SURAKSHA E-Caretaker:使用机器学习的老年人跌倒检测和警报系统

Lahiru Mendis, Sasini Hathurusinghe, H. Epa, Thisara Edirisinghe, J. Wickramarathne, Shalini Rupasinghe
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引用次数: 0

摘要

随着年龄的增长,人们变得无法完成年轻时完成的任务。跌倒是老年人生活中的一个主要问题,因为跌倒的影响依赖于老年人的身心生活质量。本研究提出了一种基于机器学习概念的老年人跌倒检测和报警系统e-Caretaker SURAKSHA。这一领域的研究人员已经提出了不同的解决方案,只检测跌倒,而不是自动检测并通知看护人。该解决方案作为一种智能可穿戴设备,能够自动监控实时姿势,检测突然摔倒,摔倒者可能的心脏心律失常情况,以及每天的路线偏离以及摔倒的位置,最终通过移动应用程序通知看护人员。根据已经进行的研究,使用python模型开发,通过机器学习的概念,参考Markov模型、Prophet模型和Naïve Bayes算法来实现系统。该解决方案提供了本研究的结果,准确度约为89.9%,从而导致该领域的成功产品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SURAKSHA E-Caretaker: Elders Falling Detection and Alerting System using Machine Learning
People become unable to perform tasks that were done at the younger ages as they were when the ages pass with time. Falls play a major issue in the lives of elderly people as the physical and mental quality of life is dependable on the effects of falls. This research presents an e-Caretaker SURAKSHA which is an elder falling detection and alerting system based on Machine Learning concepts. Researchers that have been done in this area have produced different solutions to detect only the falls but not to automatically detect and notify them to the caretakers. This solution serves as a smart wearable device that is capable of automatically monitoring real-time postures, detecting sudden falls, possible arrhythmia conditions of the heart of the fallen person, and daily route deviations along with the fallen location which is finally notified to the caretakers through a mobile application. According to the performed studies, python model development was used to implement the system through Machine Learning concepts by referring to the Markov model, Prophet model, and Naïve Bayes algorithms. This solution provides the results of this research with an accuracy of around 89.9% leading to a successful product in the domain.
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