Sultana Arifa, Deb Kaushik, Dhar Pranab Kumar, Koshiba Takeshi
Department of Computer Science and Engineering, Chittagong University of Engineering & Technology (CUET), Chattogram 4349, Bangladesh.
Faculty of Education and Integrated Arts and Sciences, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo 169-8050, Japan.
Entropy (Basel). 2021 Mar 10;23(3):328. doi: 10.3390/e23030328.
Human fall identification can play a significant role in generating sensor based alarm systems, assisting physical therapists not only to reduce after fall effects but also to save human lives. Usually, elderly people suffer from various kinds of diseases and fall action is a very frequently occurring circumstance at this time for them. In this regard, this paper represents an architecture to classify fall events from others indoor natural activities of human beings. Video frame generator is applied to extract frame from video clips. Initially, a two dimensional convolutional neural network (2DCNN) model is proposed to extract features from video frames. Afterward, gated recurrent unit (GRU) network finds the temporal dependency of human movement. Binary cross-entropy loss function is calculated to update the attributes of the network like weights, learning rate to minimize the losses. Finally, sigmoid classifier is used for binary classification to detect human fall events. Experimental result shows that the proposed model obtains an accuracy of 99%, which outperforms other state-of-the-art models.
人体跌倒识别在基于传感器的报警系统中发挥着重要作用，不仅能协助物理治疗师减轻跌倒后的影响，还能挽救生命。通常，老年人患有各种疾病，此时跌倒行为对他们来说是非常常见的情况。在这方面，本文提出了一种架构，用于将跌倒事件与人类其他室内自然活动区分开来。视频帧生成器用于从视频片段中提取帧。首先，提出了一种二维卷积神经网络（2DCNN）模型来从视频帧中提取特征。之后，门控循环单元（GRU）网络找出人体运动的时间依赖性。计算二元交叉熵损失函数以更新网络的属性，如权重、学习率，以最小化损失。最后，使用 sigmoid 分类器进行二元分类以检测人体跌倒事件。实验结果表明，所提出的模型获得了 99%的准确率，优于其他现有最先进的模型。
