SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 676–700 of 744 papers

TitleStatusHype
Continuous Human Activity Recognition using a MIMO Radar for Transitional Motion Analysis—0
Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR—0
Contrastive Predictive Coding for Human Activity Recognition—0
ConViViT -- A Deep Neural Network Combining Convolutions and Factorized Self-Attention for Human Activity Recognition—0
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition—0
Creating a Large-scale Synthetic Dataset for Human Activity Recognition—0
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator—0
Cross-Country Skiing Gears Classification using Deep Learning—0
Cross-domain Activity Recognition via Substructural Optimal Transport—0
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition—0
Cross-position Activity Recognition with Stratified Transfer Learning—0
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks—0
Cross-user activity recognition using deep domain adaptation with temporal relation information—0
Cross-user activity recognition via temporal relation optimal transport—0
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
CSI-Based Cross-Domain Activity Recognition via Zero-Shot Prototypical Networks—0
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network—0
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors—0
Dataset Bias in Human Activity Recognition—0
Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition—0
Deep Activity Recognition Models with Triaxial Accelerometers—0
Deep Adaptive Temporal Pooling for Activity Recognition—0
Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition—0
Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables—0
Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition—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