University of South Florida, Tampa, FL 33620, USA.
University of South Florida, Tampa, FL 33620, USA.
J Biomed Inform. 2022 Oct;134:104180. doi: 10.1016/j.jbi.2022.104180. Epub 2022 Aug 27.
Deep learning versus feature engineering has drawn significant attention specifically for applications where expertly crafted features have been used for decades. Human activity recognition is no exception where statistical and motion specific features have shown potential in detecting falls and other daily activities across a wide range of datasets. This paper provides an in-depth study and comparison of two fundamentally different approaches to HAR while introducing a novel way to harness the spectral properties of biological time series in addition to temporal features. A research group at Stanford recently proposed subject agnostic features as state-of-the-art when applied to a large dataset with many participants of different ages. In this paper, we demonstrate that implicit feature learning in the latent spaces of deep learning algorithms can be powerful alternatives to using finely tuned domain-specific features for HAR. In fact, when using a spectrotemporal representation of the raw sensor data in the form of spectrograms, a standard convolutional neural network without any prior conditioning on the features, statistically significantly outperforms the prior state-of-the-art using subject agnostic features in all the different partitions of the dataset with a significant 29.8% reduction in the overall average error rate.
深度学习与特征工程都受到了广泛关注，特别是在那些需要使用专家精心设计的特征的应用中。人类活动识别也不例外，统计和运动特定特征在检测跌倒和其他日常活动方面已经在各种数据集上显示出了潜力。本文深入研究和比较了两种基本不同的 HAR 方法，同时引入了一种新的方法，利用生物时间序列的光谱特性来补充时间特征。斯坦福的一个研究小组最近提出，当应用于具有许多不同年龄参与者的大型数据集时，基于无监督学习的特征是当前的最新技术。在本文中，我们证明了深度学习算法的潜在空间中的隐式特征学习可以替代使用精细调整的特定领域的特征进行 HAR。实际上，当使用以声谱图形式的原始传感器数据的时频表示时，一个标准的卷积神经网络，无需对特征进行任何预先处理，在数据集的所有不同分区中，在统计上显著优于使用基于无监督学习的特征的先前的最新技术，总体平均错误率降低了 29.8%。
