Department of Electrical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan.
Estarta Co., Ltd., Amman 11942, Jordan.
Sensors (Basel). 2022 Jan 28;22(3):1016. doi: 10.3390/s22031016.
Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.
人体活动识别 (HAR) 系统旨在读取传感器数据并对其进行分析，以对任何检测到的运动进行分类并做出相应响应。然而，我们需要更具响应性和接近实时的系统来区分虚假和真实警报。为了准确确定警报触发，需要在一定时间段内存储合法用户的运动模式，并使用这些模式来训练系统识别与其运动相关的特征。然后，该训练过程会进行测试周期，使用与训练数据集相似或不同的不同活动模式的实际数据。本文评估了组合卷积神经网络 (CNN) 和朴素贝叶斯的使用，以准确性和稳健性来正确识别例如蜂鸣器声音等真实警报触发。结果表明，即使从完整运动路径中提取出部分运动模式作为子集，也可以使用这两种方法之一实现模式识别。
