ArticleSensors (Basel, Switzerland)2022
Human Activity Recognition via Hybrid Deep Learning Based Model.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Application of the Ultraleap 3Di-Based Gesture-Controlled 3D Imaging Visualization System in Pulmonary Segmentectomy: A Single-Center Prospective Study.Bioengineering (Basel, Switzerland) · 2026Article
- Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models.Sensors (Basel, Switzerland) · 2026Article
- A tiny inertial transformer for human activity recognition via multimodal knowledge distillation and explainable AI.Scientific reports · 2025Article
- A hybrid multi-layer perceptron with selective stacked ensemble learning approach for recognizing human activity using sensor dataset.Scientific reports · 2025Article
- Personalized Smart Home Automation Using Machine Learning: Predicting User Activities.Sensors (Basel, Switzerland) · 2025Article
- Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition.Scientific reports · 2025Article
- The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports.PloS one · 2025Article
- Dual-Stream Architecture Enhanced by Soft-Attention Mechanism for Plant Species Classification.Plants (Basel, Switzerland) · 2024Article
- Prediction of white blood cell count during exercise: a comparison between standalone and hybrid intelligent algorithms.Scientific reports · 2024Article
- PAR-Net: An Enhanced Dual-Stream CNN-ESN Architecture for Human Physical Activity Recognition.Sensors (Basel, Switzerland) · 2024Article
- Wearable Sensor-Based Residual Multifeature Fusion Shrinkage Networks for Human Activity Recognition.Sensors (Basel, Switzerland) · 2024Article
- Wearable sensors based on artificial intelligence models for human activity recognition.Frontiers in artificial intelligence · 2024Article
- Deep Wavelet Convolutional Neural Networks for Multimodal Human Activity Recognition Using Wearable Inertial Sensors.Sensors (Basel, Switzerland) · 2023Article
- Multivariate CNN Model for Human Locomotion Activity Recognition with a Wearable Exoskeleton Robot.Bioengineering (Basel, Switzerland) · 2023Article
- Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas.Diagnostics (Basel, Switzerland) · 2023Article
- Recurrent Network Solutions for Human Posture Recognition Based on Kinect Skeletal Data.Sensors (Basel, Switzerland) · 2023Article
- Table-Balancing Cooperative Robot Based on Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2023Article
- Deep SE-BiLSTM with IFPOA Fine-Tuning for Human Activity Recognition Using Mobile and Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- A Multi-Label Based Physical Activity Recognition via Cascade Classifier.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications.
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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.