Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.

中文翻译：

---

使用医疗物联网、智能传感器和 GRU 深度学习技术的养老环境辅助生活框架

由于全球老龄化人口的增加及其相关的与年龄相关的挑战，老年人可能会出现各种认知、身体和社会问题，例如步行速度减慢、行动不便、跌倒、疲劳、日常活动困难、记忆力-相关和社会孤立问题。反过来，需要对老年人进行持续的监督、干预、帮助和照顾，以实现积极和健康的老龄化。本研究提出了一种具有医疗物联网的环境辅助生活系统，该系统利用深度学习技术来监测和评估老年人的活动和生命体征，以支持临床决策。所提出的方法的新颖之处在于双向门控循环单元，门控循环单元深度学习技术和基于互信息的特征选择技术被应用于选择鲁棒特征以识别目标活动和异常。使用双向门控循环单元和门控循环单元深度学习技术在两个数据集（记录的环境辅助生活数据和 MHealth 基准数据）上进行了实验，并与其他最先进的技术进行了比较。使用不同的评估指标来评估性能，结果表明双向门控循环单元深度学习技术优于其他最先进的方法，环境辅助生活数据的准确率为 98.14%，使用所提出的方法的 MHealth 数据准确率为 99.26%。使用双向门控循环单元和门控循环单元深度学习技术在两个数据集（记录的环境辅助生活数据和 MHealth 基准数据）上进行了实验，并与其他最先进的技术进行了比较。使用不同的评估指标来评估性能，结果表明双向门控循环单元深度学习技术优于其他最先进的方法，环境辅助生活数据的准确率为 98.14%，使用所提出的方法的 MHealth 数据准确率为 99.26%。使用双向门控循环单元和门控循环单元深度学习技术在两个数据集（记录的环境辅助生活数据和 MHealth 基准数据）上进行了实验，并与其他最先进的技术进行了比较。使用不同的评估指标来评估性能，结果表明双向门控循环单元深度学习技术优于其他最先进的方法，环境辅助生活数据的准确率为 98.14%，使用所提出的方法的 MHealth 数据准确率为 99.26%。