Human-machine interfaces based on EMG and EEG applied to robotic systems
Human-machine interfaces based on EMG and EEG applied to robotic systems
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DOI：
10.1186/1743-0003-5-10
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发表时间：
2008-03-26
影响因子：
5.1
通讯作者：
Carelli, Ricardo
中科院分区：
文献类型：
--
作者：
Ferreira, Andre;Celeste, Wanderley C.;Carelli, Ricardo
背景：开发了两种不同的人机界面（HMI），两者都基于电生物信号。一种是基于EMG信号，另一种是基于EEG信号。这种接口的两个主要特征是其相对简单的数据采集和处理系统，其仅需要一些硬件和软件资源，因此从计算和财务角度讲，它们是低成本的解决方案。这两个接口被应用到机器人系统，他们的性能进行了分析。基于EMG的HMI在移动的机器人中进行了测试，而基于EEG的HMI在移动的机器人和机器人manipulator.Results中进行了测试：使用基于EMG的HMI的实验进行了八个人，谁被要求完成10眨眼与每只眼睛，以测试眨眼检测算法。具有眨眼能力的个体达到的平均正确率约为95%，这使得可以得出结论，该系统可以用于命令设备。脑电图实验包括邀请25人（其中一些人患有脑膜炎和癫痫）来测试系统。他们所有人都在一次培训中成功地处理了HMI。他们中的大多数人在不到15分钟的时间内学会了如何使用这种HMI。观察到的最小和最大训练时间分别为3和50 minutes.Conclusion：这样的作品是一个系统的初始部分，以帮助人们与神经运动疾病，包括那些有严重的功能障碍。接下来的步骤是将商用轮椅转换为自主移动的车辆;在自主轮椅上实现HMI，从而帮助患有运动疾病的人，并探索EEG信号的潜力，使基于EEG的HMI更加强大和快速，旨在使用它来帮助患有严重运动功能障碍的人。
Background: Two different Human-Machine Interfaces (HMIs) were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well.Results: Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people ( some of them had suffered cases of meningitis and epilepsy) to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively.Conclusion: Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.