Human Activity Recognition (HAR) has attracted much attention from researchers in the recent past. The intensification of research into HAR lies in the motive to understand human behaviour and inherently anticipate human intentions. Human activity data obtained via wearable sensors like gyroscope and accelerometer is in the form of time series data, as each reading has a timestamp associated with it. For HAR, it is important to extract the relevant temporal features from raw sensor data. Most of the approaches for HAR involves a good amount of feature engineering and data pre-processing, which in turn requires domain expertise. Such approaches are time-consuming and are application-specific. In this work, a Deep Neural Network based model, which uses Convolutional Neural Network, and Gated Recurrent Unit is proposed as an end-to-end model performing automatic feature extraction and classification of the activities as well. The experiments in this work were carried out using the raw data obtained from wearable sensors with nominal pre-processing and don’t involve any handcrafted feature extraction techniques. The accuracies obtained on UCI-HAR, WISDM, and PAMAP2 datasets are 96.20%, 97.21%, and 95.27% respectively. The results of the experiments establish that the proposed model achieved superior classification performance than other similar architectures.

最近，人类活动识别（HAR）引起了研究人员的广泛关注。加强对HAR的研究在于了解人类行为并固有地预测人类意图的动机。通过可穿戴式传感器（例如陀螺仪和加速度计）获得的人类活动数据采用时间序列数据的形式，因为每个读数都具有与之相关的时间戳。对于HAR，从原始传感器数据中提取相关的时间特征非常重要。大多数用于HAR的方法都涉及大量的特征工程和数据预处理，这又需要领域专业知识。这样的方法是耗时的并且是针对特定应用的。在这项工作中，使用了卷积神经网络的基于深度神经网络的模型，提出了“门控循环单元”作为端对端模型，该模型还执行活动的自动特征提取和分类。这项工作中的实验是使用从可穿戴式传感器获得的原始数据进行的，并经过名义上的预处理，并且不涉及任何手工特征提取技术。在UCI-HAR，WISDM和PAMAP2数据集上获得的准确度分别为96.20％，97.21％和95.27％。实验结果表明，提出的模型比其他类似体系结构具有更好的分类性能。在UCI-HAR，WISDM和PAMAP2数据集上获得的准确度分别为96.20％，97.21％和95.27％。实验结果表明，提出的模型比其他类似体系结构具有更好的分类性能。在UCI-HAR，WISDM和PAMAP2数据集上获得的准确度分别为96.20％，97.21％和95.27％。实验结果表明，提出的模型比其他类似体系结构具有更好的分类性能。