SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 426–450 of 744 papers

TitleStatusHype
A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation—0
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity RecognitionCode1
Video2IMU: Realistic IMU features and signals from videos—0
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects—0
DIAT-μ RadHAR (micro-doppler signature dataset) & μ RadNet (a lightweight DCNN)—For human suspicious activity recognition—0
DIAT-RadHARNet: A lightweight DCNN for radar based classification of human suspicious activities—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs—0
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning—0
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data—0
Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition—0
A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian MutationCode0
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks—0
Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals—0
Accoustate: Auto-annotation of IMU-generated Activity Signatures under Smart InfrastructureCode0
SSDL: Self-Supervised Dictionary Learning—0
Human Activity Recognition Using 3D Orthogonally-projected EfficientNet on Radar Time-Range-Doppler Signature—0
Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart HomesCode0
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps—0
Classifying Human Activities with Inertial Sensors: A Machine Learning Approach—0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition—0
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances—0
RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity RecognitionCode1
Human Activity Recognition using Attribute-Based Neural Networks and Context Information—0
Adversarial Deep Feature Extraction Network for User Independent 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