SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 476–500 of 744 papers

TitleStatusHype
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data—0
Human activity recognition using deep learning approaches and single frame cnn and convolutional lstm—0
Human activity recognition using improved dynamic image—0
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey—0
Human Activity Recognition Using LSTM-RNN Deep Neural Network Architecture—0
Human Activity Recognition Using Multichannel Convolutional Neural Network—0
Human Activity Recognition using Recurrent Neural Networks—0
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning—0
Human Activity Recognition Using Self-Supervised Representations of Wearable Data—0
Human Activity Recognition using Smartphone—0
Human Activity Recognition using Smartphones—0
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects—0
Human Activity Recognition with a 6.5 GHz Reconfigurable Intelligent Surface for Wi-Fi 6E—0
Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Semi-supervised Cross-domain Neural Networks for Indoor Environment—0
Human Body Parts Tracking: Applications to Activity Recognition—0
Human Interaction Recognition Framework based on Interacting Body Part Attention—0
Human Pose Estimation using Motion Priors and Ensemble Models—0
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data—0
iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets—0
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition—0
Impact of Physical Activity on Sleep:A Deep Learning Based Exploration—0
Importance of user inputs while using incremental learning to personalize human activity recognition models—0
Improve Unsupervised Domain Adaptation with Mixup Training—0
Improving Human Activity Recognition Through Ranking and Re-ranking—0
IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video 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