Novelty detection algorithms to help identify abnormal activities in the daily lives of elderly people

IF 1.3 4区工程技术 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Latin America Transactions Pub Date : 2024-02-09 DOI:10.1109/TLA.2024.10431423

Anita Fernandes;Valderi Leithardt;Juan Francisco Santana

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Abstract

The populations life expectancy is increasing, and this scenario will bring challenges to be faced in the coming decades to provide healthy and inclusive aging. At this stage of life, several common health conditions, chronic illnesses, and disabilities affect the individuals physical and mental health and prevent him from carrying out Activities of Daily Living. In this context, this article presents a comparative study between some Machine Learning algorithms used to identify behavioral abnormalities based on ADL (Activities of Daily Living), through the Novelty Detection technique. ADL data were used to create a model that defines the baseline behavior of an elderly person, and new observations, to verify significant changes in behavior, are classified as outliers or abnormal. The Local Outlier Factor, One-class Support Vector Machine, Robust Covariance, and Isolation Forest algorithms were analyzed, and the Local Outlier Factor obtained the best result, reaching a precision and F1-Score of 96%.

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新奇事物检测算法帮助识别老年人日常生活中的异常活动

人口的预期寿命在不断延长，这种情况将给未来几十年提供健康和包容的老龄化带来挑战。在人生的这一阶段，一些常见的健康状况、慢性疾病和残疾会影响个人的身心健康，使其无法进行日常生活活动。在此背景下，本文通过新颖性检测技术，对一些用于识别基于 ADL（日常生活活动）的行为异常的机器学习算法进行了比较研究。ADL 数据被用来创建一个定义老年人基线行为的模型，而新的观察结果则被归类为异常值或异常行为，以验证行为的显著变化。对本地离群因子、单类支持向量机、鲁棒性协方差和隔离森林算法进行了分析，本地离群因子取得了最佳结果，精确度和 F1 分数均达到 96%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Latin America Transactions COMPUTER SCIENCE, INFORMATION SYSTEMS-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

3.50

自引率

7.70%

发文量

192

审稿时长

3-8 weeks

期刊介绍： IEEE Latin America Transactions (IEEE LATAM) is an interdisciplinary journal focused on the dissemination of original and quality research papers / review articles in Spanish and Portuguese of emerging topics in three main areas: Computing, Electric Energy and Electronics. Some of the sub-areas of the journal are, but not limited to: Automatic control, communications, instrumentation, artificial intelligence, power and industrial electronics, fault diagnosis and detection, transportation electrification, internet of things, electrical machines, circuits and systems, biomedicine and biomedical / haptic applications, secure communications, robotics, sensors and actuators, computer networks, smart grids, among others.