ArticleSensors (Basel, Switzerland)2022
Wearable Sensor-Based Human Activity Recognition with Transformer 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 44 papers.
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Who cites it
44 citing papers in PubMed.
- Adaptive Convolutional Neural Network-Enhanced Scale-Fusion Network for Human Activity Recognition Using Wearable Sensors.Sensors (Basel, Switzerland) · 2026Article
- An Adaptive IMU-Visual Multimodal Fusion System for Real-Time Exercise Recognition and Movement Quality Assessment.Sensors (Basel, Switzerland) · 2026Article
- Integrating Artificial Intelligence with Wearable Sensors for Advanced Health Monitoring and Diagnosis.Biosensors · 2026Review
- Intelligent fall risk prediction and real-time warning system for elderly care based on multimodal deep learning and wearable sensor fusion.Scientific reports · 2026Article
- Dynamic-Attentive Selective Mamba with Group-Aware Convolution for Wearable Sensor-Based Sports and Daily Activity Recognition.Sensors (Basel, Switzerland) · 2026Article
- IoT framework for sports activity safety monitoring based on wearable sensors and CRNN spatiotemporal analysis.Scientific reports · 2026Article
- An explainable real time sensor graph transformer for dance recognition.Scientific reports · 2026Article
- Deep Learning for Dynamic Prognostic Prediction in Minimally Invasive Surgery for Intracerebral Hemorrhage: Model Development and Validation Study.JMIR medical informatics · 2026Article
- GADF-enhanced global-local feature network for wearable sensor-based cow behavior recognition.Frontiers in veterinary science · 2026Article
- Optimizing Sensor and Data Selection on Lower Limbs via Deep Learning for Real-Time Human Activity Recognition.IEEE transactions on human-machine systems · 2026Article
- SKS-transformer: multi-scale and direction-aware attention for inertial sensor-based activity recognition.Frontiers in sports and active living · 2026Article
- Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition.Scientific reports · 2025Article
- Real-time activity and fall detection using transformer-based deep learning models for elderly care applications.BMJ health & care informatics · 2025Article
- Human Activity Recognition via Attention-Augmented TCN-BiGRU Fusion.Sensors (Basel, Switzerland) · 2025Article
- High-Accuracy Lower-Limb Intent Recognition: A KPCA-ISSA-SVM Approach with sEMG-IMU Sensor Fusion.Biomimetics (Basel, Switzerland) · 2025Article
- Continuous Monitoring with AI-Enhanced BioMEMS Sensors: A Focus on Sustainable Energy Harvesting and Predictive Analytics.Micromachines · 2025Review
- A human activity recognition model based on deep neural network integrating attention mechanism.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
- Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity Recognition.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Computing devices that can recognize various human activities or movements can be used to assist people in healthcare, sports, or human-robot interaction. Readily available data for this purpose can be obtained from the accelerometer and the gyroscope built into everyday smartphones. Effective classification of real-time activity data is, therefore, actively pursued using various machine learning methods. In this study, the transformer model, a deep learning neural network model developed primarily for the natural language processing and vision tasks, was adapted for a time-series analysis of motion signals. The self-attention mechanism inherent in the transformer, which expresses individual dependencies between signal values within a time series, can match the performance of state-of-the-art convolutional neural networks with long short-term memory. The performance of the proposed adapted transformer method was tested on the largest available public dataset of smartphone motion sensor data covering a wide range of activities, and obtained an average identification accuracy of 99.2% as compared with 89.67% achieved on the same data by a conventional machine learning method. The results suggest the expected future relevance of the transformer model for human activity recognition.
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Registered trials
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.