| 基于柔性应变传感器的数据手套手势识别研究 |
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| 引用本文： | 朱银龙,沈宏骏,吴杰,王旭,刘英.基于柔性应变传感器的数据手套手势识别研究[J].农业机械学报,2024,55(6):451-458. |
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| 作者姓名： | 朱银龙 沈宏骏 吴杰 王旭 刘英 |
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| 作者单位： | 南京林业大学 |
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| 基金项目： | 国家自然科学基金项目（51305209）、江苏省高等学校自然科学研究项目（18KJA4600050、21KJB460010）、江苏省“六大人才高峰”高层次人才项目（GDZB-024）和机器人学国家重点实验室开放项目(2018-O16) |
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| 摘 要： | 针对传统手势识别系统识别率不高、响应不稳定等问题，设计了一个包括柔性传感器、信号采集系统、手势识别算法的柔性应变传感器数据手套手势识别系统。该系统可准确捕捉每根手指关节运动信息，具有高自由度、低成本、高识别率等特点。在软硅胶材料中掺杂特定配比的碳黑(CB)和碳纳米管(CNTs)，通过转印技术设计出线性度好、灵敏度高的电阻式传感器。实验结果表明，传感器具有较好的静态、动态响应特性，并完成传感器标定；利用多个柔性传感器制备数据手套并搭建信号采集系统，进一步提出融合BP神经网络和模板匹配技术的手势识别方法，以提升相近手势字母识别率，算法识别率为98.5%；针对不同人群开展手势识别实验，结果表明，该手势识别系统准确率达到92.8%，响应时间约40ms，该数据手套具有较好的应用潜力。
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| 关 键 词： | 柔性传感器 模板匹配法 BP神经网络 手势识别 数据手套 |
| 收稿时间： | 2023/10/19 0:00:00 |
Data Glove Gesture Recognition Based on Flexible Strain Sensors |
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| ZHU Yinlong,SHEN Hongjun,WU Jie,WANG Xu,LIU Ying.Data Glove Gesture Recognition Based on Flexible Strain Sensors[J].Transactions of the Chinese Society of Agricultural Machinery,2024,55(6):451-458. |
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| Authors: | ZHU Yinlong SHEN Hongjun WU Jie WANG Xu LIU Ying |
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| Institution: | Nanjing Forestry University |
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| Abstract: | In response to the problems of low recognition rate and unstable response in traditional gesture recognition systems, a flexible strain sensor data glove gesture recognition system was developed, which included flexible sensors, signal acquisition systems, and gesture recognition algorithms. The system can accurately capture the motion information of each finger joint, and had the characteristics of high degree of freedom, low cost and high recognition rate. Carbon black (CB) and carbon nanotubes (CNTs) were doped into soft silica gel, and a resistive sensor with good linearity and high sensitivity was designed by extension technology. The experimental results showed that the sensor had good static and dynamic response characteristics, and the sensor calibration was completed. Using multiple flexible sensors to prepare data gloves and build a signal acquisition system, a gesture recognition method combining BP neural network and template matching technology was further proposed to improve the recognition rate of similar gestures, and the recognition rate of the algorithm was 98.5%. Gesture recognition experiments were carried out for different groups of people. The results showed that the accuracy of the gesture recognition system reached 92.8%, and the response time was about 40ms. The data glove had good application potential. |
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| Keywords: | flexible sensors template matching method BP neural network gesture recognition data glove |
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