Particle Filters vs Hidden Markov Models for Prosthetic Robot Hand Grasp Selection
Particle Filters vs Hidden Markov Models for Prosthetic Robot Hand Grasp Selection
复制标题
用于假肢机器人手抓握选择的粒子滤波器与隐马尔可夫模型
DOI：
10.35708/rc1868-126253
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
M. Sharif
中科院分区：
文献类型：
--
作者：
M. Sharif
机器人假手通常使用肌电(EMG)信号作为推断用户意图的手段来控制。然而，仅依靠肌电信号，尽管在实验室设置中提供了非常好的结果，但在现实生活条件下并不够健壮。为此，前人提出了利用其他语境线索的方法。本文提出了一种基于手部轨迹信息的粒子滤波意图推理方法。我们的方法还提供了到达时间的估计，即到达对象之前的剩余时间，这是成功抓住对象的基本变量。建议的概率框架可以结合可用的信息来源来改进推理过程。我们还提出了一种基于隐马尔可夫模型(HMM)的数据驱动的意图推理方法。隐马尔可夫模型被广泛用于人体手势分类。这些算法针对从10个受试者一次到达四个对象中的一个收集的160个到达轨迹进行了测试(和训练)。
Robotic prosthetic hands are commonly controlled using electromyography (EMG) signals as a means of inferring user intention. However, relying on EMG signals alone, although provides very good results in lab settings, is not sufficiently robust to real-life conditions. For this reason, taking advantage of other contextual clues are proposed in previous works. In this work, we propose a method for intention inference based on particle filtering (PF) based on user hand's trajectory information. Our methodology, also provides an estimate of time-to-arrive, i.e. time left until reaching to the object, which is an essential variable in successful grasping of objects. The proposed probabilistic framework can incorporate available sources of information to improve the inference process. We also provide a data-driven method based on hidden Markov model (HMM) as a baseline for intention inference. HMM is widely used for human gesture classification. The algorithms were tested (and trained) with regards to 160 reaching trajectories collected from 10 subjects reaching to one of four objects at a time.