UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor

By Cheng Chen, Roozbeh Jafari, Nasser Kehtarnavaz. 2015

772 citations recorded by the source corpus.

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

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Citations in the corpus

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “1 - - - - - - - - Y N N [61] Sound classificatio n, activity recognition Microphone, magnetic kinematic sensor data Gaussian Mixture Model classifier Testing data from 15 actors N N 72 - - - - - - - - N Y N [34] Fall detection Acoustical, medical, infrared sensor data Fuzzy Logic HOMECAD [77] Y N 95”
RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “The curves under the images are the accelerometer data at 50 Hz of devices attached to the knife, the mixing spoon, the small spoon, the peeler, the glass, the oil bottle, and the pepper dispenser mentioned datasets above, another 26 extra RGB-D datasets for different applications are also added int”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “Many other datasets captured using a Kinect v1 camera were also released to the public, including the MSR Daily Activity 3D [121], MSR Action Pairs [113], Online RGBD Action (ORGBD) [107], UTKinect-Action [124], Florence 3D-Action [118], CMU-MAD [104], UTD-MHAD [103], G3D/G3Di [105], [119], SPHERE [”
PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding (2017, neutral): “MSR-Action3D [17] 20 567 567 1 D+Skeleton No 2010 RGBD-HuDaAct [21] 13 1189 1189 1 RGB+D No 2011 MSR-DailyActivity [43] 16 320 320 1 RGB+D+Skeleton No 2012 Act4 [8] 14 6844 6844 1 RGB+D No 2012 UTKinect-Action [53] 10 200 200 1 RGB+D+Skeleton No 2012 3D Action Pairs [23] 12 360 360 1 RGB+D+Skeleton ”
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep CNN (2017, methodology): “For this dataset, cross-subjects protocol was adopted as in [30], namely, the data from the subjects numbered 1, 3, 5, 7 were used for training while subjects 2, 4, 6, 8 were used for testing.”
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation (2017, methodology): “There are 10 common actions between MSRAction3DExt and UTD-MHAD: wave, hand catch, right arm high throw, draw x, draw circle, two hand front clap, jogging, tennis swing, tennis serve, and pickup and throw.”
Structured Images for RGB-D Action Recognition (2017, methodology): “For this dataset, cross-subjects protocol is adopted as in [4], namely, the data from the subject numbers 1, 3, 5, 7 used for training while 2, 4, 6, 8 used for testing.”
A survey of depth and inertial sensor fusion for human action recognition (2015, methodology): “The UTD-MHAD dataset 4 [12] is a comprehensive multimodal human action dataset that consists of data from a Kinect sensor and a wearable inertial sensor capturing 3-axis acceleration and 3-axis angular velocity signals [17].”
A Convolutional Neural Network-Based Sensor Fusion System for Monitoring Transition Movements in Healthcare Applications (2018, methodology): “images and inertial signals from the Kinect depth camera and the inertial sensor, respectively, were collected simultaneously and synchronized using the time stamp scheme discussed in [9].”
Elderly Fall Detection Systems: A Literature Survey (2020, background): “(2017) divided falls into a broader set of categories, namely forward, backward, left-side, right-side, blinded-forward, and blinded-backward, and in the study by Chen et al. (2018) falls are grouped in more specific categories including fall lateral left lie on the floor, fall lateral left and sit ”
Spatially and Temporally Structured Global to Local Aggregation of Dynamic Depth Information for Action Recognition (2018, methodology): “The proposed representation is evaluated on six benchmark data sets, namely,MSRAction3D, G3D,MSRDailyActivity3D, SYSU 3D HOI, UTD-MHAD, and M2I data sets and achieves the state-of-the-art results on all six data sets.”
RGBD Datasets: Past, Present and Future (2016, background): “Other datasets contain labels appropriate for tracking: two semantic scene datasets [112, 62] have static objects labeled through video as the camera moves, while the 6-DOF object pose annotations in [45] could also be useful.”