论文标题
使用多头CNN的人类活动识别，然后是LSTM
Human Activity Recognition using Multi-Head CNN followed by LSTM
论文作者
论文摘要
这项研究提出了一种新的方法，可以使用CNN识别人类体育活动，然后是LSTM。通过传统的机器学习算法（例如SVM，KNN和随机森林方法）实现高精度是一项艰巨的任务，因为从加速度计和陀螺仪（例如加速度计和陀螺仪）中获取的数据是时间序列数据。因此，为了达到高精度，我们提出了一个由三个CNN组成的多头CNN模型，以提取从不同传感器中获取的数据的功能，然后将所有三个CNN合并，然后是LSTM层和密集层。所有三个CNN的配置都保持不变，以便为CNN的每个输入获得相同数量的功能。通过使用所提出的方法，我们实现了最先进的准确性，这与传统的机器学习算法和其他深神经网络算法相媲美。
This study presents a novel method to recognize human physical activities using CNN followed by LSTM. Achieving high accuracy by traditional machine learning algorithms, (such as SVM, KNN and random forest method) is a challenging task because the data acquired from the wearable sensors like accelerometer and gyroscope is a time-series data. So, to achieve high accuracy, we propose a multi-head CNN model comprising of three CNNs to extract features for the data acquired from different sensors and all three CNNs are then merged, which are followed by an LSTM layer and a dense layer. The configuration of all three CNNs is kept the same so that the same number of features are obtained for every input to CNN. By using the proposed method, we achieve state-of-the-art accuracy, which is comparable to traditional machine learning algorithms and other deep neural network algorithms.
