ArticleSensors (Basel, Switzerland)2023
Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Real-Time Vision-Based Fall Detection Systems for the Elderly: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Edge-AI Instrumentation Framework for Multimodal Biometric Sensing in Active Aging Environments.Sensors (Basel, Switzerland) · 2026Article
- Efficient Fall Detection from Wrist-Worn IMU Signals via Knowledge Distillation: A Lightweight CNN Approach Using the UMAFall Dataset.Sensors (Basel, Switzerland) · 2026Article
- An adaptive fall detection system based on ensemble learning using variants of YOLO V8 retinanet and DETR.Scientific reports · 2025Article
- Enhancing Real-World Fall Detection Using Commodity Devices: A Systematic Study.Sensors (Basel, Switzerland) · 2025Article
- Experimental Study of Long Short-Term Memory and Transformer Models for Fall Detection on Smartwatches.Sensors (Basel, Switzerland) · 2024Article
- Fall Detection System Based on Point Cloud Enhancement Model for 24 GHz FMCW Radar.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors.
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Abstract
Falls can easily cause major harm to the health of the elderly, and timely detection can avoid further injuries. To detect the occurrence of falls in time, we propose a new method called Patch-Transformer Network (PTN) wearable-sensor-based fall detection algorithm. The neural network includes a convolution layer, a Transformer encoding layer, and a linear classification layer. The convolution layer is used to extract local features and project them into feature matrices. After adding positional coding information, the global features of falls are learned through the multi-head self-attention mechanism in the Transformer encoding layer. Global average pooling (GAP) is used to strengthen the correlation between features and categories. The final classification results are provided by the linear layer. The accuracy of the model obtained on the public available datasets SisFall and UnMib SHAR is 99.86% and 99.14%, respectively. The network model has fewer parameters and lower complexity, with detection times of 0.004 s and 0.001 s on the two datasets. Therefore, our proposed method can timely and accurately detect the occurrence of falls, which is important for protecting the lives of the elderly.
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