The field of Human Activity Recognition (HAR) has experienced a significant surge in interest due to its essential role across numerous areas, including human–computer interaction (HCI), healthcare, smart homes, and various Internet of Things (IoT) applications. The power of deep learning methods in performing various classification tasks, including HAR, has been well-demonstrated. In light of this, our paper presents an efficient HAR system developed using a unique deep-learning architecture called TCN-Inception, which is designed for multivariate time series tasks like HAR data, by combining Temporal Convolutional Network (TCN) and Inception modules. The network starts with an Inception module that uses parallel convolution layers with various kernel sizes for feature extraction. It then includes a TCN module with dilated convolutions to grasp extended temporal patterns. Features are merged from different channels, and the use of residual connections and batch normalization improves training and deepens the architecture. We use four public datasets, UCI-HAR, MobiAct, Daphnet, and DSADS to assess the performance of the TCN-Inception model, and it obtains an average accuracy of 96.15%, 98.86%, 92.63%, and 99.50% for each dataset, respectively. Moreover, we compare the TCN-Inception to several deep learning frameworks to verify its performance. Finally, we implement an ablation study using several architectural configurations of the TCN-Inception model.

中文翻译：

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TCN-Inception：用于基于传感器的人类活动识别的时间卷积网络和 Inception 模块


人类活动识别 (HAR) 领域因其在人机交互 (HCI)、医疗保健、智能家居和各种物联网 (IoT) 应用等众多领域的重要作用而受到极大关注。深度学习方法在执行各种分类任务（包括 HAR）方面的强大功能已得到充分证明。有鉴于此，我们的论文提出了一种使用称为 TCN-Inception 的独特深度学习架构开发的高效 HAR 系统，该系统通过结合时间卷积网络（TCN）和 Inception 模块，专为 HAR 数据等多元时间序列任务而设计。该网络从 Inception 模块开始，该模块使用具有不同内核大小的并行卷积层进行特征提取。然后，它包含一个带有扩张卷积的 TCN 模块，以掌握扩展的时间模式。合并来自不同通道的特征，并使用残差连接和批量归一化改进训练并深化架构。我们使用 UCI-HAR、MobiAct、Daphnet 和 DSADS 四个公共数据集来评估 TCN-Inception 模型的性能，每个数据集的平均准确率分别为 96.15%、98.86%、92.63% 和 99.50%，分别。此外，我们将 TCN-Inception 与几个深度学习框架进行比较以验证其性能。最后，我们使用 TCN-Inception 模型的几种架构配置来实施消融研究。