利用深度学习进行特征融合,实现基于智能手机的人类活动识别。

Dipanwita Thakur, Suparna Biswas
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引用次数: 0

摘要

长期以来,人类体能活动识别一直是一个活跃的研究领域,因为它可应用于个性化健康和体能监测。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,用于有效识别人类的身体活动。人工提取的特征(手工制作)需要专家的领域知识。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,有人利用深度学习方法从原始感官数据中自动提取特征,用于 HAR 模型。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,我们使用深度学习(DL)将手工特征和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,与最先进的 HAR 文献相比,我们提出的基于特征融合的 HAR 模型在自收集数据集和公共数据集上都具有更高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Feature fusion using deep learning for smartphone based human activity recognition.

Feature fusion using deep learning for smartphone based human activity recognition.

Feature fusion using deep learning for smartphone based human activity recognition.

Feature fusion using deep learning for smartphone based human activity recognition.

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.

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