Imagen de Portada

Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Bian, Sizhen, Rey, Vitor Fortes, Yuan, Siyu, Lukowicz, Paul
Formato:	Preprint
Publicado:	2025
Materias:	Signal Processing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Collaborative Human Activity Recognition with Passive Inter-Body Electrostatic Field
por: Bian, Sizhen, et al.
Publicado: (2025)

Passive Body-Area Electrostatic Field (Human Body Capacitance) for Ubiquitous Computing
por: Bian, Sizhen, et al.
Publicado: (2025)

Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey
por: Bian, Sizhen, et al.
Publicado: (2024)

Earable and Wrist-worn Setup for Accurate Step Counting Utilizing Body-Area Electrostatic Sensing
por: Bian, Sizhen, et al.
Publicado: (2024)

Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook
por: Bian, Sizhen, et al.
Publicado: (2026)

Fully Automatic Gym Exercises Recording: An IoT Solution
por: Bian, Sizhen, et al.
Publicado: (2023)

iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets
por: Liu, Mengxi, et al.
Publicado: (2024)

Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study
por: Liu, Mengxi, et al.
Publicado: (2025)

KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition
por: Liu, Mengxi, et al.
Publicado: (2026)

Optimization of An Induced Magnetic Field-Based Positioning System
por: Bian, Sizhen, et al.
Publicado: (2025)

Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition
por: Zuo, Si, et al.
Publicado: (2023)

iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition
por: Liu, Mengxi, et al.
Publicado: (2024)

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
por: Bian, Sizhen, et al.
Publicado: (2025)

Calibration-Free Induced Magnetic Field Indoor and Outdoor Positioning via Data-Driven Modeling
por: Guo, Qiushi, et al.
Publicado: (2026)

Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition
por: Liu, Mengxi, et al.
Publicado: (2024)

Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition
por: Zolfaghari, Parham, et al.
Publicado: (2024)

FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors
por: Liu, Mengxi, et al.
Publicado: (2023)

Evaluation of Encoding Schemes on Ubiquitous Sensor Signal for Spiking Neural Network
por: Bian, Sizhen, et al.
Publicado: (2024)

From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation
por: Liu, Mengxi, et al.
Publicado: (2025)

Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR
por: Ray, Lala Shakti Swarup, et al.
Publicado: (2024)

CycloWatt: An Affordable, TinyML-enhanced IoT Device Revolutionizing Cycling Power Metrics
por: Luder, Victor, et al.
Publicado: (2024)

PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition
por: Nshimyimana, Dominique, et al.
Publicado: (2025)

MeciFace: Mechanomyography and Inertial Fusion-based Glasses for Edge Real-Time Recognition of Facial and Eating Activities
por: Bello, Hymalai, et al.
Publicado: (2023)

DMA Reception for Simultaneous Area-Wide Sensing and Multi-User Uplink Communications
por: Gavras, Ioannis, et al.
Publicado: (2025)

Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition
por: Calatrava-Nicolás, Francisco M., et al.
Publicado: (2026)

EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
por: Peng, Wei, et al.
Publicado: (2024)

A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition
por: Liu, Mengxi, et al.
Publicado: (2024)

Learning to Count Targets from Dual-Window: A CNN Approach for OFDM ISAC
por: Khansa, Ali Al
Publicado: (2025)

SwinFi: a CSI Compression Method based on Swin Transformer for Wi-Fi Sensing
por: Bian, Jichen
Publicado: (2024)

OpenMarcie: Dataset for Multimodal Action Recognition in Industrial Environments
por: Bello, Hymalai, et al.
Publicado: (2026)

Enhancing Inertial Hand based HAR through Joint Representation of Language, Pose and Synthetic IMUs
por: Rey, Vitor Fortes, et al.
Publicado: (2024)

Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task
por: Haresamudram, Harish, et al.
Publicado: (2024)

Highly Efficient Waveform Design and Hybrid Duplex for Joint Communication and Sensing
por: Ma, Yihua, et al.
Publicado: (2022)

Predictive Target-to-User Association in Complex Scenarios via Hybrid-Field ISAC Signaling
por: Yuan, Yifeng, et al.
Publicado: (2025)

Deep Learning-Based Data Fusion of 6G Sensing and Inertial Information for Target Positioning: Experimental Validation
por: Muthineni, Karthik, et al.
Publicado: (2025)

CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition
por: Liu, Mengxi, et al.
Publicado: (2024)

Interference Detection and Exploitation for Multi-User Radar Sensing
por: Randers, Laurits, et al.
Publicado: (2026)

Few-Shot Recognition and Classification Framework for Jamming Signal: A CGAN-Based Fusion CNN Approach
por: Ding, Xuhui, et al.
Publicado: (2023)

On Neural Inertial Classification Networks for Pedestrian Activity Recognition
por: Yampolsky, Zeev, et al.
Publicado: (2025)

Temporal Action Localization for Inertial-based Human Activity Recognition
por: Bock, Marius, et al.
Publicado: (2023)