使用批学习和流学习算法的日常生活活动自动检测和分类的比较评价。

IF 3 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Paula Sofía Muñoz, Ana Sofía Orozco, Jaime Pabón, Daniel Gómez, Ricardo Salazar-Cabrera, Jesús D Cerón, Diego M López, Bernd Blobel
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引用次数: 0

摘要

背景/目的:日常生活活动(adl)对于评估个人的自主性至关重要,包括进食、穿衣和四处走动等任务。预测这些活动是健康监测、老年护理和智能系统、改善生活质量和促进早期依赖检测的一部分,所有这些都是个性化健康和社会护理的相关组成部分。然而,由于人类行为的高度可变性、传感器噪声和数据采集协议的差异,从传感器数据中自动分类adl仍然具有挑战性。这些挑战限制了现有解决方案的准确性和适用性。本研究详细介绍了基于批处理学习(BL)和流学习(SL)算法的实时ADL分类模型的建模和评估。方法:采用跨行业数据挖掘标准流程(CRISP-DM)。模型使用一个综合数据集进行训练,该数据集包含23个以adl为中心的数据集,使用加速度计和陀螺仪数据。采用归一化和采样率统一技术对数据进行预处理,最终选择相应的传感器在人体上的位置。结果:在清理和调试之后,生成了最终的数据集,其中包含238,990个样本、56个活动和52列。该研究比较了用BL和SL算法训练的模型,通过准确率、曲线下面积(AUC)和f1评分指标来评估它们在不同分类场景下的性能。最后,开发了一个移动应用程序来实时分类adl(从数据集馈送数据)。结论:本研究的结果可用于与ADL和人类活动识别(HAR)相关的各种数据科学项目,并且由于集成了各种数据源,它可能有助于解决机器学习模型中的偏见和提高泛化性。在线学习算法的主要优势是动态适应数据变化,这代表了个人自主和医疗保健监测方面的重大进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Evaluation of Automatic Detection and Classification of Daily Living Activities Using Batch Learning and Stream Learning Algorithms.

Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing an individual's autonomy, encompassing tasks such as eating, dressing, and moving around, among others. Predicting these activities is part of health monitoring, elderly care, and intelligent systems, improving quality of life, and facilitating early dependency detection, all of which are relevant components of personalized health and social care. However, the automatic classification of ADLs from sensor data remains challenging due to high variability in human behavior, sensor noise, and discrepancies in data acquisition protocols. These challenges limit the accuracy and applicability of existing solutions. This study details the modeling and evaluation of real-time ADL classification models based on batch learning (BL) and stream learning (SL) algorithms. Methods: The methodology followed is the Cross-Industry Standard Process for Data Mining (CRISP-DM). The models were trained with a comprehensive dataset integrating 23 ADL-centric datasets using accelerometers and gyroscopes data. The data were preprocessed by applying normalization and sampling rate unification techniques, and finally, relevant sensor locations on the body were selected. Results: After cleaning and debugging, a final dataset was generated, containing 238,990 samples, 56 activities, and 52 columns. The study compared models trained with BL and SL algorithms, evaluating their performance under various classification scenarios using accuracy, area under the curve (AUC), and F1-score metrics. Finally, a mobile application was developed to classify ADLs in real time (feeding data from a dataset). Conclusions: The outcome of this study can be used in various data science projects related to ADL and Human activity recognition (HAR), and due to the integration of diverse data sources, it is potentially useful to address bias and improve generalizability in Machine Learning models. The principal advantage of online learning algorithms is dynamically adapting to data changes, representing a significant advance in personal autonomy and health care monitoring.

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来源期刊
Journal of Personalized Medicine
Journal of Personalized Medicine Medicine-Medicine (miscellaneous)
CiteScore
4.10
自引率
0.00%
发文量
1878
审稿时长
11 weeks
期刊介绍: Journal of Personalized Medicine (JPM; ISSN 2075-4426) is an international, open access journal aimed at bringing all aspects of personalized medicine to one platform. JPM publishes cutting edge, innovative preclinical and translational scientific research and technologies related to personalized medicine (e.g., pharmacogenomics/proteomics, systems biology). JPM recognizes that personalized medicine—the assessment of genetic, environmental and host factors that cause variability of individuals—is a challenging, transdisciplinary topic that requires discussions from a range of experts. For a comprehensive perspective of personalized medicine, JPM aims to integrate expertise from the molecular and translational sciences, therapeutics and diagnostics, as well as discussions of regulatory, social, ethical and policy aspects. We provide a forum to bring together academic and clinical researchers, biotechnology, diagnostic and pharmaceutical companies, health professionals, regulatory and ethical experts, and government and regulatory authorities.
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