ArticleSensors (Basel, Switzerland)2022
Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning.
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 12 papers.
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Who cites it
12 citing papers in PubMed.
- Millimeter-wave technology for multi-person fall detection validated through wearable sensors and real-life scenarios.Scientific reports · 2026Article
- A Novel Deep Learning Model for Human Skeleton Estimation Using FMCW Radar.Sensors (Basel, Switzerland) · 2025Article
- A Comprehensive Survey of Research Trends in mmWave Technologies for Medical Applications.Sensors (Basel, Switzerland) · 2025Review
- A Magnetoelectric Distance Estimation System for Relative Human Motion Tracking.Sensors (Basel, Switzerland) · 2025Article
- Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine.Sensors (Basel, Switzerland) · 2024Article
- Human Pose Estimation for Clinical Analysis of Gait Pathologies.Bioinformatics and biology insights · 2024Article
- A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring.IEEE open journal of engineering in medicine and biology · 2024Article
- PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds.Sensors (Basel, Switzerland) · 2023Article
- How is Big Data reshaping preclinical aging research?Lab animal · 2023Review
- Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar.Sensors (Basel, Switzerland) · 2023Article
- Article
- Walking Step Monitoring with a Millimeter-Wave Radar in Real-Life Environment for Disease and Fall Prevention for the Elderly.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors.
Funding
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Abstract
Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram and point clouds for 19 human joints. We developed a multilayer Convolutional Neural Network (CNN) to recognize and classify five different gait patterns with an accuracy of 95.7 to 98.8% using MMW radar data. During training of the CNN algorithm, we used the extracted 3D coordinates of 25 joints using the Kinect V2 sensor and compared them with the point clouds data to improve the estimation. Finally, we performed a real-time simulation to observe the point cloud behavior for different activities and validated our system against the ground truth values. The proposed method demonstrates the ability to distinguish between different human activities to obtain clinically relevant gait information.
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