SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 744 papers

TitleStatusHype
Classifying Human Activities using Machine Learning and Deep Learning Techniques—0
FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires—0
Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition—0
Classification of human activity recognition using smartphones—0
Self-supervised New Activity Detection in Sensor-based Smart Environments—0
Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition—0
A distillation-based approach integrating continual learning and federated learning for pervasive services—0
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
Classifying Human Activities with Inertial Sensors: A Machine Learning Approach—0
FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments—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
CMD-HAR: Cross-Modal Disentanglement for Wearable 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
FLOW: Fusing and Shuffling Global and Local Views for Cross-User Human Activity Recognition with IMUs—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