Estimation of Grasp States in Prosthetic Hands using Deep Learning
Estimation of Grasp States in Prosthetic Hands using Deep Learning
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DOI：
10.1109/compsac48688.2020.00-79
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
V. Parque;T. Miyashita
中科院分区：
文献类型：
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作者：
V. Parque;T. Miyashita
肌电假手抓握状态的估计与人机工程学接口、控制和康复措施有关。在本文中，我们评估了通过在涉及亮度，对比度和翻转变化的测试场景中使用众所周知的深度学习架构来从RGB帧推断假手的抓握状态的可能性。我们的结果表明，使用相对较少的训练帧，通过基于GoogLeNet的深度架构来估计假手姿势是可行的、具有吸引力的准确性和效率。
The estimation of grasp states in myoelectric prosthetic hands is relevant for ergonomic interfacing, control and rehabilitation initiatives. In this paper we evaluate the possibility to infer the grasp state of a prosthetic hand from RGB frames by using well-known deep learning architectures in testing scenarios involving variations of brightness, contrast and flips. Our results show the feasibility, the attractive accuracy and efficiency to estimate prosthetic hand poses with a GoogLeNet-based deep architecture using relatively few training frames.