Wise Information Technology of Med: Human Pose Recognition in Elderly Care
The growing problem of aging has led to a social concern on how to take care of the elderly living alone. Many traditional methods based on visual cameras have been used in elder monitoring. However, these methods are difficult to be applied in daily life, limited by high storage space with the came...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7130 |
| _version_ | 1797511776763379712 |
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| author | Difei Xu Xuelei Qi Chen Li Ziheng Sheng Hailong Huang |
| author_facet | Difei Xu Xuelei Qi Chen Li Ziheng Sheng Hailong Huang |
| author_sort | Difei Xu |
| collection | DOAJ |
| description | The growing problem of aging has led to a social concern on how to take care of the elderly living alone. Many traditional methods based on visual cameras have been used in elder monitoring. However, these methods are difficult to be applied in daily life, limited by high storage space with the camera, low-speed information processing, sensitivity to lighting, the blind area in vision, and the possibility of revealing privacy. Therefore, wise information technology of the Med System based on the micro-Doppler effect and Ultra Wide Band (UWB) radar for human pose recognition in the elderly living alone is proposed to effectively identify and classify the human poses in static and moving conditions. In recognition processing, an improved PCA-LSTM approach is proposed by combing with the Principal Component Analysis (PCA) and Long Short Term Memory (LSTM) to integrate the micro-Doppler features and time sequence of the human body to classify and recognize the human postures. Moreover, the classification accuracy with different kernel functions in the Support Vector Machine (SVM) is also studied. In the real experiment, there are two healthy men and one woman (22–26 years old) selected to imitate the movements of the elderly and slowly perform five postures (from sitting to standing, from standing to sitting, walking in place, falling and boxing). The experimental results show that the resolution of the entire system for the five actions reaches 99.1% in the case of using Gaussian kernel function, so the proposed method is effective and the Gaussian kernel function is suitable for human pose recognition. |
| first_indexed | 2024-03-10T05:52:52Z |
| format | Article |
| id | doaj.art-5df6b2fbaf1f4530847cf7c12160f1a7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:52:52Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5df6b2fbaf1f4530847cf7c12160f1a72023-11-22T21:37:02ZengMDPI AGSensors1424-82202021-10-012121713010.3390/s21217130Wise Information Technology of Med: Human Pose Recognition in Elderly CareDifei Xu0Xuelei Qi1Chen Li2Ziheng Sheng3Hailong Huang4School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, AustraliaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, AustraliaSchool of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, AustraliaDepartment of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong KongThe growing problem of aging has led to a social concern on how to take care of the elderly living alone. Many traditional methods based on visual cameras have been used in elder monitoring. However, these methods are difficult to be applied in daily life, limited by high storage space with the camera, low-speed information processing, sensitivity to lighting, the blind area in vision, and the possibility of revealing privacy. Therefore, wise information technology of the Med System based on the micro-Doppler effect and Ultra Wide Band (UWB) radar for human pose recognition in the elderly living alone is proposed to effectively identify and classify the human poses in static and moving conditions. In recognition processing, an improved PCA-LSTM approach is proposed by combing with the Principal Component Analysis (PCA) and Long Short Term Memory (LSTM) to integrate the micro-Doppler features and time sequence of the human body to classify and recognize the human postures. Moreover, the classification accuracy with different kernel functions in the Support Vector Machine (SVM) is also studied. In the real experiment, there are two healthy men and one woman (22–26 years old) selected to imitate the movements of the elderly and slowly perform five postures (from sitting to standing, from standing to sitting, walking in place, falling and boxing). The experimental results show that the resolution of the entire system for the five actions reaches 99.1% in the case of using Gaussian kernel function, so the proposed method is effective and the Gaussian kernel function is suitable for human pose recognition.https://www.mdpi.com/1424-8220/21/21/7130elderly carehuman pose recognitionfeature extractionPCA-LSTM recognitiongaussian kernel function classification |
| spellingShingle | Difei Xu Xuelei Qi Chen Li Ziheng Sheng Hailong Huang Wise Information Technology of Med: Human Pose Recognition in Elderly Care Sensors elderly care human pose recognition feature extraction PCA-LSTM recognition gaussian kernel function classification |
| title | Wise Information Technology of Med: Human Pose Recognition in Elderly Care |
| title_full | Wise Information Technology of Med: Human Pose Recognition in Elderly Care |
| title_fullStr | Wise Information Technology of Med: Human Pose Recognition in Elderly Care |
| title_full_unstemmed | Wise Information Technology of Med: Human Pose Recognition in Elderly Care |
| title_short | Wise Information Technology of Med: Human Pose Recognition in Elderly Care |
| title_sort | wise information technology of med human pose recognition in elderly care |
| topic | elderly care human pose recognition feature extraction PCA-LSTM recognition gaussian kernel function classification |
| url | https://www.mdpi.com/1424-8220/21/21/7130 |
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