引用本文:盛敏,刘双庆,王婕,等.基于改进模板匹配的智能下肢假肢运动意图实时识别[J].控制与决策,2020,35(9):2153-2161
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览次   下载 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于改进模板匹配的智能下肢假肢运动意图实时识别
盛敏1,2, 刘双庆1,2, 王婕2,3, 苏本跃2,3
(1. 安庆师范大学数学与计算科学学院,安徽安庆246133;2. 安徽省智能感知与计算重点实验室,安徽安庆246133;3. 安庆师范大学计算机与信息学院,安徽安庆246133)
摘要:
传统下肢假肢运动意图识别常使用多模态传感器采集残肢侧时频域特征,在短时意图识别中,具有一定的复杂性和滞后性,且时频域特征不稳定难以达到实时意图识别的目的.鉴于此,提出基于改进模板匹配技术的智能下肢假肢运动意图实时识别的方法.在重新定义单侧下肢截肢者的运动模式后,仅采用惯性传感器采集健肢侧位于摆动相的数据,基于改进的模板匹配,通过滑动窗口创建完备的模板库,使得每类运动模式在库中有充足的原子模式,对下肢假肢的运动意图进行实时识别.实验结果表明,所提出方法在5种稳态模式(平地行走、上下楼、上下坡)的识别率为99.50%,在引入8种转换模式后的识别率为97.03%,可以大大提高下肢假肢实时识别性能,助力单侧下肢截肢者更自然地行走.
关键词:  意图识别  完备模板库  原子模式  模板匹配  转换模式
DOI:10.13195/j.kzyjc.2019.0070
分类号:TP391
基金项目:国家自然科学基金项目(11475003,61603003,11471093);教育部“云数融合科教创新”基金项目(2017A09116);安徽省科技重大专项项目(18030901021);安徽省高校领军人才团队项目;安徽省高校优秀拔尖人才培育项目(gxbjZD26).
Real-time motion intent recognition of intelligent lower limb prosthesis based on improved template matching technique
SHENG Min1,2,LIU Shuang-qing1,2,WANG Jie2,3,SU Ben-yue2,3
(1. School of Mathematics and Computational Science,Anqing Normal University,Anqing246133,China;2. University Key Laboratory of Intelligent Perception and Computing of Anhui Province,Anqing246133,China;3. School of Computer and Information,Anqing Normal University,Anqing246133,China)
Abstract:
Traditional lower limb prosthesis motion intent recognition often uses multi-modal sensors to collect time-frequency domain features of the residual limb side. In short-term intent recognition, it has certain complexity and lag, and time-frequency domain features are unstable, which is difficult to achieve the purpose of real-time intent recognition. This paper proposes a real-time intelligent lower limb prosthesis motion intent recognition method based on improved template matching technology. After the amputee's motion pattern is acquired, onlys an inertial sensor is used to collect the data of the sound limb side in the swing phase. Based on the improved template matching, a complete template library is created through sliding window, so that each kind of motion pattern is sufficient atomic patterns in the library, and the motion intent of the lower limb prosthesis is recognized in real time. The experimental results show that the recognition rate of the algorithm reaches 99.50% in 5 steady states: level-ground walking, stair ascent, stair descent, ramp ascent and ramp descent, and 97.03% after introducing 8 transitional states. This method can greatly improve the real-time recognition performance of lower limb prosthesis and help unilateral lower limb amputees walk more naturally.
Key words:  motion intent recognition  complete template set  atomic pattern  improved template matching  transitional state

用微信扫一扫

用微信扫一扫