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：

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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。