Distributed recognition of human actions using wearable motion sensor networks

By Allen Yuqing Yang, Roozbeh Jafari, S. Sastry, Ruzena Bajcsy. 2009

224 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Lifelong Graph Learning (2020, positive): “distributed human action recognition using wearable motion sensor networks in Figure 2a (Data from [55]).”
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors (2012, methodology): “The WARD (wearable action recognition database) dataset developed by the University of California, Berkeley (UC Berkeley) consists of continuous sequences of human activities measured by a network of wearable sensors [23].”
Sparse Representation for Computer Vision and Pattern Recognition (2010, background): “Moreover, while in this section we have focused on the interplay between theory and practice in one particular application, face recognition, similar ideas have seen application on a number of problems in and even beyond vision, e.g., in sensor networks and human activity classification [ 74 ] as we”
A Convolutional Neural Network-Based Sensor Fusion System for Monitoring Transition Movements in Healthcare Applications (2018, methodology): “The inertial sensor used here is a small wireless wearable sensor reported in [14] which is capable of generating 3-axis acceleration signals and 3-axis angular velocity signals.”
A Survey on Ambient Intelligence in Health Care (2013, unknown): “In the case of wearable sensors, sensors are attached to the body [37] or woven into garments [38], [39].”
Inverse Dynamics for Action Recognition (2013, background): “The main advantage of the volume-based approach is that it is not necessary to build complex models of body configuration and kinematics.”
UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor (2015, background): “, UC Berkeley Wearable Action Recognition Database (WARD) [9], and USC Human Activity Dataset (HAD) [10]).”
A survey of depth and inertial sensor fusion for human action recognition (2015, background): “To address such challenges, many researchers have utilized wearable inertial sensors incorporating accelerometers and gyroscopes, e.g., [34, 72].”
Kernel Cross-Correlator (2017, background): “Dataset The public wearable action recognition database (WARD) (Yang et al. 2009) is chosen.”
Fusion of depth, skeleton, and inertial data for human action recognition (2016, methodology): “Furthermore, the skeleton data was not utilized due to jitters in skeleton joint positions in the Kinect v1 sensor.”