孙淑睿,黄品高,汪鑫,陈世雄,李光林.基于不同训练模式的纳米金柔性传感器的 手势动作识别[J].集成技术,2020,(2):1-8
基于不同训练模式的纳米金柔性传感器的 手势动作识别
Nano-Gold Flexible Sensor Based Gesture Motion Recognition withDifferent Training Modes
  
DOI:10.12146/j.issn.2095-3135.20191122001
中文关键词:  纳米金柔性传感器;训练模式;模式识别
英文关键词:nano-gold flexible sensor; training mode; pattern recognition
基金项目:深圳市基础研究学科布局项目(JCYJ20180507182241622);国家自然科学基金项目(61771462);广州市科技计划项目 (201803010093)
作者单位
孙淑睿 中国科学院深圳先进技术研究院 深圳 518055;重庆理工大学药学与生物工程学院 重庆 400054 
黄品高 中国科学院深圳先进技术研究院 深圳 518055;中国科学院大学深圳先进技术学院 深圳 518055 
汪鑫 中国科学院深圳先进技术研究院 深圳 518055;中国科学院大学深圳先进技术学院 深圳 518055 
陈世雄 中国科学院深圳先进技术研究院 深圳 518055 
李光林 中国科学院深圳先进技术研究院 深圳 518055 
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中文摘要:
      假肢控制技术研究中,研究者们通常利用解码表面肌电信号(Surface Electromyography,sEMG)来获得截肢者的运动意图。传统的 sEMG 采集中,为降低皮肤与电极之间的阻抗,通常需要涂导电膏后与皮肤直接接触,因此会导致部分患者皮肤过敏和身体不适,且 sEMG 容易受肌肉疲劳影响,不利于长期监测。针对以上问题,该研究采用一种纳米金柔性传感器,解码其因肌肉收缩拉伸后产生的形变信号,并探究两种不同训练模式在此方法下的分类性能。其中,训练模式包括:重复训练模式,即每组重复做同一个动作;随机训练模式,即动作顺序随机化,每组每个动作只做一次。结果表明,所有受试者使用纳米金柔性传感器的平均手势识别率均在 90% 以上,且两种训练模式间无显著性差异(重复训练模式为 95.46%、随机训练模式为94.18%,P 值为 0.227 5)。这表明纳米金柔性传感器与湿电极一样,可以实现可靠的手势识别。
英文摘要:
      In the study of prosthetic control techniques, researchers usually decode surface electromyography (sEMG) to obtain the amputee’s intention of motion. The traditional sEMG electrode usually requires direct contact with the skin by conductive paste to reduce the impedance between the skin and the electrode, which may cause skin allergies and physical discomfort. sEMG is also easily affected by muscle fatigue, which is inconvenient in long-term monitoring. To address the above issues, this study used a nano-gold flexible sensor to decode the deformation signal generated by muscle contraction and explored the classification performance of two different training modes. The first mode was the sequential training mode, where each action was repeated three times, and the second one was the random training mode, where the order of actions was randomized, and each action only appeared once. The results show that the average gesture recognition rate of all subjects is above 90%, and there is no significant difference between the two training modes (sequential training mode is 95.46%, random training mode is 94.18%, P-value is 0.227 5). The experimental results demonstrate that the nano-gold flexible sensor, like the wet electrode, enables reliable gesture recognition.
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