Department of Electrical Engineering, National Taipei University of Technology, Taipei 10617, Taiwan.
Department of Electronics, School of Information and Communication Technology, Mongolian University of Science and Technology, Ulaanbaatar 13341, Mongolia.
Sensors (Basel). 2021 Aug 9;21(16):5371. doi: 10.3390/s21165371.
The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly. One way to detect the abnormal behavior of elderly residents under the condition of maintaining privacy is to equip the resident's house with an Internet of Things system based on a non-invasive binary motion sensor array. We propose to concatenate external features ( and ) along with extracted features with a bi-directional long short-term memory (Bi-LSTM) neural network to recognize the activities of daily living with a higher accuracy. The concatenated features are classified by a fully connected neural network (FCNN). The proposed model was evaluated on open dataset from the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University. The experimental results show that the proposed method outperformed state-of-the-art models with a margin of more than 6.25% of the score on the same dataset.
近年来，人口老龄化的增长导致人们对家庭监护的需求不断增加，因为独居生活越来越流行。这种监护可以保护独自生活的老年人免受摔倒或某些疾病恶化等意外事件的影响。然而，尽管可穿戴设备和基于摄像头的系统可以提供关于人体运动的相对精确信息，但它们侵犯了老年人的隐私。在不侵犯隐私的情况下检测老年居民异常行为的一种方法是在居民的家中配备基于非侵入式二进制运动传感器阵列的物联网系统。我们建议将外部特征（和）与提取的特征与双向长短时记忆（Bi-LSTM）神经网络串联起来，以更高的精度识别日常生活活动。串联的特征由全连接神经网络（FCNN）分类。该模型在华盛顿州立大学先进自适应系统研究中心（CASAS）的公开数据集上进行了评估。实验结果表明，该方法在相同数据集上的得分超过了最先进模型 6.25%以上。
