论文标题
在医疗保健领域的手势识别的手势识别功能选择
Feature selection for gesture recognition in Internet-of-Things for healthcare
论文作者
论文摘要
物联网正在迅速跨越多个领域，包括医疗保健，提出与通信能力，能源效率和传感器有关的相关问题。特别是，在识别手势的背景下，可以分别通过脑电图和EMG同时记录不同物体，大脑和肌肉活动的抓地力，并进行了分析以识别已完成的手势及其性能的质量。本文提出了一种新的算法，该算法旨在（i）鲁棒提取最相关的功能以对不同的掌握任务进行分类，并且（ii）保留所选功能的自然含义。反过来，这有机会简化录制设置，以最大程度地减少通信网络（包括互联网）的数据流量，并为医学解释提供生理上重要的功能。通过共识聚类作为特征选择策略以及通过嵌套的交叉验证方案来确保算法的鲁棒性来确保算法的鲁棒性。
Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition of gestures, e.g., grasping of different objects, brain and muscular activity could be simultaneously recorded via EEG and EMG, respectively, and analyzed to identify the gesture that is being accomplished, and the quality of its performance. This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features. This, in turn, gives the opportunity to simplify the recording setup to minimize the data traffic over the communication network, including Internet, and provide physiologically significant features for medical interpretation. The algorithm robustness is ensured both by consensus clustering as a feature selection strategy, and by nested cross-validation scheme to evaluate its classification performance.
