Human activity recognition and deep learning are two fields that have attracted attention in recent years. The former due to its relevance in many application domains, such as ambient assisted living or health monitoring, and the latter for its recent and excellent performance achievements in different domains of application such as image and speech recognition. In this article, an extensive analysis among the most suited deep learning architectures for activity recognition is conducted to compare its performance in terms of accuracy, speed, and memory requirements. In particular, convolutional neural networks (CNN), long short‐term memory networks (LSTM), bidirectional LSTM (biLSTM), gated recurrent unit networks (GRU), and deep belief networks (DBN) have been tested on a total of 10 publicly available datasets, with different sensors, sets of activities, and sampling rates. All tests have been designed under a multimodal approach to take advantage of synchronized raw sensor' signals. Results show that CNNs are efficient at capturing local temporal dependencies of activity signals, as well as at identifying correlations among sensors. Their performance in activity classification is comparable with, and in most cases better than, the performance of recurrent models. Their faster response and lower memory footprint make them the architecture of choice for wearable and IoT devices.

中文翻译：

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用于人类活动识别的深度神经网络研究

人类活动识别和深度学习是近年来引起关注的两个领域。前者是由于其在许多应用领域中的相关性，例如环境辅助生活或健康监测，而后者则是由于其在不同应用领域（例如图像和语音识别）中的最新和出色的性能成就。在本文中，针对活动识别最适合的深度学习体系结构进行了广泛的分析，以比较其在准确性，速度和内存要求方面的性能。特别是，卷积神经网络（CNN），长短期记忆网络（LSTM），双向LSTM（biLSTM），门控递归单元网络（GRU）和深度信念网络（DBN）已在总共10个公开场合进行了测试。具有不同传感器的可用数据集，活动集和抽样率。所有测试均采用多模式方法进行设计，以利用同步原始传感器的信号。结果表明，CNN可以有效捕获活动信号的本地时间依赖性，并可以识别传感器之间的相关性。它们在活动分类中的表现与循环模型的表现相当，并且在大多数情况下要优于循环模型。它们更快的响应速度和更低的内存占用使它们成为可穿戴设备和IoT设备的首选架构。它们在活动分类中的表现与循环模型的表现相当，并且在大多数情况下要优于循环模型。它们更快的响应速度和更低的内存占用使它们成为可穿戴设备和IoT设备的首选架构。它们在活动分类中的表现与循环模型的表现相当，并且在大多数情况下要优于循环模型。它们更快的响应速度和更低的内存占用使它们成为可穿戴设备和IoT设备的首选架构。