Design of a New Method for Detection of Occupancy in the Smart Home Using an FBG Sensor

Abstract

This article introduces a new way of using a fibre Bragg grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a smart home (SH). CO2 sensors are used to determine the CO2 concentration of the monitored rooms in an SH. CO2 sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO2 prediction, by means of an artificial neural network (ANN) with a scaled conjugate gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO2 concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO2 prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO2 prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the adaptive filtration with an LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.

Novel Framework for Multi-Scale Occupancy Sensing for distributed monitoring in Internet-of-Things

“…Discussion of various indoor occupancy sensing was also reported in work of Mena et al [28]. The study carried out by Vanus et al [29] have used Fibre Bragg Grating sensor for occupancy sensing in smart home environment. Further the study has used Arti cial Neural Network for predicting CO 2 concentration.…”

Using Interoperability between Mobile Robot and KNX Technology for Occupancy Monitoring in Smart Home Care

“…In our studies, in which a detailed description is provided, we dealt with the various optimization steps that could be undertaken to enhance the predictive accuracy of designed models [60][61][62][63][64]. The results of the proposed method are comparable with the state-of-the-art approaches, as summarized in Table 4.…”

“…We used nonlinear autoregressive Neural Networks (NN) with NAR and NARX prediction models to predict the CO 2 concentration waveform to transmit the information from KNX technology to mobile robots for monitoring and determining the occupancy of people in SHC with better than 98% accuracy. Enhanced predictive accuracy is possible using the optimization of algorithms [55][56][57][58][59][60][61][62] or filtration of predicted waveforms with adaptive algorithms [61,63] or with wavelet transformation [64,65]. In this article, we focused on NAR and NARX prediction models.…”

A Multi-Resident Number Estimation Method for Smart Homes

“…Wang et al [46] propose a method to count people by utilizing breathing traces, reaching 86% accuracy for four people. Similarly, in [47], Fiber Bragg Grating sensors are used for the detection and number of occupants, experimented with three people. Recent techniques also include voice recording to recognize up to three persons [48].…”