SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 576–600 of 744 papers

TitleStatusHype
On Attention Models for Human Activity Recognition—0
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge—0
To Store or Not? Online Data Selection for Federated Learning with Limited Storage—0
Online Human Activity Recognition Employing Hierarchical Hidden Markov Models—0
Online Human Activity Recognition using Low-Power Wearable Devices—0
On the Benefit of Generative Foundation Models for Human Activity Recognition—0
On the recognition of the game type based on physiological signals and eye tracking—0
OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments—0
Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications—0
Optimized Gated Deep Learning Architectures for Sensor Fusion—0
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models—0
Overview of Human Activity Recognition Using Sensor Data—0
P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer—0
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology—0
Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task—0
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion—0
Personalization in Human Activity Recognition—0
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework—0
Personalized Human Activity Recognition Using Convolutional Neural Networks—0
Personalized Semi-Supervised Federated Learning for Human Activity Recognition—0
Personalizing human activity recognition models using incremental learning—0
Phase-driven Domain Generalizable Learning for Nonstationary Time Series—0
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers—0
PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition—0
Pose-conditioned Spatio-Temporal Attention for Human Action 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