ArticleSensors (Basel, Switzerland)2022
Human Activity Recognition Based on Residual Network and BiLSTM.
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 26 papers.
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Who cites it
26 citing papers in PubMed.
- Adaptive Convolutional Neural Network-Enhanced Scale-Fusion Network for Human Activity Recognition Using Wearable Sensors.Sensors (Basel, Switzerland) · 2026Article
- Deployment-Oriented Evaluation of Lightweight IMU-Based Human Activity Recognition: On-Device Efficiency and Streaming Feasibility.Sensors (Basel, Switzerland) · 2026Article
- Analysis of decision support system of basketball sports competition based on machine learning and IoT.Scientific reports · 2026Article
- Tactile Sensor-Based Body Center of Pressure Estimation System Using Supervised Deep Learning Models.Sensors (Basel, Switzerland) · 2026Article
- Augmenting a ResNet + BiLSTM Deep Learning Model with Clinical Mobility Data Helps Outperform a Heuristic Frequency-Based Model for Walking Bout Segmentation.Sensors (Basel, Switzerland) · 2025Article
- SarcoNet: A Pilot Study on Integrating Clinical and Kinematic Features for Sarcopenia Classification.Diagnostics (Basel, Switzerland) · 2025Article
- The singing style of female roles in ethnic opera under artificial intelligence and deep neural networks.Scientific reports · 2025Article
- The use of artificial intelligence-based Siamese neural network in personalized guidance for sports dance teaching.Scientific reports · 2025Article
- Applying MLP-Mixer and gMLP to Human Activity Recognition.Sensors (Basel, Switzerland) · 2025Article
- Exploring the Possibility of Photoplethysmography-Based Human Activity Recognition Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition.Sensors (Basel, Switzerland) · 2024Article
- Wearable sensors based on artificial intelligence models for human activity recognition.Frontiers in artificial intelligence · 2024Article
- Validation of physical activity levels from shank-placed Axivity AX6 accelerometers in older adults.PloS one · 2024Article
- A Privacy and Energy-Aware Federated Framework for Human Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication.Micromachines · 2023Article
- PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images.BMC cancer · 2023Article
- Accurate Stride-Length Estimation Based on LT-StrideNet for Pedestrian Dead Reckoning Using a Shank-Mounted Sensor.Micromachines · 2023Article
- Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Deep SE-BiLSTM with IFPOA Fine-Tuning for Human Activity Recognition Using Mobile and Wearable Sensors.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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.
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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.