ArticleScientific reports2025
Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A Permutation-Invariant, Missing-View-Aware Multi-Perspective Recurrent Neural Network Cell for Time-Series Fusion.Sensors (Basel, Switzerland) · 2026Article
- AWARE-Net: A Lightweight Joint Optimization Framework for Robust Sensor-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2026Article
- Robust displacement estimation from filament-based soft sensors using a parallel attention-enhanced LSTM for rehabilitation monitoring.Scientific reports · 2026Article
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2 authors.
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Abstract
The recognition of human activity by wearable sensors has garnered significant interest owing to its extensive applications in health, sports, and surveillance systems. This paper presents a novel hybrid deep learning model, termed CNNd-TAm, for the recognition of both basic and complicated activities. The suggested approach enhances spatial feature extraction and long-term temporal dependency modeling by integrating Dilated convolutional networks with a modified temporal attention mechanism. Data from accelerometer and gyroscope sensors in the UTwente dataset, encompassing 13 activities and 10 people, underwent preparation that included filtering, normalization, and the selection of diverse time periods according to the activity type. Experimental findings demonstrate an accuracy of 99.4% in identifying intricate behaviors, such as conversing and consuming coffee, surpassing earlier hybrid deep learning models. This model represents a significant advancement in the development of efficient Human Activity Recognition systems by solving deficiencies in the recognition of intricate activities.
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