Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Mobile Technology Group, Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States.
Front Public Health. 2023 Feb 28;11:1086671. doi: 10.3389/fpubh.2023.1086671. eCollection 2023.
The emerging field of digital phenotyping leverages the numerous sensors embedded in a smartphone to better understand its user's current psychological state and behavior, enabling improved health support systems for patients. As part of this work, a common task is to use the smartphone accelerometer to automatically recognize or classify the behavior of the user, known as human activity recognition (HAR). In this article, we present a deep learning method using the Resnet architecture to implement HAR using the popular UniMiB-SHAR public dataset, containing 11,771 measurement segments from 30 users ranging in age between 18 and 60 years. We present a unified deep learning approach based on a Resnet architecture that consistently exceeds the state-of-the-art accuracy and F1-score across all classification tasks and evaluation methods mentioned in the literature. The most notable increase we disclose regards the leave-one-subject-out evaluation, known as the most rigorous evaluation method, where we push the state-of-the-art accuracy from 78.24 to 80.09% and the F1-score from 78.40 to 79.36%. For such results, we resorted to deep learning techniques, such as hyper-parameter tuning, label smoothing, and dropout, which helped regularize the Resnet training and reduced overfitting. We discuss how our approach could easily be adapted to perform HAR in real-time and discuss future research directions.
数字表型学是一个新兴领域，它利用智能手机中嵌入的众多传感器来更好地了解其用户当前的心理状态和行为，从而为患者提供更好的健康支持系统。在这项工作中，一个常见的任务是使用智能手机加速度计自动识别或分类用户的行为，即人类活动识别（HAR）。在本文中，我们提出了一种使用 Resnet 架构的深度学习方法，使用流行的 UniMiB-SHAR 公共数据集来实现 HAR，该数据集包含 30 名年龄在 18 至 60 岁之间的用户的 11771 个测量片段。我们提出了一种基于 Resnet 架构的统一深度学习方法，该方法在文献中提到的所有分类任务和评估方法中都始终超过最先进的准确性和 F1 分数。我们披露的最显著的改进是关于留一受试者外评估，这是最严格的评估方法，我们将最先进的准确性从 78.24%提高到 80.09%，F1 分数从 78.40%提高到 79.36%。为了实现这些结果，我们采用了深度学习技术，如超参数调整、标签平滑和辍学，这有助于正则化 Resnet 训练并减少过拟合。我们讨论了我们的方法如何轻松适应实时进行 HAR，并讨论了未来的研究方向。
