Visual Cues to Improve Myoelectric Control of Upper Limb Prostheses
Visual Cues to Improve Myoelectric Control of Upper Limb Prostheses
复制标题
改善上肢假肢肌电控制的视觉提示
DOI：
10.1109/biorob.2018.8487923
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
B. Caputo
中科院分区：
文献类型：
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作者：
Andrea Gigli;A. Gijsberts;Valentina Gregori;Matteo Cognolato;M. Atzori;B. Caputo
随着时间的推移，肌电信号的不稳定性使其用于控制多关节假手变得复杂。为了解决这个问题，研究尝试将联合收割机表面肌电图与受截肢和环境影响较小的模态（例如加速度计和凝视信息）相结合。在后一种情况下，假设是受试者看着他或她打算操纵的对象，并且该对象的视觉特性允许更好地预测期望的手姿势。我们在本文中提出的方法自动检测稳定的凝视固定，并使用固定对象的视觉特性，以提高性能的多模态把握分类器。特别地，该算法在线识别注视点和相应注视点的发生，通过卷积神经网络获得注视对象的高级特征表示，并在分类阶段将其与传统的表面肌电相结合。已进行测试的数据从五个完整的主题谁进行了十种类型的把握在静态和功能任务的各种对象。结果表明，添加凝视信息增加了抓握分类精度，这种改进对于所有抓握是一致的，并且集中在运动开始和偏移期间。
The instability of myoelectric signals over time complicates their use to control poly-articulated prosthetic hands. To address this problem, studies have tried to combine surface electromyography with modalities that are less affected by the amputation and the environment, such as accelerometry and gaze information. In the latter case, the hypothesis is that a subject looks at the object he or she intends to manipulate, and that the visual characteristics of that object allow to better predict the desired hand posture. The method we present in this paper automatically detects stable gaze fixations and uses the visual characteristics of the fixated objects to improve the performance of a multimodal grasp classifier. Particularly, the algorithm identifies online the onset of a prehension and the corresponding gaze fixations, obtains high-level feature representations of the fixated objects by means of a Convolutional Neural Network, and combines them with traditional surface electromyography in the classification stage. Tests have been performed on data acquired from five intact subjects who performed ten types of grasps on various objects during both static and functional tasks. The results show that the addition of gaze information increases the grasp classification accuracy, that this improvement is consistent for all grasps and concentrated during the movement onset and offset.