In recent years, the adoption of machine learning has grown steadily in different fields affecting the day-to-day decisions of individuals. This paper presents an intelligent system for recognizing human’s daily activities in a complex IoT environment. An enhanced model of capsule neural network called 1D-HARCapsNe is proposed. This proposed model consists of convolution layer, primary capsule layer, activity capsules flat layer and output layer. It is validated using WISDM dataset collected via smart devices and normalized using the random-SMOTE algorithm to handle the imbalanced behavior of the dataset. The experimental results indicate the potential and strengths of the proposed 1D-HARCapsNet that achieved enhanced performance with an accuracy of 98.67%, precision of 98.66%, recall of 98.67%, and F1-measure of 0.987 which shows major performance enhancement compared to the Conventional CapsNet (accuracy 90.11%, precision 91.88%, recall 89.94%, and F1-measure 0.93).

近年来，机器学习的采用在不同领域稳步增长，影响着个人的日常决策。本文提出了一种智能系统，用于在复杂的物联网环境中识别人类的日常活动。提出了一种称为 1D-HARCapsNe 的胶囊神经网络增强模型。该模型由卷积层、主胶囊层、活动胶囊平面层和输出层组成。它使用通过智能设备收集的 WISDM 数据集进行验证，并使用随机 SMOTE 算法进行标准化，以处理数据集的不平衡行为。实验结果表明了所提出的 1D-HARCapsNet 的潜力和优势，它实现了增强的性能，准确度为 98.67%，精确度为 98.66%，召回率为 98.67%，F1 测量为 0.987，与传统方法相比，性能显着增强CapsNet（准确度 90.11%，精确度 91.88%，召回率 89.94%，F1 测量 0.93）。