ArticleIEEE journal of translational engineering in health and medicine2022
Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor.
Article in IEEE journal of translational engineering in health and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 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
- A Gear-Driven Plantar Energy Harvester with Integrated Self-Sensing for Human Locomotion Recognition.Sensors (Basel, Switzerland) · 2026Article
- Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study.JMIR mHealth and uHealth · 2026Article
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- Robust displacement estimation from filament-based soft sensors using a parallel attention-enhanced LSTM for rehabilitation monitoring.Scientific reports · 2026Article
- Pedestrian navigation activity recognition method based on two-stream transformer and contrastive learning.iScience · 2026Article
- IoT framework for sports activity safety monitoring based on wearable sensors and CRNN spatiotemporal analysis.Scientific reports · 2026Article
- A wearable IMU-based framework for daily physical activity recognition and energy expenditure level classification in university students.Frontiers in public health · 2026Article
- Spatiotemporal deep learning framework for predictive behavioral threat detection in surveillance footage.Frontiers in big data · 2026Article
- Hybrid Convolutional-Gated Recurrent Neural Network for Robust Mobile Health Activities Classification.International journal of telemedicine and applications · 2026Article
- Review
- Wearable sensor data-driven sports posture recognition using the ST-GCN spatio-temporal graph convolutional network.Scientific reports · 2025Article
- An enhanced dual inception-attention-BiGRU-attention model integrating wavelet transform for wearable sensor-based human activity recognition.Scientific reports · 2025Article
- A tiny inertial transformer for human activity recognition via multimodal knowledge distillation and explainable AI.Scientific reports · 2025Article
- An Improved Step Detection Algorithm for Indoor Navigation Problems with Pre-Determined Types of Activity.Sensors (Basel, Switzerland) · 2025Article
- Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition.Scientific reports · 2025Article
- Human Activity Recognition via Attention-Augmented TCN-BiGRU Fusion.Sensors (Basel, Switzerland) · 2025Article
- A human activity recognition model based on deep neural network integrating attention mechanism.Scientific reports · 2025Article
- A learning behavior classification model based on classroom meta-action sequences.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely
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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.