An Indoor Localization System for Telehomecare Applications

Abstract

In this paper, we present a novel probabilistic technique, based on the Bayes filter, able to estimate the user location, even with unreliable sensor data coming only from fixed sensors in the monitored environment. Our approach has been extensively tested in a home-like environment, as well as in a real home, and achieves very good results. We present results on two datasets, representative of real life conditions, collected during the testing phase. We detect the patient location with subroom accuracy, an improvement over the state of the art for localization using only environmental sensors. The main drawback is that it is only suitable for applications where a single person is present in the environment, like as with other approaches that do not use any mobile device. For this reason, we introduced the "telehomecare" term, therefore differentiating from generic telemedicine applications, where many people can be in the same environment at the same time.

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

“…Analyzing the papers in Table S3, it can be observed that, according to the authors of these papers, all of the studies focused on smart homes. The authors of these scientific articles made use in their analyses of different types of sensors, including: biomedical sensors [11]; ambient data sensors [11,34,68]; acoustic sensor networks [67]; WiFi-enabled sensors [36]; Passive Infrared (PIR) sensors [30,34]; binary sensors [31,69]; and motion sensors [30,70].…”

“…With respect to the reasons for using the Naïve Bayes method with sensor equipment in smart buildings, one can observe that the recognition of human activity was the main subject of the identified papers summarized in Table S3, being addressed in papers [11,30,31,34,[68][69][70]. Meanwhile, several of the above-mentioned scientific papers that use the Naïve Bayes integrated with sensor devices in Smart Buildings also addressed issues regarding assisted living [11,30,31,34,36]. Other reasons for applying the Naïve Bayes method with sensors in smart buildings include obtaining accurate information regarding the positions of surrounding objects, an aspect especially useful for autonomous systems and smart devices [67] or in developing an Internet of Things (IoT)-based fully automated nutrition monitoring system [36].…”

“…In [68], a comparison of the supervised learning models was presented: Naïve Bayes (NB), C4.5 Decision Tree, Logistic Regression, K-Nearest Neighbor, and Random Forest were used in order to detect and estimate occupancy in smart homes. In [36], the authors developed a hybrid approach by combining Bayesian algorithms and a 5-layer Perceptron Neural Network method for diet monitoring purposes; the authors of [34] used the Bayes filter algorithm to locate people. In [30], the authors made use of learning classification algorithms, including Naïve Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF).…”

“…The performance metrics that were chosen by the authors of the scientific papers that use the Naïve Bayes method integrated with sensor devices in smart buildings include: Accuracy [11,31,34,36,68,70]; Precision [11,30,69]; Recall [11,69]; F-measure [11,30,69]; Mean Value and Standard Deviation [67]; Accuracy, True Positive Rate, True Negative Rate with a view to assessing the performance in detecting the occupancy, along with the Mean Absolute Error and the Root Mean Square Error, for establishing the number of occupants [68]; and Error Rate [34].…”

“…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]…”