Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning
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)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/15/5470 |
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| author | Mubarak A. Alanazi Abdullah K. Alhazmi Osama Alsattam Kara Gnau Meghan Brown Shannon Thiel Kurt Jackson Vamsy P. Chodavarapu |
| author_facet | Mubarak A. Alanazi Abdullah K. Alhazmi Osama Alsattam Kara Gnau Meghan Brown Shannon Thiel Kurt Jackson Vamsy P. Chodavarapu |
| author_sort | Mubarak A. Alanazi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T05:01:15Z |
| format | Article |
| id | doaj.art-90cf10f7fede40a1bd79ddd33564b3df |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:01:15Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-90cf10f7fede40a1bd79ddd33564b3df2023-12-03T12:59:30ZengMDPI AGSensors1424-82202022-07-012215547010.3390/s22155470Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine LearningMubarak A. Alanazi0Abdullah K. Alhazmi1Osama Alsattam2Kara Gnau3Meghan Brown4Shannon Thiel5Kurt Jackson6Vamsy P. Chodavarapu7Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USADepartment of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USADepartment of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USADepartment of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USADepartment of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USADepartment of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USADepartment of Physical Therapy, University of Dayton, 300 College Park, Dayton, OH 45469, USADepartment of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45469, USAHuman 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.https://www.mdpi.com/1424-8220/22/15/5470human gait recognitiongait analysismmWave radarmachine learning3D point-cloudIWR6843ISK-ODS |
| spellingShingle | Mubarak A. Alanazi Abdullah K. Alhazmi Osama Alsattam Kara Gnau Meghan Brown Shannon Thiel Kurt Jackson Vamsy P. Chodavarapu Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning Sensors human gait recognition gait analysis mmWave radar machine learning 3D point-cloud IWR6843ISK-ODS |
| title | Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning |
| title_full | Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning |
| title_fullStr | Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning |
| title_full_unstemmed | Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning |
| title_short | Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning |
| title_sort | towards a low cost solution for gait analysis using millimeter wave sensor and machine learning |
| topic | human gait recognition gait analysis mmWave radar machine learning 3D point-cloud IWR6843ISK-ODS |
| url | https://www.mdpi.com/1424-8220/22/15/5470 |
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