ArticleSensors (Basel, Switzerland)2021
A CSI-Based Human Activity Recognition Using Deep Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 122 citations in OpenAlex.
- Structure-aware multi-task learning with domain generalization for robust vertebrae analysis in spinal CT.NPJ digital medicine · 2026Article
- An integration of deep learning models for effective classification of human activity patterns in disabled people using gesture analysis.Scientific reports · 2025Article
- Multi-Feature Unsupervised Domain Adaptation (M-FUDA) Applied to Cross Unaligned Domain-Specific Distributions in Device-Free Human Activity Classification.Sensors (Basel, Switzerland) · 2025Article
- Human Activity Recognition Through Augmented WiFi CSI Signals by Lightweight Attention-GRU.Sensors (Basel, Switzerland) · 2025Article
- Decision Fusion-Based Deep Learning for Channel State Information Channel-Aware Human Action Recognition.Sensors (Basel, Switzerland) · 2025Article
- Enhanced Human Activity Recognition Using Wi-Fi Sensing: Leveraging Phase and Amplitude with Attention Mechanisms.Sensors (Basel, Switzerland) · 2025Article
- Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition.Sensors (Basel, Switzerland) · 2024Article
- Non-Contact Cross-Person Activity Recognition by Deep Metric Ensemble Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Cross-Domain Human Activity Recognition Using Low-Resolution Infrared Sensors.Sensors (Basel, Switzerland) · 2024Article
- A high-dimensional, multi-transceiver channel state information dataset for enhanced human activity recognition.Data in brief · 2024Article
- Device-Free Wireless Sensing for Gesture Recognition Based on Complementary CSI Amplitude and Phase.Sensors (Basel, Switzerland) · 2024Article
- Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags.Sensors (Basel, Switzerland) · 2024Article
- MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference.Sensors (Basel, Switzerland) · 2024Article
- Adversarial AI applied to cross-user inter-domain and intra-domain adaptation in human activity recognition using wireless signals.PloS one · 2024Article
- Human Activity Recognition via Score Level Fusion of Wi-Fi CSI Signals.Sensors (Basel, Switzerland) · 2023Article
- CSI-Based Human Activity Recognition Using Multi-Input Multi-Output Autoencoder and Fine-Tuning.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends.Sensors (Basel, Switzerland) · 2023Review
- SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing.Patterns (New York, N.Y.) · 2023Article
- STC-NLSTMNet: An Improved Human Activity Recognition Method Using Convolutional Neural Network with NLSTM from WiFi CSI.Sensors (Basel, Switzerland) · 2022Article
- CNN-Based Smoker Classification and Detection in Smart City Application.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users' inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.