Quite a few people in the world have to stay under permanent surveillance for health reasons; they include diabetic people or people with some other chronic conditions, the elderly and the disabled.These groups may face heightened risk of having life-threatening falls or of being struck by a syncope. Due to limited availability of resources a substantial part of people at risk can not receive necessary monitoring and thus are exposed to excessive danger. Nowadays, this problem is usually solved via applying Human Activity Recognition (HAR) methods. HAR is a perspective and fast-paced Data Science field, which has a wide range of application areas such as healthcare, sport, security etc. However, the currently techniques of recognition are markedly lacking in accuracy, hence, the present paper suggests a highly accurate method for human activity classification. Wepropose a new workflow to address the HAR problem and evaluate it on the UniMiB SHAR dataset, which consists of the accelerometer signals. The model we suggest is based on continuous wavelet transform (CWT) and convolutional neural networks (CNNs). Wavelet transform localizes signal features both in time and frequency domains and after that a CNN extracts these features and recognizes activity. It is also worth noting that CWT converts 1D accelerometer signal into 2D images and thus enables to obtain better results as 2D networks have a significantly higher predictive capacity. In the course of the work we build a convolutional neural network and vary such model parameters as number of spatial axes, number of layers, number of neurons in each layer, image size, type of mother wavelet, the order of zero moment of mother wavelet etc. Besides, we also apply models with residual blocks which resulted in significantly higher metric values. Finally, we succeed to reach 99.26 % accuracy and it is a worthy performance for this problem.

翻译：由于健康原因,世界上相当多的人必须长期受到监视;他们包括糖尿病患者或患有其他慢性病的人、老年人和残疾人。这些群体可能面临生命受到威胁的下降或被同步撞击的高度风险。由于资源有限,相当一部分面临风险的人无法得到必要的监测,因此面临过度的危险。现在,这个问题通常通过应用人类活动识别(HAR)方法来解决。HAR是一个视角和快速速度的数据科学参数,其应用领域包括保健、体育、安全等广泛的应用领域。然而,目前的认识技术明显缺乏准确性,因此,本文件显示了一种非常准确的人类活动分类方法。由于资源有限,很大一部分面临风险的人无法接受必要的监测,因而面临过度的危险。现在,这一问题通常通过应用人类活动识别(HAR)方法来解决。我们建议的模式基于连续的波变模型(CWT)和动态神经网络(CNNs) 。波变本地化信号在时间和频域中都具有价值的信号特征,因此本文显示了一种非常高的准确性能,因此,我们也可以将这些信号网络的运行结果转换为C级。