ArticleSensors (Basel, Switzerland)2023
Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Office Posture Detection Using Ceiling-Mounted Ultra-Wideband Radar and Attention-Based Modality Fusion.Sensors (Basel, Switzerland) · 2025Article
- Non-Contact Fall Detection System Using 4D Imaging Radar for Elderly Safety Based on a CNN Model.Sensors (Basel, Switzerland) · 2025Article
- Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning.Scientific reports · 2025Article
- A deep learning AI model for determining the relationship between X-Ray detectors and patient positioning in chest radiography.PloS one · 2025Article
- HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
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Abstract
Human posture recognition technology is widely used in the fields of healthcare, human-computer interaction, and sports. The use of a Frequency-Modulated Continuous Wave (FMCW) millimetre-wave (MMW) radar sensor in measuring human posture characteristics data is of great significance because of its robust and strong recognition capabilities. This paper demonstrates how human posture characteristics data are measured, classified, and identified using FMCW techniques. First of all, the characteristics data of human posture is measured with the MMW radar sensors. Secondly, the point cloud data for human posture is generated, considering both the dynamic and static features of the reflected signal from the human body, which not only greatly reduces the environmental noise but also strengthens the reflection of the detected target. Lastly, six different machine learning models are applied for posture classification based on the generated point cloud data. To comparatively evaluate the proper model for point cloud data classification procedure-in addition to using the traditional index-the Kappa index was introduced to eliminate the effect due to the uncontrollable imbalance of the sampling data. These results support our conclusion that among the six machine learning algorithms implemented in this paper, the multi-layer perceptron (MLP) method is regarded as the most promising classifier.
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