论文标题
强制Myshotem -Benchmark数据进行手势识别和转移学习
Force myography benchmark data for hand gesture recognition and transfer learning
论文作者
论文摘要
力量密集图最近引起了人们对手势识别任务的越来越多的关注。但是，缺乏公开可用的基准数据，大多数现有研究经常用自定义硬件和各种手势集收集自己的数据。这限制了比较各种算法的能力，以及在不首先需要收集数据的情况下进行研究的可能性。我们通过使涵盖18个独特手势的20人的市售传感器设置收集的基准数据集为该领域的发展做出了贡献，以期使结果进一步比较，并更容易进入该研究领域。我们为此类数据说明了一个用例，显示了如何通过利用转移学习来合并来自其他多个人的数据来提高手势识别精度。这也说明数据集可以用作基准数据集，以促进有关转移学习算法的研究。
Force myography has recently gained increasing attention for hand gesture recognition tasks. However, there is a lack of publicly available benchmark data, with most existing studies collecting their own data often with custom hardware and for varying sets of gestures. This limits the ability to compare various algorithms, as well as the possibility for research to be done without first needing to collect data oneself. We contribute to the advancement of this field by making accessible a benchmark dataset collected using a commercially available sensor setup from 20 persons covering 18 unique gestures, in the hope of allowing further comparison of results as well as easier entry into this field of research. We illustrate one use-case for such data, showing how we can improve gesture recognition accuracy by utilising transfer learning to incorporate data from multiple other persons. This also illustrates that the dataset can serve as a benchmark dataset to facilitate research on transfer learning algorithms.
