SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

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Showing 126–150 of 744 papers

TitleStatusHype
A Tiny Supervised ODL Core with Auto Data Pruning for Human Activity Recognition—0
Skeleton-Based Action Recognition with Spatial-Structural Graph ConvolutionCode0
FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy InsightsCode0
Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment—0
C3T: Cross-modal Transfer Through Time for Sensor-based Human Activity Recognition—0
Probing Fine-Grained Action Understanding and Cross-View Generalization of Foundation Models—0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering—0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices—0
VCHAR:Variance-Driven Complex Human Activity Recognition framework with Generative Representation—0
Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing DataCode0
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge TransferCode0
Mask and Compress: Efficient Skeleton-based Action Recognition in Continual LearningCode0
Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition—0
Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models—0
Leveraging LDA Feature Extraction to Augment Human Activity Recognition AccuracyCode0
Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes—0
Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models—0
EarDA: Towards Accurate and Data-Efficient Earable Activity Sensing—0
Unsupervised explainable activity prediction in competitive Nordic Walking from experimental data—0
Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition—0
Large Language Models Memorize Sensor Datasets! Implications on Human Activity Recognition Research—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
MuJo: Multimodal Joint Feature Space Learning for Human 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