ArticleScientific reports2024
TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Adaptive Convolutional Neural Network-Enhanced Scale-Fusion Network for Human Activity Recognition Using Wearable Sensors.Sensors (Basel, Switzerland) · 2026Article
- Recognition of Daily Activities Through Multi-Modal Deep Learning: A Video, Pose, and Object-Aware Approach for Ambient Assisted Living.Journal of healthcare informatics research · 2026Article
- Efficient uncertainty aware human activity recognition on microcontrollers using hyperdimensional computing and conformal prediction.Scientific reports · 2026Article
- A Real-Time Multi-Class Human Activity Monitoring System Using mmWave Radar.Sensors (Basel, Switzerland) · 2026Article
- Lightweight Multi-Scale Framework for Human Pose and Action Classification.Sensors (Basel, Switzerland) · 2026Article
- Integrating psychological profiling with deep learning for enhanced boxing action recognition.Scientific reports · 2026Article
- An open-set hybrid CNN-TCN network for human action recognition with unknown action rejection in collaborative robot workspaces.Frontiers in robotics and AI · 2026Article
- Deep Learning Algorithms for Human Activity Recognition in Manual Material Handling Tasks.Sensors (Basel, Switzerland) · 2025Article
- Human Activity Recognition via Attention-Augmented TCN-BiGRU Fusion.Sensors (Basel, Switzerland) · 2025Article
- Wearable Sensor-Based Human Activity Recognition: Performance and Interpretability of Dynamic Neural Networks.Sensors (Basel, Switzerland) · 2025Article
- A Comprehensive Methodological Survey of Human Activity Recognition Across Diverse Data Modalities.Sensors (Basel, Switzerland) · 2025Review
- A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living.Journal of imaging · 2025Article
- A novel YOLO LSTM approach for enhanced human action recognition in video sequences.Scientific reports · 2025Article
- Multi-Head Attention-Based Framework with Residual Network for Human Action Recognition.Sensors (Basel, Switzerland) · 2025Article
- A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition.Scientific reports · 2025Article
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Authors and funding
2 authors.
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Abstract
Wearable sensors are widely used in medical applications and human-computer interaction because of their portability and powerful privacy. Human activity identification based on sensor data plays a vital role in these fields. Therefore, it is important to improve the recognition performance of different types of actions. Aiming at the problems of insufficient time-varying feature extraction and gradient explosion caused by too many network layers, a time convolution network recognition model with attention mechanism (TCN-Attention-HAR) was proposed. The model effectively recognizes and emphasizes the key feature information. The ability of extracting temporal features from TCN (temporal convolution network) is improved by using the appropriate size of the receiver domain. In addition, attention mechanisms are used to assign higher weights to important information, enabling models to learn and identify human activities more effectively. The performance of the Open Data Set (WISDM, PAMAP2 and USC-HAD) is improved by 1.13%, 1.83% and 0.51%, respectively, compared with other advanced models, these results clearly show that the network model presented in this paper has excellent recognition performance. In the knowledge distillation experiment, the parameters of student model are only about 0.1% of those of teacher model, and the accuracy of the model has been greatly improved, and in the WISDM data set, compared with the teacher's model, the accuracy is 0.14% higher.
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