手机和可穿戴设备等设备的广泛使用允许自动监控人类日常活动,生成大量数据集,提供对人类长期行为的洞察。结构化和受控的数据收集过程对于释放这些信息的全部潜力至关重要。虽然用于身体活动监测的可穿戴传感器在医疗保健领域获得了巨大的吸引力,体育科学,和健身应用,为研究和算法开发确保多样化和全面的数据集是一个显著的挑战。在这个概念验证研究中,我们强调了语义表示在增强数据互操作性和促进身体活动传感器观测的高级分析方面的重要性。我们的方法侧重于通过采用医疗级(CE认证)传感器来生成合成数据集,从而增强身体活动数据集的可用性。此外,我们提供与合成数据集相关的伦理考虑因素的见解。该研究对真实和合成活性数据集进行了比较分析,评估其在减轻模型偏差和促进预测分析公平性方面的有效性。我们已经创建了一个本体,用于在语义上表示来自物理活动传感器的观察结果,并对使用MOX2-5活动传感器收集的数据进行了预测分析。直到现在,一直以来,缺乏使用MOX2-5活动监测医疗级(CE认证)设备收集的身体活动公开数据集.MOX2-5捕获和传输高分辨率数据,包括活动强度,承重,久坐,站立,低,中度,和剧烈的体力活动,以及每分钟的步数。我们的数据集包括在30-45天(约1.5个月)的时间内从16名成年人(男性:12;女性:4)收集的身体活动数据。产生相对较小的539条记录。为了解决这个限制,我们采用各种合成数据生成方法,如高斯Capula(GC),条件表格一般对抗网络(CTGAN),和表格一般对抗网络(TABGAN),用合成数据扩充数据集。对于真实的和合成的数据集,我们开发了多层感知器(MLP)分类模型,用于对日常身体活动水平进行准确分类。研究结果强调了语义本体在语义搜索中的有效性,知识表示,数据集成,推理,并捕获数据之间有意义的关系。该分析支持以下假设:预测模型的效率随着额外合成训练数据量的增加而提高。本体和生成AI具有加速行为监测研究进步的潜力。所提供的数据,包括真正的MOX2-5及其合成对应物,作为开发活动类型分类中的健壮方法的宝贵资源。此外,它为探索与合成数据相关的研究方向开辟了道路,包括模型效率,检测生成的数据,以及有关数据隐私的考虑。
The widespread use of devices like mobile phones and wearables allows for automatic monitoring of human daily activities, generating vast datasets that offer insights into long-term human behavior. A structured and controlled data collection process is essential to unlock the full potential of this information. While wearable sensors for physical activity monitoring have gained significant traction in healthcare, sports science, and fitness applications, securing diverse and comprehensive datasets for research and algorithm development poses a notable challenge. In this proof-of-concept
study, we underscore the significance of semantic representation in enhancing data interoperability and facilitating advanced analytics for physical activity sensor observations. Our approach focuses on enhancing the usability of physical activity datasets by employing a medical-grade (CE certified) sensor to generate synthetic datasets. Additionally, we provide insights into ethical considerations related to synthetic datasets. The
study conducts a comparative analysis between real and synthetic activity datasets, assessing their effectiveness in mitigating model bias and promoting fairness in predictive analysis. We have created an ontology for semantically representing observations from physical activity sensors and conducted predictive analysis on data collected using MOX2-5 activity sensors. Until now, there has been a lack of publicly available datasets for physical activity collected with MOX2-5 activity monitoring medical grade (CE certified) device. The MOX2-5 captures and transmits high-resolution data, including activity intensity, weight-bearing, sedentary, standing, low, moderate, and vigorous physical activity, as well as steps per minute. Our dataset consists of physical activity data collected from 16 adults (Male: 12; Female: 4) over a period of 30-45 days (approximately 1.5 months), yielding a relatively small volume of 539 records. To address this limitation, we employ various synthetic data generation methods, such as Gaussian Capula (GC), Conditional Tabular General Adversarial Network (CTGAN), and Tabular General Adversarial Network (TABGAN), to augment the dataset with synthetic data. For both the authentic and synthetic datasets, we have developed a Multilayer Perceptron (MLP) classification model for accurately classifying daily physical activity levels. The findings underscore the effectiveness of semantic ontology in semantic search, knowledge representation, data integration, reasoning, and capturing meaningful relationships between data. The analysis supports the hypothesis that the efficiency of predictive models improves as the volume of additional synthetic training data increases. Ontology and Generative AI hold the potential to expedite advancements in behavioral monitoring research. The data presented, encompassing both real MOX2-5 and its synthetic counterpart, serves as a valuable resource for developing robust methods in activity type classification. Furthermore, it opens avenues for exploration into research directions related to synthetic data, including model efficiency, detection of generated data, and considerations regarding data privacy.