SOTAVerified

Human Activity Recognition

Classify various human activities

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–450 of 744 papers

TitleStatusHype
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
Poselet Key-Framing: A Model for Human Activity Recognition—0
Pose Uncertainty Aware Movement Synchrony Estimation via Spatial-Temporal Graph Transformer—0
PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation—0
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps—0
Privacy in Multimodal Federated Human Activity Recognition—0
Privacy-Preserving Eye-tracking Using Deep Learning—0
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution—0
Probing Fine-Grained Action Understanding and Cross-View Generalization of Foundation Models—0
Process-aware Human Activity Recognition—0
Process Optimization and Deployment for Sensor-Based Human Activity Recognition Based on Deep Learning—0
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning—0
Provable Robustness for Streaming Models with a Sliding Window—0
Provably Secure Federated Learning against Malicious Clients—0
randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning—0
Random Projections and Natural Sparsity in Time-Series Classification: A Theoretical Analysis—0
RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing—0
Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection—0
Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices—0
Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning—0
RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics—0
Recognize Human Activities from Partially Observed Videos—0
Redundant feature screening method for human activity recognition based on attention purification mechanism—0
ReHAR: Robust and Efficient Human Activity Recognition—0
Reshaping Visual Datasets for Domain Adaptation—0
Resource-Eficient Continual Learning for Sensor-Based Human Activity Recognition—0
RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices—0
Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning—0
Robust Automated Human Activity Recognition and its Application to Sleep Research—0
Robust Multimodal Fusion for Human Activity Recognition—0
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition—0
rTsfNet: a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition—0
rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition—0
ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs—0
Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques—0
Scaling laws in wearable human activity recognition—0
Seeker: Synergizing Mobile and Energy Harvesting Wearable Sensors for Human Activity Recognition—0
Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar—0
SegTime: Precise Time Series Segmentation without Sliding Window—0
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Self-Supervised Human Activity Recognition by Augmenting Generative Adversarial 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