Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar
Abstract Human motion recognition for biological radar has made astonishing progress. However, in some applications with high real‐time requirements, it is difficult for existing approaches to achieve high accuracy. A multidimensional features long short‐term memory (LSTM) neural network model is pr...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2022-01-01
|
| Series: | IET Biometrics |
| Online Access: | https://doi.org/10.1049/bme2.12038 |
| _version_ | 1797426403843506176 |
|---|---|
| author | Jinxiao Zhong Liangnian Jin Qiang Mao |
| author_facet | Jinxiao Zhong Liangnian Jin Qiang Mao |
| author_sort | Jinxiao Zhong |
| collection | DOAJ |
| description | Abstract Human motion recognition for biological radar has made astonishing progress. However, in some applications with high real‐time requirements, it is difficult for existing approaches to achieve high accuracy. A multidimensional features long short‐term memory (LSTM) neural network model is presented using multibranch network structure and high‐dimensional radar feature fusion, which can recognise motions of human in real time, even in the presence of occlusions. The features selected for motion recognition including slow time range‐map and slow time Doppler map. A single feature‐based representation is not enough to capture the variations and attributes of individuals (range, velocity, etc.); thus, the fusion of multiple features is significant for recognising motions. Furthermore, because action reflects the behaviour of a human within a period, and the start and end are unavailable, intercepting fixed‐length data in the time domain for recognition is not feasible. Thus, we introduce an approach based on an LSTM network that extracts features along the time dimension. Experiments show that the proposed approach is effective. A recognition accuracy of above 93.38% is achieved. |
| first_indexed | 2024-03-09T08:29:44Z |
| format | Article |
| id | doaj.art-63ee274959c7413494ebaa8fe77729e7 |
| institution | Directory Open Access Journal |
| issn | 2047-4938 2047-4946 |
| language | English |
| last_indexed | 2024-03-09T08:29:44Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj.art-63ee274959c7413494ebaa8fe77729e72023-12-02T20:17:56ZengHindawi-IETIET Biometrics2047-49382047-49462022-01-011111910.1049/bme2.12038Real‐time recognition of human motions using multidimensional features in ultrawideband biological radarJinxiao Zhong0Liangnian Jin1Qiang Mao2Institute of Information and Communication Guilin University of Electronic Technology Guilin ChinaInstitute of Information and Communication Guilin University of Electronic Technology Guilin ChinaInstitute of Information and Communication Guilin University of Electronic Technology Guilin ChinaAbstract Human motion recognition for biological radar has made astonishing progress. However, in some applications with high real‐time requirements, it is difficult for existing approaches to achieve high accuracy. A multidimensional features long short‐term memory (LSTM) neural network model is presented using multibranch network structure and high‐dimensional radar feature fusion, which can recognise motions of human in real time, even in the presence of occlusions. The features selected for motion recognition including slow time range‐map and slow time Doppler map. A single feature‐based representation is not enough to capture the variations and attributes of individuals (range, velocity, etc.); thus, the fusion of multiple features is significant for recognising motions. Furthermore, because action reflects the behaviour of a human within a period, and the start and end are unavailable, intercepting fixed‐length data in the time domain for recognition is not feasible. Thus, we introduce an approach based on an LSTM network that extracts features along the time dimension. Experiments show that the proposed approach is effective. A recognition accuracy of above 93.38% is achieved.https://doi.org/10.1049/bme2.12038 |
| spellingShingle | Jinxiao Zhong Liangnian Jin Qiang Mao Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar IET Biometrics |
| title | Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar |
| title_full | Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar |
| title_fullStr | Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar |
| title_full_unstemmed | Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar |
| title_short | Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar |
| title_sort | real time recognition of human motions using multidimensional features in ultrawideband biological radar |
| url | https://doi.org/10.1049/bme2.12038 |
| work_keys_str_mv | AT jinxiaozhong realtimerecognitionofhumanmotionsusingmultidimensionalfeaturesinultrawidebandbiologicalradar AT liangnianjin realtimerecognitionofhumanmotionsusingmultidimensionalfeaturesinultrawidebandbiologicalradar AT qiangmao realtimerecognitionofhumanmotionsusingmultidimensionalfeaturesinultrawidebandbiologicalradar |