A single feature for human activity recognition using two-dimensional acoustic array

Abstract

Human activity recognition is widely used in many fields, such as the monitoring of smart homes, fire detecting and rescuing, hospital patient management, etc. Acoustic waves are an effective method for human activity recognition. In traditional ways, one or a few ultrasonic sensors are used to receive signals, which require many feature quantities of extraction from the received data to improve recognition accuracy. In this study, we propose an approach for human activity recognition based on a two-dimensional acoustic array and convolutional neural networks. A single feature quantity is utilized to characterize the sound of human activities and identify those activities. The results show that the total accuracy of the activities is 97.5% for time-domain data and 100% for frequency-domain data. The influence of the array size on recognition accuracy is discussed, and the accuracy of the proposed approach is compared with traditional recognition approaches such as k-nearest neighbor and support vector machines where it outperformed them.

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

“…The authors of these scientific articles make use of different types of sensors in their analyses. These include wearable sensors [18,74]; environmental sensors [73,74]; motion sensors [18,75]; a two-dimensional acoustic array [27]; a Wireless Sensor Network (WSN) [23] and sensor networks [76]; temperature sensors [1,63,73,77]; photosensors [1,63]; Passive Infra-Red Sensors (PIR) [73,75]; sensors for humidity and for evaluating the carbon dioxide concentration [1,77]; microphones [77]; cameras [18]; occupancy information sensors [1]; electricity meters [1,75]; accelerometers [5,63]; sensors of IoT devices [38]; an altimeter, a gyroscope and a barometer [63]; sensors mounted on different objects [75]; an unobtrusive sensing module [14]; and binary and ubiquitous sensors [29].…”

“…With respect to the reasons for implementing Neural Networks for classification integrated with sensor devices in smart buildings, these are mainly related to the recognition/classification of human activity in the papers [1,5,14,18,23,27,29,63,[73][74][75][76][77]. In some of these papers, human activity recognition has as a final purpose the detection and prediction of abnormal behavior [75], monitoring the activities of elderly who are living alone [14,63], classification of the gender of occupants in a building [5], and monitoring the activities of elderly who are living in smart homes care [18,77].…”

“…With respect to the devised methods, in the paper [18], the authors made use of a hybrid approach, combining Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) methods. In [27], the authors implemented Convolutional Neural Networks, comparing them with traditional recognition approaches such as K-Nearest Neighbor and Support Vector Machines. In [23], the authors used the Multilayer Perceptron (MLP) method and compared it with Linear Regression (LR), Support Vector Machine (SVM), Gradient Boosting Machine (GBM) and Random Forest (RF).…”

“…The performance metrics considered in the scientific papers that use Neural Networks for classification purposes integrated with sensor devices in smart buildings include: Confusion Matrix [18,38,73]; F1 Score [18,73,74,76]; Accuracy [1,5,18,27,29,38,73,74,76]; Root Mean Square Error (RMSE) [23,75,77]; Precision [29,38,73,74,76]; Recall [29,38,73,74,76]; Standard Deviation (STD) [1,63]; Mean Absolute Percentage Error (MAPE) [23,77]; Mean Squared Error (MSE) [77]; Coefficient of Determination (R 2 ) and Mean Absolute Error (MAE) [23]; Specificity [14,73]; Sensitivity (SN), Area Under the Receiver Operating Characteristic Curve (AUC) [14]; and Maximum, Minimum, Median, Mode, Kurtosis, Skewness, Intensity, Difference, Root-Mean-Square (RMS), Energy, Entropy and Key Coefficient [63].…”

“…There is an increasing interest in the scientific literature in studies related to these topics; for example, there have been papers focusing on smart buildings [1][2][3][4][5], smart homes [6][7][8][9][10], smart hospitals [11], smart commercial buildings [12,13], sensor devices [9,[14][15][16][17], supervised machine learning models for classification purposes [1,11,[16][17][18] or for regression purposes [19][20][21][22][23], unsupervised machine learning models for clustering purposes [24][25][26], deep learning techniques [18,27,28], human activity recognition and classification with a view to assisted living [15,[29][30][31][32][33][34][35], Internet of Things (IoT) [21,[36][37][38]…”