论文标题
宴会厅舞蹈运动使用智能手表认可
Ballroom Dance Movement Recognition Using a Smart Watch
论文作者
论文摘要
惯性测量单元（IMU）传感器正在越来越多地用于检测人类的手势和运动。使用单个IMU传感器，全身运动识别仍然是一个严重的问题，因为传感器可能无法充分捕获运动。在本文中，我们在宴会厅舞蹈中使用单个智能手表介绍了全身运动检测研究。深度学习表示形式用于对定义明确的运动序列进行分类，称为\ emph {figures}。发现这些表示胜过随机森林和隐藏的马尔可夫模型的合奏。 85.95％的分类精度通过将舞蹈建模为一阶马尔可夫链，并纠正立即图的一阶马尔可夫链，将85.95％的分类精度提高到92.31 \％。
Inertial Measurement Unit (IMU) sensors are being increasingly used to detect human gestures and movements. Using a single IMU sensor, whole body movement recognition remains a hard problem because movements may not be adequately captured by the sensor. In this paper, we present a whole body movement detection study using a single smart watch in the context of ballroom dancing. Deep learning representations are used to classify well-defined sequences of movements, called \emph{figures}. Those representations are found to outperform ensembles of random forests and hidden Markov models. The classification accuracy of 85.95\% was improved to 92.31\% by modeling a dance as a first-order Markov chain of figures and correcting estimates of the immediately preceding figure.
