Passive Radar for Opportunistic Monitoring in E-Health Applications

Abstract

This paper proposes a passive Doppler radar as a non-contact sensing method to capture human body movements, recognize respiration, and physical activities in e-Health applications. The system uses existing in-home wireless signal as the source to interpret human activity. This paper shows that passive radar is a novel solution for multiple healthcare applications which complements traditional smart home sensor systems. An innovative two-stage signal processing framework is outlined to enable the multi-purpose monitoring function. The first stage is to obtain premier Doppler information by using the high speed passive radar signal processing. The second stage is the functional signal processing including micro Doppler extraction for breathing detection and support vector machine classifier for physical activity recognition. The experimental results show that the proposed system provides adequate performance for both purposes, and prove that non-contact passive Doppler radar is a complementary technology to meet the challenges of future healthcare applications.

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

“…In their research, the authors of these papers implement various types of sensors, according to their purposes, namely: indoor sensors [1], occupancy information sensors [1], electricity meters [1,6,44], motion sensors [6,7,30,59,60], item kitchen sensors [6], door sensors [6,59,61,62], temperature sensors [1,2,6,59,63], photosensors [1,3,63], status of water and burner sensors [6,59], acceleration sensors [4,7], Kinect motion sensors [7], modern smartphone sensors [4,7,60], passive radar-based sensors [8], unobtrusive sensors [9,14], infrared sensors [15,30], wireless sensor networks [61,62], accelerometers [5,63], altimeters [63], gyroscopes [63], barometers [63], heart rate monitor [63], embedded sensors [4,…”

“…With respect to the reasons for using the SVM method with sensor equipment in smart buildings, it can be observed that the recognition of human activity is at the forefront, as this is addressed in most of the papers [3,4,6,[8][9][10]14,15,[29][30][31][32]59,60,62,63]. Assisted living was a strong motivation for using the SVM method with sensor devices in the smart buildings sector; seven of the identified papers focusing on the recognition of human activity did so in order to provide appropriate assisted living [6,14,15,[30][31][32]63], while other papers aimed to achieve assisted living by focusing on human fall detection [7], human behavior recognition [2], assessment of occupancy status information, and identification of human behavior [61].…”

“…In [6], the Support Vector Machine approach was implemented with a polynomial kernel of degree 3 (P-SVM), and afterwards, a comparison was conducted with other four classifiers: Radial Basis Function kernel-Support Vector Machine (RBF-SVM), Naïve Bayes, logistic recognition, and Recurrent Neural Network (RNN). The authors of [7,8,32,52,59,60] developed their research based solely on the Support Vector Machine technique. In [2], the Support Vector Regression (SVR) and Recurrent Neural Network (RNN) approaches were used.…”

“…The performance metrics considered in the scientific papers that used Support Vector Machines integrated with sensor devices in smart buildings include: Accuracy [1,3,5,[7][8][9][10]29,31,59,61,62,64]; Standard Deviation [1,63]; True Positive Rate [6,7,59]; False Positive Rate [6,7,59]; Precision [6,29,30,59,61,62]; Recall [6,29,59,61,62]; F-measure [6,29,30,59,61,62]; True Negative rate [7,59]; False Negative Rate [7,59]; Sensitivity [7,14,30]; Specificity [7,14,30,59];...…”

“…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]…”