School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2023 Jun 2;23(11):5277. doi: 10.3390/s23115277.
Accurate recognition of disabled persons' behavioral intentions is the key to reconstructing hand function. Their intentions can be understood to some extent by electromyography (EMG), electroencephalogram (EEG), and arm movements, but they are not reliable enough to be generally accepted. In this paper, characteristics of foot contact force signals are investigated, and a method of expressing grasping intentions based on hallux (big toe) touch sense is proposed. First, force signals acquisition methods and devices are investigated and designed. By analyzing characteristics of signals in different areas of the foot, the hallux is selected. The peak number and other characteristic parameters are used to characterize signals, which can significantly express grasping intentions. Second, considering complex and fine tasks of the assistive hand, a posture control method is proposed. Based on this, many human-in-the-loop experiments are conducted using human-computer interaction methods. The results showed that people with hand disabilities could accurately express their grasping intentions through their toes, and could accurately grasp objects of different sizes, shapes, and hardness using their feet. The accuracy of the action completion for single-handed and double-handed disabled individuals was 99% and 98%, respectively. This proves that the method of using toe tactile sensation for assisting disabled individuals in hand control can help them complete daily fine motor activities. The method is easily acceptable in terms of reliability, unobtrusiveness, and aesthetics.
准确识别残疾人的行为意图是重建手部功能的关键。通过肌电图(EMG)、脑电图(EEG)和手臂运动可以在一定程度上理解他们的意图,但还不够可靠,无法被普遍接受。本文研究了足底接触力信号的特征,并提出了一种基于大脚趾触感的抓握意图表达方法。首先,研究并设计了力信号采集方法和设备。通过分析足部不同区域信号的特征,选择大脚趾作为研究对象。利用峰值数量和其他特征参数来对信号进行特征描述,从而能够显著表达抓握意图。其次,考虑到辅助手的复杂和精细任务,提出了一种姿态控制方法。在此基础上,采用人机交互方法进行了多次人机交互实验。结果表明,手部残疾者可以通过脚趾准确表达抓握意图,并且可以用脚准确抓取不同大小、形状和硬度的物体。单手和双手残疾个体的动作完成准确率分别为 99%和 98%。这证明了使用脚趾触觉辅助残疾人手部控制的方法可以帮助他们完成日常精细运动活动。该方法在可靠性、非侵入性和美观性方面都很容易被接受。