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aily Human Activity Recognition Using Non-Intrusive Sensors
Sensors ( IF 3.4 ) Pub Date : 2021-08-04 , DOI: 10.3390/s21165270
Raúl Gómez Ramos 1, 2 , Jaime Duque Domingo 1 , Eduardo Zalama 1, 2 , Jaime Gómez-García-Bermejo 1, 2
Affiliation  

In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.

中文翻译:

使用非侵入式传感器的日常人类活动识别

近年来,人工智能技术 (AIT) 已得到发展,以提高老年人的生活质量和居家安全。这项工作的重点是开发一个系统,该系统能够实时识别老年人日常生活中最常见的活动,使专家能够监控此人的习惯,例如服药或吃正确的一日三餐. 为此,基于循环神经网络,特别是双向 LSTM 网络,开发了一种预测模型,以根据一组不同的数据提供的信息实时获取个人在家中进行的活动。安装在每个人家中的传感器。本文开发的预测模型提供了 95.42% 的准确率,改进当前使用的类似模型的结果。为了得到准确率高的可靠模型,对数据进行了一系列的处理和过滤过程,如基于滑动窗口的方法或堆叠重排序算法等,后续使用训练从公共数据库 CASAS 获得的神经网络。
更新日期:2021-08-04
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