A survey of depth and inertial sensor fusion for human action recognition

By Roozbeh Jafari, Nasser Kehtarnavaz. 2015

332 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

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Citations in the corpus

Expectation Learning for Adaptive Crossmodal Stimuli Association (2018, background): “straction for a perception task. However, these models typically rely on individual and independent mechanisms for processing unimodal representations where modalities do not inﬂuence each other [8], [9]. Neurophysiological ﬁndings evidence that different brain regions are activated and communicatin”
Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things (2020, background): “These results demonstrated that activity recognition techniques based on wearable sensors could work effectively for the low-level vision, but failed to handle high-level recognition tasks for complex activity recognition [22].”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “reviewed methods on depth-based HAR [35], [39], [40], [41].”
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 Convolutional Neural Network-Based Sensor Fusion System for Monitoring Transition Movements in Healthcare Applications (2018, background): “In general, as noted in [7], a single modality sensing cannot deal with various realistic situations that occur in practice.”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, background): “In the future, the use of sensor fusion techniques will span a wide range of applications [27].”
Elderly Fall Detection Systems: A Literature Survey (2020, background): “Based on the review provided by Chen et al. (2017a), individual depth cameras and inertial sensors seem to be the most signiﬁcant approaches in vision-and non-vision-based systems, respectively.”
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].”