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工程设计学报
工程设计学报  2019, Vol. 26 Issue (3): 260-266    DOI: 10.3785/j.issn.1006-754X.2019.03.003
创新设计     
基于足底压力传感器的不控制减重比例下步态相位识别
宋广玥, 宋智斌, 项忠霞
天津大学 机械工程学院 机构理论与装备设计教育部重点实验室，天津300350
Gait phase recognition under proportion-uncontrolled body weight support based on plantar pressure sensor
SONG Guang-yue, SONG Zhi-bin, XIANG Zhong-xia
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering,Tianjin University, Tianjin 300350, China
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摘要：

下肢外骨骼机器人是帮助下肢运动功能障碍患者步行训练的新手段，能够减轻医护人员的劳动强度，它常采用减重方式完成辅助训练。然而，对于地面行走减重外骨骼机器人系统而言，其减重比例随步态及穿戴方式变化而变化，因此不控制减重比例下的步态相位识别具有重要意义。通过搭建基于Arduino Mega2560板卡和单侧鞋内8个薄膜式压力传感器的足底压力采集系统，分别采集了正常行走、不控制减重比例减重带减重状态下行走时的足底压力信息，并采用神经网络算法进行步态相位识别。结果表明：在减重状态下行走与正常行走相比，左右脚压力值均出现明显下降且两侧具有对称性；足底每个压力传感器处的压力减小比例不同；采用神经网络算法对正常行走时步态相位总体识别率达到96.8%，对减重行走时步态相位总体识别率达到94.8%。研究结果表明该足底压力采集系统可以有效测量减重行走时的足底压力，为在地面不控制减重比例下减重带减重的外骨骼机器人控制策略的制定提供一定支持。
关键词： 步态相位识别;  足底压力;  减重;  神经网络    
Abstract:

The lower extremity exoskeleton robot is a new means to help patients with lower extremity motor dysfunction, which can reduce the labor intensity of therapists. Body weight support is often used to complete the auxiliary training. However, for the ground walking weight-loss exoskeleton robot system, the ratio of body weight support would change with the gait and wearing situation, so it is important to recognize the gait phase under proportion-uncontrolled body weight support. A plantar pressure acquisition system based on the Arduino Mega2560 board and eight single-membrane pressure sensors in one side shoe was established, and the plantar pressure information of normal walking and body weight support walking with weight-loss belt under proportion-uncontrolled body weight support was collected. Then, the gait phase recognition was carried out using neural network algorithm. The results showed that the pressure values of left and right feet significantly decreased and two sides were symmetrical in body weight support walking compared with normal walking, but the pressure reduction ratio of each pressure sensor was different. The gait phase recognition rate of normal walking reached 96.8% and the gait phase recognition rate of body weight support walking could still reach 94.8% by using neural network algorithm. The research results show that the plantar pressure acquisition system can effectively measure the plantar pressure under body weight support walking, and provide some support for the formulation of control strategy for body weight support exoskeleton robots with weight-loss belt under proportion-uncontrolled body weight support on the ground.
Key words: gait phase recognition    plantar pressure    body weight support    neural network
收稿日期: 2018-10-17 出版日期: 2019-06-28
CLC:  TP 391.4  
基金资助:

国家自然科学基金资助项目（51475332）；天津市自然科学基金资助项目（17JCZDJC30300）
通讯作者: 宋智斌(1983—)，男，天津人，副教授，博士，从事康复机器人研究，E-mail:songzhibin@tju.edu.cn，https://orcid.org/0000-0002-3715-2003     E-mail: songzhibin@tju.edu.cn
作者简介: 宋广玥(1993—)，男，山东德州人，硕士生，从事下肢康复机器人研究，E-mail:SGY2058@163.com，https://orcid.org/0000-0002-1542-3749
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引用本文:

宋广玥, 宋智斌, 项忠霞. 基于足底压力传感器的不控制减重比例下步态相位识别[J]. 工程设计学报, 2019, 26(3): 260-266.

SONG Guang-yue, SONG Zhi-bin, XIANG Zhong-xia. Gait phase recognition under proportion-uncontrolled body weight support based on plantar pressure sensor. Chinese Journal of Engineering Design, 2019, 26(3): 260-266.

链接本文:

https://www.zjujournals.com/gcsjxb/CN/10.3785/j.issn.1006-754X.2019.03.003    或    https://www.zjujournals.com/gcsjxb/CN/Y2019/V26/I3/260

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