ArticleScientific reports2022
A Novel CNN-based Bi-LSTM parallel model with attention mechanism for human activity recognition with noisy data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Adaptive Convolutional Neural Network-Enhanced Scale-Fusion Network for Human Activity Recognition Using Wearable Sensors.Sensors (Basel, Switzerland) · 2026Article
- LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.Journal of chemical information and modeling · 2026Article
- Improved CNN with BiLSTM model for early melanoma and skin lesion classification.Frontiers in artificial intelligence · 2026Article
- SKS-transformer: multi-scale and direction-aware attention for inertial sensor-based activity recognition.Frontiers in sports and active living · 2026Article
- A Feature-Augmented Transformer Model to Recognize Functional Activities From in-the-Wild Smartwatch Data.IEEE journal of biomedical and health informatics · 2026Article
- Climate impacts and future trends of hailstorms in China based on millennial records.Nature communications · 2025Article
- Human activity recognition algorithms for manual material handling activities.Scientific reports · 2025Article
- A Multi-Agent and Attention-Aware Enhanced CNN-BiLSTM Model for Human Activity Recognition for Enhanced Disability Assistance.Diagnostics (Basel, Switzerland) · 2025Article
- A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition.Scientific reports · 2025Article
- Hydra-TS: Enhancing Human Activity Recognition with Multi-Objective Synthetic Time Series Data Generation.IEEE sensors journal · 2025Article
- Human Multi-Activities Classification Using mmWave Radar: Feature Fusion in Time-Domain and PCANet.Sensors (Basel, Switzerland) · 2024Article
- Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles.Sensors (Basel, Switzerland) · 2024Article
- Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.Cancer medicine · 2024Article
- TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network.Scientific reports · 2024Article
- An intelligent agriculture management system for rainfall prediction and fruit health monitoring.Scientific reports · 2024Article
- A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data.Sensors (Basel, Switzerland) · 2023Article
- Human behavior recognition based on sparse transformer with channel attention mechanism.Frontiers in physiology · 2023Article
- Matched Filter Interpretation of CNN Classifiers with Application to HAR.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Boosted by mobile communication technologies, Human Activity Recognition (HAR) based on smartphones has attracted more and more attentions of researchers. One of the main challenges is the classification time and accuracy in processing long-time dependent sequence samples with noisy or missed data. In this paper, a 1-D Convolution Neural Network (CNN)-based bi-directional Long Short-Term Memory (LSTM) parallel model with attention mechanism (ConvBLSTM-PMwA) is proposed. The original features of sensors are segmented into sub-segments by well-designed equal time step sliding window, and fed into 1-D CNN-based bi-directional LSTM parallel layer to accelerate feature extraction with noisy and missed data. The weights of extracted features are redistributed by attention mechanism and integrated into complete features. At last, the final classification results are obtained with the full connection layer. The performance is evaluated on public UCI and WISDM HAR datasets. The results show that the ConvBLSTM-PMwA model performs better than the existing CNN and RNN models in both classification accuracy (96.71%) and computational time complexity (1.1 times faster at least), even if facing HAR data with noise.
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