IEEE Trans Biomed Eng. 2018 Aug;65(8):1759-1770. doi: 10.1109/TBME.2017.2776157. Epub 2017 Nov 21.
The intent recognizers of advanced lower limb prostheses utilize mechanical sensors on the prosthesis and/or electromyographic measurements from the residual limb. Besides the delay caused by these signals, such systems require user-specific databases to train the recognizers. In this paper, our objective is the development and validation of a user-independent intent recognition framework utilizing depth sensing.
We collected a depth image dataset from 12 healthy subjects engaging in a variety of routine activities. After filtering the depth images, we extracted simple features employing a recursive strategy. The feature vectors were classified using a support vector machine. For robust activity mode switching, we implemented a voting filter scheme.
The model selection showed that the support vector machine classifier with no dimension reduction has the highest classification accuracy. Specifically, it reached 94.1% accuracy on the testing data from four subjects. We also observed a positive trend in the accuracy of classifiers trained with data from increasing the number of subjects. Activity mode switching using a voting filter detected 732 out of 778 activity mode transitions of the four users while initiating 70 erroneous transitions during steady-state activities.
The intent recognizer trained on multiple subjects can be used for any other subject, providing a promising solution for supervisory control of powered lower limb prostheses.
A user-independent intent recognition framework has the potential to decrease or eliminate the time required for extensive data collection regiments for intent recognizer training. This could accelerate the introduction of robotic lower limb prostheses to the market.
先进下肢假肢的意图识别器利用假肢上的机械传感器和/或残肢的肌电图测量值。除了这些信号引起的延迟外，此类系统还需要用户特定的数据库来训练识别器。在本文中，我们的目标是开发和验证一种利用深度感应的用户独立意图识别框架。
我们从 12 名健康受试者中收集了一个进行各种日常活动的深度图像数据集。在过滤深度图像后，我们使用递归策略提取简单特征。使用支持向量机对特征向量进行分类。为了实现稳健的活动模式切换，我们实现了投票滤波器方案。
模型选择表明，没有降维的支持向量机分类器具有最高的分类准确性。具体来说，它在来自四个受试者的测试数据上达到了 94.1%的准确率。我们还观察到，随着训练数据中受试者数量的增加，分类器的准确性呈正趋势。使用投票滤波器进行的活动模式切换检测到四个用户中的 732 次活动模式转换，而在稳态活动中发起了 70 次错误转换。
在多个受试者上训练的意图识别器可以用于任何其他受试者，为动力下肢假肢的监督控制提供了有前途的解决方案。
用户独立的意图识别框架有可能减少或消除意图识别器训练所需的广泛数据收集时间。这可以加速机器人下肢假肢推向市场。
