Real-time LSTM-based Multi-dimensional Features Gesture Recognition
Gesture recognition is widely used in the field of sensing.There are three kinds of gesture recognition methods based on computer vision,depth sensor and motion sensor.The recognition based on motion sensor has the advantages of less input data,high speed,and direct acquisition of hand 3D informatio...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2021-08-01
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| Series: | Jisuanji kexue |
| Subjects: | |
| Online Access: | http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-8-328.pdf |
| _version_ | 1818355791250325504 |
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| author | LIU Liang, PU Hao-yang |
| author_facet | LIU Liang, PU Hao-yang |
| author_sort | LIU Liang, PU Hao-yang |
| collection | DOAJ |
| description | Gesture recognition is widely used in the field of sensing.There are three kinds of gesture recognition methods based on computer vision,depth sensor and motion sensor.The recognition based on motion sensor has the advantages of less input data,high speed,and direct acquisition of hand 3D information,which has gradually become a research hotspot.Traditional gesture recognition based on motion sensor can be considered as a pattern recognition problem essentially and its accuracy depends heavily on feature data sets extracted from prior experience.Different from traditional pattern recognition methods,deep learning can greatly reduce the workload of artificial heuristic feature extraction.To solve the problem of traditional pattern recognition,this paper proposes a real-time multi-dimensional features recognition method based on Long Short-Term Memory(LSTM)and the performance of the method is verified by sufficient experiment.The method defines a gesture library consisting of five basic gestures and seven complex gestures at first.Based on the kinematic features of hand posture,the angle features and displacement features are extracted and then the frequency domain features of sensor data are extracted by short-time Fourier transform(SFTF).Then,three features are inputted into deep neural network LSTM for training,so the collected gestures are classified and recognized.At the same time,in order to verify the effectiveness of the proposed method,the gesture data of six volunteers are collected as the experimental data set by self-designed hand-held experience stick.The experimental results show that the accuracy of the recognition method proposed in this paper achieves 94.38% for basic and complex gestures,and the recognition accuracy is improved by nearly 2% compared with the traditional support vector machine,K-nearest neighbor method and fully connected neural network. |
| first_indexed | 2024-12-13T19:46:56Z |
| format | Article |
| id | doaj.art-0b02232cc2084247b8b0066d77217c53 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-12-13T19:46:56Z |
| publishDate | 2021-08-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-0b02232cc2084247b8b0066d77217c532022-12-21T23:33:31ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2021-08-0148832833310.11896/jsjkx.210300079Real-time LSTM-based Multi-dimensional Features Gesture RecognitionLIU Liang, PU Hao-yang0School of Cyber Science and Engineering,Sichuan University,Chengdu 610000,ChinaGesture recognition is widely used in the field of sensing.There are three kinds of gesture recognition methods based on computer vision,depth sensor and motion sensor.The recognition based on motion sensor has the advantages of less input data,high speed,and direct acquisition of hand 3D information,which has gradually become a research hotspot.Traditional gesture recognition based on motion sensor can be considered as a pattern recognition problem essentially and its accuracy depends heavily on feature data sets extracted from prior experience.Different from traditional pattern recognition methods,deep learning can greatly reduce the workload of artificial heuristic feature extraction.To solve the problem of traditional pattern recognition,this paper proposes a real-time multi-dimensional features recognition method based on Long Short-Term Memory(LSTM)and the performance of the method is verified by sufficient experiment.The method defines a gesture library consisting of five basic gestures and seven complex gestures at first.Based on the kinematic features of hand posture,the angle features and displacement features are extracted and then the frequency domain features of sensor data are extracted by short-time Fourier transform(SFTF).Then,three features are inputted into deep neural network LSTM for training,so the collected gestures are classified and recognized.At the same time,in order to verify the effectiveness of the proposed method,the gesture data of six volunteers are collected as the experimental data set by self-designed hand-held experience stick.The experimental results show that the accuracy of the recognition method proposed in this paper achieves 94.38% for basic and complex gestures,and the recognition accuracy is improved by nearly 2% compared with the traditional support vector machine,K-nearest neighbor method and fully connected neural network.http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-8-328.pdfhuman-computer interaction|gesture recognition|inertial sensor|motion capture |
| spellingShingle | LIU Liang, PU Hao-yang Real-time LSTM-based Multi-dimensional Features Gesture Recognition Jisuanji kexue human-computer interaction|gesture recognition|inertial sensor|motion capture |
| title | Real-time LSTM-based Multi-dimensional Features Gesture Recognition |
| title_full | Real-time LSTM-based Multi-dimensional Features Gesture Recognition |
| title_fullStr | Real-time LSTM-based Multi-dimensional Features Gesture Recognition |
| title_full_unstemmed | Real-time LSTM-based Multi-dimensional Features Gesture Recognition |
| title_short | Real-time LSTM-based Multi-dimensional Features Gesture Recognition |
| title_sort | real time lstm based multi dimensional features gesture recognition |
| topic | human-computer interaction|gesture recognition|inertial sensor|motion capture |
| url | http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-8-328.pdf |
| work_keys_str_mv | AT liuliangpuhaoyang realtimelstmbasedmultidimensionalfeaturesgesturerecognition |