A Non-Contact Paraparesis Detection Technique Based on 1D-CNN
In clinical practice, doctors are using bedside tests to assist in the diagnosis of paraparesis. The disadvantage is that it depends on the doctor's clinical experience and the supervisor's judgment. Therefore, there is an urgent need for an objective and efficient diagnostic equipment. Wi...
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8931587/ |
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doaj-abb768a1a43e46348015e041d21c06522021-03-30T00:41:08ZengIEEEIEEE Access2169-35362019-01-01718228018228810.1109/ACCESS.2019.29590238931587A Non-Contact Paraparesis Detection Technique Based on 1D-CNNLei Guan0https://orcid.org/0000-0002-4701-1204Fangming Hu1https://orcid.org/0000-0001-7902-0247Fadi Al-Turjman2https://orcid.org/0000-0001-6375-4123Muhammad Bilal Khan3https://orcid.org/0000-0001-6223-8987Xiaodong Yang4School of Life Science and Technology, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Xidian University, Xi’an, ChinaArtificial Intelligence Department, Near East University, Mersin, TurkeySchool of Electronic Engineering, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Xidian University, Xi’an, ChinaIn clinical practice, doctors are using bedside tests to assist in the diagnosis of paraparesis. The disadvantage is that it depends on the doctor's clinical experience and the supervisor's judgment. Therefore, there is an urgent need for an objective and efficient diagnostic equipment. With the rapid development of wireless technology, ubiquitous RF signals become a promising sensing technology. In this study, we propose a non-contact wireless sensing method based on RF signals to detect paraparesis. Our system can reduce the burden on doctors and improve work efficiency. Outlier filters and wavelet hard threshold decomposition are used to filter the wireless signal. A 1D-CNN model is designed to automatically extract valid features and classifications. The results analyze in two bedside tests, our system perform efficiently and accurately patient screening with suspected paraparesis. This provide more effective guidance and assistance for further treatment. The proposed method has an average accuracy of 99.4% and 98.5% in the Barre test and Mingazzini test respectively.https://ieeexplore.ieee.org/document/8931587/BarreCNNlower limb paraparesisMingazziniwireless sensing |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Lei Guan Fangming Hu Fadi Al-Turjman Muhammad Bilal Khan Xiaodong Yang |
| spellingShingle |
Lei Guan Fangming Hu Fadi Al-Turjman Muhammad Bilal Khan Xiaodong Yang A Non-Contact Paraparesis Detection Technique Based on 1D-CNN IEEE Access Barre CNN lower limb paraparesis Mingazzini wireless sensing |
| author_facet |
Lei Guan Fangming Hu Fadi Al-Turjman Muhammad Bilal Khan Xiaodong Yang |
| author_sort |
Lei Guan |
| title |
A Non-Contact Paraparesis Detection Technique Based on 1D-CNN |
| title_short |
A Non-Contact Paraparesis Detection Technique Based on 1D-CNN |
| title_full |
A Non-Contact Paraparesis Detection Technique Based on 1D-CNN |
| title_fullStr |
A Non-Contact Paraparesis Detection Technique Based on 1D-CNN |
| title_full_unstemmed |
A Non-Contact Paraparesis Detection Technique Based on 1D-CNN |
| title_sort |
non-contact paraparesis detection technique based on 1d-cnn |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2019-01-01 |
| description |
In clinical practice, doctors are using bedside tests to assist in the diagnosis of paraparesis. The disadvantage is that it depends on the doctor's clinical experience and the supervisor's judgment. Therefore, there is an urgent need for an objective and efficient diagnostic equipment. With the rapid development of wireless technology, ubiquitous RF signals become a promising sensing technology. In this study, we propose a non-contact wireless sensing method based on RF signals to detect paraparesis. Our system can reduce the burden on doctors and improve work efficiency. Outlier filters and wavelet hard threshold decomposition are used to filter the wireless signal. A 1D-CNN model is designed to automatically extract valid features and classifications. The results analyze in two bedside tests, our system perform efficiently and accurately patient screening with suspected paraparesis. This provide more effective guidance and assistance for further treatment. The proposed method has an average accuracy of 99.4% and 98.5% in the Barre test and Mingazzini test respectively. |
| topic |
Barre CNN lower limb paraparesis Mingazzini wireless sensing |
| url |
https://ieeexplore.ieee.org/document/8931587/ |
| work_keys_str_mv |
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