Combining emerging patterns with random forest for complex activity recognition in smart homes

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

“…It can be seen that 32% of the scientific papers selected and summarized in Table S6, presented in the Supplementary Materials file, analyze smart buildings in general, while 53% target exclusively smart homes, 11% take into consideration smart office buildings, and the remaining 4% analyze smart spaces. The authors of these papers make use of different types of sensors, including wireless sensor networks [17,21,53,79]; sensors for detecting carbon dioxide concentration [1,17,50,53,68,78]; sensors for detecting total volatile organic compounds [68]; air temperature and humidity sensors [1,50,53,68,80]; pressure sensors [5,80]; wind speed sensors [50,80]; motion sensors [30,78,81]; Passive Infrared (PIR) sensors [30,82]; electricity meters [1,78,81]; smartphone sensors and Bluetooth beacon data [19]; indoor environment sensors [1]; occupancy information sensors [1]; sensors measuring the visibility outside the building [80]; sensors embedded in the environment [81]; wearable and environmental sensors [53,74]; binary infrared sensors [83]; unobtrusive sensing modules, including a gateway and a set of passive sensors [14]; simple non-intrusive sensors, door sensors and occupancy sensors …”

“…In these papers, the reasons for using the Decision Tree integrated with sensor devices in smart buildings were mainly related to human activity recognition [1,5,14,17,19,21,30,50,53,68,69,74,[78][79][80][81][82][83][84]. In some of these papers, human activity recognition was just a first step, subsequently focusing on: analyzing and improving the energy prediction performance [1,80]; analyzing and ensuring the thermal comfort of the occupants [50,53]; forecasting energy consumption [21]; estimating the number of occupants [78]; identifying behavioral patterns [79]; detecting deviating human behavior [82]; monitoring the activities of elderly people living alone [14]; classifying the gender of occupants [5]; and improving home-based assisted living [30].…”

“…With respect to the devised research methods, in [ The performance metrics chosen by the authors of the scientific papers that used the Decision Tree method integrated with sensor devices in smart buildings included Accuracy [1,5,17,19,50,53,68,74,80]; Confusion Matrix [19]; Precision [19,30,69,74,80]; Recall [19,69,74,80]; F1 Score [19,74]; Standard Deviation [1]; Root Mean Square Error (RMSE) [17,21,68,80]; Mean Percentage Error (MPE) and Mean Absolute Percentage Error (MAPE) [21]; Average Error of Occupancy Estimation [78]; Normalized Root-Mean-Square Error and Coefficient of Variation of the RMSD (CV) [17]; True Positive Rate and True Negative Rate [68,80]; Mean Absolute Error (MAE) [68,80,84]; Runtime [79]; the Receiver Operating Characteristic (ROC) curve [80]; the F-Measure [30,69,80,81]; Recognition Success Rate [83,84]; Sensitivity (SN), Specificity (SP) and Area Under the Receive...…”

“…Analyzing the papers selected and summarized in Table S13, it can be observed that 78% of them exclusively analyze smart homes, 16% take into consideration smart buildings in general, 3% analyze both smart homes and buildings, while the remaining 3% of the selected papers refer to smart workplace environments. The authors of these scientific articles make use of different types of sensors in their analyses, including wireless sensor networks [70,115,[119][120][121][122][123][124]; passive infrared motion sensors [82,97,117,118,122,125,126]; motion sensors [25,70,81,118,120,127,128]; environmental sensors [10,25,81,82,[116][117][118]123,[127][128][129][130][131][132]; temperature sensors [116,118,120,123,125,[131][132][133]; humidity sensors…”

“…The performance metrics that chosen by the authors of the scientific papers using the Hidden Markov Model integrated with sensor devices in smart buildings included: Accuracy [3,10,25,33,70,115,117,120,122,123,125,127,131,133,136,138]; Precision [25,118,128,133,135,137]; Recall [25,118,128,135]; F-Measure [25,81,121,130,133]; Sensitivity and Specificity [25,33,133]; F1 Score [116,133]; Confusion Matrix [116,127,129]; and Correctness [97,118]. In addition to the above-mentioned performance metrics, other methods that were used to assess the performance of the developed methods by the authors of the scientific papers selected and summarized in Table S13 included: a numerical case study highlighting the efficiency of the developed model [134]; thread latency [119]; evaluation of energy savings…”