The surveillance of indoor air quality is paramount for ensuring environmental safety, a task made increasingly viable due to advancements in technology and the application of artificial intelligence and deep learning (DL) tools. This paper introduces an intelligent system dedicated to monitoring air quality and categorizing activities within indoor environments using a DL approach based on 1D Convolutional Neural Networks (1D-CNNs). Our system integrates six diverse sensors to gather measurement parameters, which subsequently train a 1D CNN model for activity recognition. This proposed model boasts a lightweight and edge-deployable design, rendering it ideal for real-time applications. We conducted our experiments utilizing an air quality dataset specifically designed for Activity of Daily Living (ADL) classification. The results illustrate the proposed model's efficacy, achieving a remarkable accuracy of 97.00%, a minimal loss value of 0.15%, and a swift prediction time of 41 milliseconds.

翻译：室内空气质量监测对于确保环境安全至关重要，得益于技术进步以及人工智能和深度学习工具的应用，这一任务正变得日益可行。本文介绍了一种智能系统，该系统基于一维卷积神经网络的深度学习方法，专用于监测室内环境空气质量并分类其中的活动。我们的系统集成了六种不同的传感器以收集测量参数，随后这些参数用于训练一个一维卷积神经网络模型进行活动识别。所提出的模型具有轻量级且可部署于边缘设备的设计特点，使其非常适合实时应用。我们利用一个专门为日常生活活动分类设计的空气质量数据集进行了实验。结果表明，该模型性能显著，达到了97.00%的卓越准确率、0.15%的最小损失值，以及41毫秒的快速预测时间。