor Fusion System for Monitoring Transition Movements in Healthcare Applications (2018, methodology): “Several action recognition sensor fusion systems have been developed by our research group in [8-12] by using two different modality sensors at the same time in order to achieve robust recognition under realistic operating conditions.”
3D skeleton-based human action classification: A survey (2016, methodology): “In a similar way, in [144,145] measurements from accelerometers and skeletal data are concatenated and used to classify various activities.”
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.”
A survey of depth and inertial sensor fusion for human action recognition (2015, methodology): “[43] Depth and inertial sensor data were concatenated.”