SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–425 of 744 papers

TitleStatusHype
Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study—0
Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding—0
Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs—0
Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models—0
Feature Learning for Interaction Activity Recognition in RGBD Videos—0
Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition—0
Federated Continual Learning through distillation in pervasive computing—0
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain—0
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring—0
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR—0
Federated Split Learning for Human Activity Recognition with Differential Privacy—0
Federated Unlearning for Human Activity Recognition—0
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring—0
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning—0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Few Shot Activity Recognition Using Variational Inference—0
Few-Shot Learning-Based Human Activity Recognition—0
Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection—0
FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors—0
Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data—0
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk—0
FLAME: Federated Learning Across Multi-device Environments—0
FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments—0
FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires—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