Identification of human physical activities is an active research area since long due to its application in personalized health and fitness monitoring. The performance accuracy of human activity recognition (HAR) models mainly depend on the features which are extracted from domain knowledge. The features are the input of the classification algorithm to efficiently identify human physical activities. Manually extracted features (handcrafted) need expert domain knowledge. Thus these features have significant importance to identify different human activities. Recently deep learning methods are utilized to extract the features automatically from raw sensory data for HAR models. However, state-of-the-art HAR literature established that the importance of handcrafted features can’t be ignored as it is extracted from expert domain knowledge. Thus, in this paper we use the fusion of both the handcrafted features and automatically extracted features using deep learning (DL) for HAR model to enhance the performance of HAR. Extensive experimental results demonstrate that our proposed feature fusion based HAR model gives higher accuracy compared with state-of-the-art HAR literature for both the self collected and public dataset.

由于其在个性化健康和健身监测中的应用，人类身体活动的识别长期以来一直是一个活跃的研究领域。人类活动识别（HAR）模型的性能准确性主要取决于从领域知识中提取的特征。这些特征是分类算法的输入，以有效地识别人类的身体活动。手动提取的特征（手工制作）需要专业领域知识。因此，这些特征对于识别不同的人类活动具有重要意义。最近，深度学习方法被用来从 HAR 模型的原始感官数据中自动提取特征。然而，最先进的 HAR 文献表明，手工特征的重要性不容忽视，因为它是从专家领域知识中提取的。因此，在本文中，我们将手工制作的特征和使用深度学习 (DL) 自动提取的特征融合到 HAR 模型中，以提高 HAR 的性能。广泛的实验结果表明，我们提出的基于特征融合的 HAR 模型与自收集和公共数据集的最新 HAR 文献相比具有更高的准确性。