IEEE J Biomed Health Inform. 2020 Jan;24(1):292-299. doi: 10.1109/JBHI.2019.2909688. Epub 2019 Apr 9.
Human activity recognition has been widely used in healthcare applications such as elderly monitoring, exercise supervision, and rehabilitation monitoring. Compared with other approaches, sensor-based wearable human activity recognition is less affected by environmental noise and therefore is promising in providing higher recognition accuracy. However, one of the major issues of existing wearable human activity recognition methods is that although the average recognition accuracy is acceptable, the recognition accuracy for some activities (e.g., ascending stairs and descending stairs) is low, mainly due to relatively less training data and complex behavior pattern for these activities. Another issue is that the recognition accuracy is low when the training data from the test subject are limited, which is a common case in real practice. In addition, the use of neural network leads to large computational complexity and thus high power consumption. To address these issues, we proposed a new human activity recognition method with two-stage end-to-end convolutional neural network and a data augmentation method. Compared with the state-of-the-art methods (including neural network based methods and other methods), the proposed methods achieve significantly improved recognition accuracy and reduced computational complexity.
人体活动识别已广泛应用于医疗保健应用中，如老年人监测、运动监督和康复监测。与其他方法相比，基于传感器的可穿戴人体活动识别受环境噪声的影响较小，因此有望提供更高的识别精度。然而，现有人体活动识别方法的主要问题之一是，尽管平均识别精度可以接受，但某些活动（例如上下楼梯）的识别精度较低，主要是由于这些活动的训练数据相对较少且行为模式复杂。另一个问题是，当测试对象的训练数据有限时，识别精度较低，这在实际实践中是很常见的。此外，神经网络的使用会导致计算复杂度大，从而功耗高。为了解决这些问题，我们提出了一种具有两级端到端卷积神经网络和数据增强方法的新的人体活动识别方法。与最先进的方法（包括基于神经网络的方法和其他方法）相比，所提出的方法显著提高了识别精度，同时降低了计算复杂度。
