Methods of human activity classification in buildings

The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and detection of falling events of people. Two different approaches are proposed to integrate human activity recognition within smart homes. The first approach utilizes KNX standard-based devices to obtain room air quality data (humidity, CO2, temperature) and combine the obtained data with two wearable devices that provide movement-related data. The second approach simplifies, improves, and addresses a few of the shortcomings of the first approach, it utilizes different measuring devices with higher sampling rates. It examines multiple statistical methods and ultimately chooses a simpler multi-layer perceptron neural network model. Resulting in a less computationally intensive solution with higher accuracy levels. The study achieved cross-validation accuracy levels above 98 %.Chytrých domácností rychle přibývá. Inteligentní domy obvykle obsahují funkce, jako jsou hlasově aktivované funkce, automatizace, monitorování a sledování událostí. Kromě komfortu a pohodlí může integrace funkcí chytré domácnosti s metodami zpracování dat poskytnout cenné informace o pohodě rezidence chytré domácnosti. Tato studie je zaměřena na analýzu dat v inteligentních domácnostech nad rámec monitorování obsazenosti a detekce pádu osob. Jsou navrženy dva různé přístupy k integraci rozpoznávání lidské činnosti do inteligentních domácností. První přístup využívá zařízení založená na standardu KNX k získávání dat o kvalitě vzduchu v místnosti (vlhkost, CO2, teplota) a kombinování získaných dat se dvěma nositelnými zařízeními, které poskytují údaje související s pohybem. Druhý přístup zjednodušuje, zlepšuje a řeší několik nedostatků prvního přístupu, využívá různá měřicí zařízení s vyšší vzorkovací frekvencí. Zkoumá více statistických metod a nakonec volí jednodušší vícevrstvý model perceptronové neuronové sítě. Výsledkem je méně výpočetně náročné řešení s vyšší úrovní přesnosti. Studie dosáhla úrovně přesnosti křížové validace nad 98 %.450 - Katedra kybernetiky a biomedicínského inženýrstvívyhově

Novel proposal for prediction of CO2 course and occupancy recognition in Intelligent Buildings within IoT

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.Web of Science1223art. no. 454

Wavelet-based filtration procedure for denoising the predicted CO2 waveforms in smart home within the Internet of Things

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.Web of Science203art. no. 62

Application of a new CO2 prediction method within family house occupancy monitoring

The article describes the application of Python for verification of a newly designed method of CO2 prediction from measurements of indoor parameters of temperature and relative humidity within occupancy monitoring in real conditions of a family home. The article describes the implementation of non-electric quantities (indoor CO2 concentration, indoor temperature, indoor relative humidity) measurement in five rooms of a family home (living room, kitchen, children's room, bathroom, bedroom) using Loxone technology sensors. The IBM IoT (Internet Of Things) was used for storing and subsequent processing of the measured values within the time interval of December 22, 2018, to December 31, 2018. The devised method used radial basis function (artificial neural networks (ANN)) mathematical method (implementation in Python environment) to perform accurate predictions. For further increase of the accuracy and reduction of prediction noise from the obtained course of the predicted signal, multiple variations of the LMS adaptive filter algorithm (Sign, Sign-Sign, Sign-Regressor) were used (implemented in the MATLAB SW tool). The accuracy of the newly proposed CO2 concentration prediction method exceeds 95% in the selected experiments.Web of Science915877215876

Indirect recognition of predefined human activities

The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.Web of Science2017art. no. 482

Human activity classification using multilayer perceptron

The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.Web of Science2118art. no. 620

A flexible thermoelectric generator worn on the leg to harvest body heat energy and to recognize motor activities: A preliminary study

Wearable devices are commonly used to monitor human movement since motor activity is a fundamental element in all phases of a person's life. Patients with motor disorders need to be monitored for a prolonged period and the battery life can be a limit for such a goal. Here the technique of harvesting energy from body heat to supply energy to wearable devices is investigated. A commercial flexible thermoelectric generator, equipped with an accelerometer, is placed on the lower leg above the ankle. The accelerometer serves to detect diverse motor activities carried out by ten students of VSB-Technical University of Ostrava involved in the execution of two tasks. To summarize, the motor activities analyzed in the proposed work are: "Sit", "Walk", "Rest", "Go biking", "Rest after biking", and "Go down and up the stairs". The maximum measured value of power density was 20.3 mu W cm(-2) for the "Walk" activity, corresponding to a gradient of temperature between the hot and cold side of the thermocouples constituting the flexible thermoelectric generator of 1.5 degrees C, while the minimum measured value of power density was 8.3 mu W cm(-2) for the "Sit" activity, corresponding to a gradient of temperature of 1.1 degrees C. Moreover, a mathematical model was developed for the recognition of motor activities carried out during the execution of the experiments. As a preliminary result, it is possible to state that semi-stationary parts of the signal generated by the thermoelectric generator can be traced back to the performance of an activity.Web of Science9208922087