SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–625 of 744 papers

TitleStatusHype
Personalized Activity Recognition with Deep Triplet EmbeddingsCode0
Classification of human activity recognition using smartphones—0
Improve Unsupervised Domain Adaptation with Mixup Training—0
Feature engineering workflow for activity recognition from synchronized inertial measurement unitsCode0
Kernel learning for visual perceptionCode0
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data—0
Template co-updating in multi-modal human activity recognition systems—0
Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach—0
Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition—0
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning—0
A systematic review of smartphone-based human activity recognition for health research—0
New Convex Relaxations for MRF Inference With Unknown Graphs—0
Generating Fair Universal Representations using Adversarial Models—0
Sign Language Recognition Analysis using Multimodal Data—0
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices—0
Human activity recognition from skeleton posesCode0
MEx: Multi-modal Exercises Dataset for Human Activity RecognitionCode0
Multi-task Self-Supervised Learning for Human Activity Detection—0
Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions—0
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing—0
Human Body Parts Tracking: Applications to Activity Recognition—0
An IoT Based Framework For Activity Recognition Using Deep Learning TechniqueCode0
Different Approaches for Human Activity Recognition: A Survey—0
Context-driven Active and Incremental Activity Recognition—0
SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks—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