Allahbakhshi Hoda, Röcke Christina, Weibel Robert
Department of Geography, University of Zurich (UZH), Zurich, Switzerland.
Digital Society Initiative, UZH, Zurich, Switzerland.
Front Physiol. 2021 Oct 22;12:738939. doi: 10.3389/fphys.2021.738939. eCollection 2021.
Increasing the amount of physical activity (PA) in older adults that have shifted to a sedentary lifestyle is a determining factor in decreasing health and social costs. It is, therefore, imperative to develop objective methods that accurately detect daily PA types and provide detailed PA guidance for healthy aging. Most of the existing techniques have been applied in the younger generation or validated in the laboratory. To what extent, these methods are transferable to real-life and older adults are a question that this paper aims to answer. Sixty-three participants, including 33 younger and 30 older healthy adults, participated in our study. Each participant wore five devices mounted on the left and right hips, right knee, chest, and left pocket and collected accelerometer and GPS data in both semi-structured and real-life environments. Using this dataset, we developed machine-learning models to detect PA types walking, non-level walking, jogging/running, sitting, standing, and lying. Besides, we examined the accuracy of the models within-and between-age groups applying different scenarios and validation approaches. The within-age models showed convincing classification results. The findings indicate that due to age-related behavioral differences, there are more confusion errors between walking, non-level walking, and running in older adults' results. Using semi-structured training data, the younger adults' models outperformed older adults' models. However, using real-life training data alone or in combination with semi-structured data generated better results for older adults who had high real-life data quality. Assessing the transferability of the models to older adults showed that the models trained with younger adults' data were only weakly transferable. However, training the models with a combined dataset of both age groups led to reliable transferability of results to the data of the older subgroup. We show that age-related behavioral differences can alter the PA classification performance. We demonstrate that PA type detection models that rely on combined datasets of young and older adults are strongly transferable to real-life and older adults' data. Our results yield significant time and cost savings for future PA studies by reducing the overall volume of training data required.
增加已转向久坐生活方式的老年人的身体活动（PA）量是降低健康和社会成本的一个决定性因素。因此，开发能够准确检测日常PA类型并为健康老龄化提供详细PA指导的客观方法势在必行。现有的大多数技术都应用于年轻一代或在实验室中得到验证。这些方法在多大程度上可转移到现实生活中以及老年人身上，是本文旨在回答的一个问题。63名参与者，包括33名年轻健康成年人和30名老年健康成年人，参与了我们的研究。每位参与者在左、右臀部、右膝、胸部和左口袋佩戴五个设备，并在半结构化和现实生活环境中收集加速度计和GPS数据。利用该数据集，我们开发了机器学习模型来检测PA类型，包括步行、非水平步行、慢跑/跑步、坐着、站着和躺着。此外，我们在不同场景和验证方法下，检验了模型在年龄组内和年龄组间的准确性。年龄组内模型显示出令人信服的分类结果。研究结果表明，由于与年龄相关的行为差异，老年人在步行、非水平步行和跑步结果之间存在更多混淆误差。使用半结构化训练数据时，年轻成年人的模型优于老年人的模型。然而，仅使用现实生活训练数据或与半结构化数据结合使用时，对于具有高现实生活数据质量的老年人产生了更好的结果。评估模型对老年人的可转移性表明，用年轻成年人数据训练的模型仅有较弱的可转移性。然而，用两个年龄组的组合数据集训练模型，可使结果可靠地转移到老年亚组的数据上。我们表明，与年龄相关的行为差异会改变PA分类性能。我们证明，依赖于年轻和老年成年人组合数据集的PA类型检测模型可强烈转移到现实生活和老年人的数据上。我们的结果通过减少所需训练数据的总体量，为未来的PA研究节省了大量时间和成本。
