SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 526–550 of 744 papers

TitleStatusHype
Human Activity Recognition Using Multichannel Convolutional Neural Network—0
A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity RecognitionCode0
A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network—0
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition—0
SkipW: Resource adaptable RNN with strict upper computational limit—0
Deep Positive Unlabeled Learning with a Sequential Bias—0
Invariant Feature Learning for Sensor-based Human Activity Recognition—0
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring—0
Contrastive Predictive Coding for Human Activity Recognition—0
Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks—0
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring—0
MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection—0
A compact sequence encoding scheme for online human activity recognition in HRI applications—0
LaHAR: Latent Human Activity Recognition using LDACode0
Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos—0
Human activity recognition using improved dynamic image—0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition—0
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous DataCode0
Semi-supervised Federated Learning for Activity Recognition—0
A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition—0
Bubblenet: A Disperse Recurrent Structure To Recognize Activities—0
Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion—0
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network—0
Attention-Driven Body Pose Encoding for Human Activity Recognition—0
Semi-supervised sequence classification through change point detection—0
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All datasetsRHMOAD datasetPAMAP2HARHMDB51MM-FitRadar Dataset (DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition)UCF-101

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Dual-Stream C3DAccuracy (Top-1)71.06—Unverified
2C3DAccuracy (Top-1)70.3—Unverified
3Dual-Stream ConvNetAccuracy (Top-1)62.77—Unverified
4SlowFast (101)Accuracy (Top-1)45.28—Unverified
#ModelMetricClaimedVerifiedStatus
1ESTIE + VGG16 (transfer-learning)Accuracy95.22—Unverified
2STIE + VGG16 (transfer-learning)Accuracy94.77—Unverified
3STIE + VGG16(fine-tuning)Accuracy86.81—Unverified
#ModelMetricClaimedVerifiedStatus
1AFVFAccuracy0.97—Unverified
2Selective HAR ClusteringNMI0.88—Unverified
3Unsupervised embedding learning for human activity recognition using wearable sensor dataNMI0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1LMSSAccuracy1—Unverified
2AFVFAccuracy0.99—Unverified
#ModelMetricClaimedVerifiedStatus
1Label-RankerAccuracy61.18—Unverified
#ModelMetricClaimedVerifiedStatus
1unsupervised statistical feature guided diffusion modelF1 - macro0.44—Unverified
#ModelMetricClaimedVerifiedStatus
1DIAT-RadHARNet1:1 Accuracy99.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Label-RankerAccuracy89.5—Unverified