Monitoring of the daily living activities in smart home care

Abstract

One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO 2 , humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO 2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg-Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO 2 concentrations from obtained measurements of temperature (indoor, outdoor) T i , T o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO 2 , T and rH with the aim to indirectly predict CO 2 leading to the elimination of CO 2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015ANN (1.6. -30.6.2015. For learned ANN (1.2.2014ANN (1.2. -27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO 2 for ADL monitoring in SHC.

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]. In addition to these purposes, in other papers, the authors target the study of energy consumption forecasting [1,23] or achieving advanced connectivity between devices, systems, and services that continuously record enormous amounts of data from the sensors of IoT devices [38].…”

Design and application of a KNX‐based home automation simulator for smart home system education

“…Among automation systems, in recent years, KNX‐based systems have come to the forefront with many features such as simple installation, economy, and easy programming. At home applications, using KNX‐based systems, designers and constructers offer effective solutions for lighting, motion sensitivity, heating, and blind motion control , more comfort, convenience, security, and low energy demand for their clients . Thanks to these advantages, KNX‐based systems has over 70% share of the automation market of Europe .…”

Recent advances of the signal processing techniques in future smart grids

“…In references [69,70], the authors presented the concept of a modern smart home and the inclusion of renewable energy with the smart grid scenario to reduce the electric bills accordingly. A global overview of the applications of signal processing techniques in smart grids is given in Table 1.…”