Detecting human movement by differential air pressure sensing in HVAC system ductwork: An exploration in infrastructure mediated sensing (original) (raw)
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REAL-TIME BUILDING OCCUPANCY SENSING FOR SUPPORTING DEMAND DRIVEN HVAC OPERATIONS
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Indoor Air Quality Monitoring Using Infrastructure-Based Motion Detectors
2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
Poor indoor air quality is a significant burden to society that can cause health issues and decrease productivity. According to research, indoor air quality is intrinsically linked with human activity and mobility. Indeed, mobility is directly linked with transfer of small particles (e.g. PM 2.5) and extent of activity affects production of CO 2. Currently, however, estimation of indoor quality is difficult, requiring deployment of highly specialized sensing devices which need to be carefully placed and maintained. In this paper, we contribute by examining the suitability of infrastructure-based motion detectors for indoor air quality estimation. Such sensors are increasingly being deployed into smart environments, e.g., to control lighting and ventilation for energy management purposes. Being able to take advantage of these sensors would thus provide a cost-effective solution for indoor quality monitoring without need for deploying additional sensors. We perform a feasibility study considering measurements collected from a smart office environment having a dense deployment of motion detectors and correlating measurements obtained from motion detectors against air quality values. We consider two main pollutants, PM 2.5 and CO 2 , and demonstrate that there indeed is a connection between extent of movement and PM 2.5 concentration. However, for CO 2 , no relationship can be established, mostly due to difficulties in separating between people passing by and those residing long-term in the environment.
GasSense: Appliance-Level, Single-Point Sensing of Gas Activity in the Home
Lecture Notes in Computer Science, 2010
This paper presents GasSense, a low-cost, single-point sensing solution for automatically identifying gas use down to its source (e.g., water heater, furnace, fireplace). This work adds a complementary sensing solution to the growing body of work in infrastructure-mediated sensing. GasSense analyzes the acoustic response of a home's government mandated gas regulator, which provides the unique capability of sensing both the individual appliance at which gas is currently being consumed as well as an estimate of the amount of gas flow. Our approach provides a number of appealing features including the ability to be easily and safely installed without the need of a professional. We deployed our solution in nine different homes and initial results show that GasSense has an average accuracy of 95.2% in identifying individual appliance usage.
Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition
2006
The home deployment of sensor-based systems offers many opportunities, particularly in the area of using sensor-based systems to support aging in place by monitoring an elder's activities of daily living. But existing approaches to home activity recognition are typically expensive, difficult to install, or intrude into the living space. This paper considers the feasibility of a new approach that "reaches into the home" via the existing infrastructure. Specifically, we deploy a small number of low-cost sensors at critical locations in a home's water distribution infrastructure. Based on water usage patterns, we can then infer activities in the home. To examine the feasibility of this approach, we deployed real sensors into a real home for six weeks. Among other findings, we show that a model built on microphone-based sensors that are placed away from systematic noise sources can identify 100% of clothes washer usage, 95% of dishwasher usage, 94% of showers, 88% of toilet flushes, 73% of bathroom sink activity lasting ten seconds or longer, and 81% of kitchen sink activity lasting ten seconds or longer. While there are clear limits to what activities can be detected when analyzing water usage, our new approach represents a sweet spot in the tradeoff between what information is collected at what cost.
Detecting Room-to-Room Movement by Passive Infrared Sensors in Home Environments
We discuss in this paper the problem of simultaneous track-ing, which exploits the synergy between location and movement to pro-vide the information necessary for intelligent home appliance control. Our goal is to carry out accurate movement estimation for multiple peo-ple in a home environment. We propose an approach that uses infor-mation gathered using only passive infrared sensors commonly found in lighting control systems. No special devices or video cameras are needed. Moreover, it is not necessary to carry out data collection for training. We evaluated our approach by conducting experiments in a real home fitted with sensors and we confirmed that room-to-room movement was detected with an accuracy of 0.82 for two inhabitants who moved freely through the house.
An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors
Electronics
As people get older, living at home can expose them to potentially dangerous situations when performing everyday actions or simple tasks due to physical, sensory or cognitive limitations. This could compromise the residents’ health, a risk that in many cases could be reduced by early detection of the incidents. The present work focuses on the development of a system capable of detecting in real time the main activities of daily life that one or several people can perform at the same time inside their home. The proposed approach corresponds to an unsupervised learning method, which has a number of advantages, such as facilitating future replication or improving control and knowledge of the internal workings of the system. The final objective of this system is to facilitate the implementation of this method in a larger number of homes. The system is able to analyse the events provided by a network of non-intrusive sensors and the locations of the residents inside the home through a Bl...
Classification of Human Activities Indoors using Microclimate Sensors and Semiconductor Gas Sensors
Proceedings of the 8th International Conference on Sensor Networks, 2019
Nowadays, one of important problems faced by people in developed countries is poor indoor air quality (IAQ). Factors, which influence air inside buildings should be recognised for planning actions aimed at the improvement of indoor conditions. Our study was focused on human impact on IAQ. The aim of this work was the classification of the occurrence of occupants activities, which influence IAQ. The classification was based on measurements of indoor air using sensors. The presented analysis was focussed on the kind of sensors that are capable of providing the information which is most relevant for classification. Two groups of such devices were considered. The first included sensors which are typically used in microclimate measurements, i.e. temperature, relative humidity and CO2 concentration sensor. The second group included semiconductor gas sensors, which are considered as the sources of information about the chemical quality of indoor air. Classification tree was applied as the classifier. The obtained results showed that the measurement data provided by both groups of sensors can be applied for the classification of human activities, with the satisfactory performance. It may be understood that the impact of human activities on indoor air is very broad and may be examined using versatile sources of measurement data.
Energies
Building Energy and Comfort Management (BECM) systems have the potential to considerably reduce costs related to energy consumption and improve the efficiency of resource exploitation, by implementing strategies for resource management and control and policies for Demand-Side Management (DSM). One of the main requirements for such systems is to be able to adapt their management decisions to the users’ specific habits and preferences, even when they change over time. This feature is fundamental to prevent users’ disaffection and the gradual abandonment of the system. In this paper, a sensor-based system for analysis of user habits and early detection and prediction of user activities is presented. To improve the resulting accuracy, the system incorporates statistics related to other relevant external conditions that have been observed to be correlated (e.g., time of the day). Performance evaluation on a real use case proves that the proposed system enables early recognition of activi...
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Energy and Buildings, 2011
In this paper, we propose a vision-based system for human detection and tracking in indoor environment allowing to collect higher level information on people activity. The developed presence sensor based on video analysis, using a static camera is first of all presented. Composed of three main steps, the first one consists in change detection using a background model updated at different levels to manage the most common variations of the environment. A moving objects tracking based on interest points tracking is then performed. The classification step finally relies on the use of statistical tools and multiple classifiers for the whole body and for the upper-body. The validation protocol, defined by the industrial partners involved in the CAP T HOM project focusing among other things on "Energy Management in Building", is then detailed. Three applications integrated into the CAP T HOM draft finally illustrate how the developed system can also help in collecting useful information for the building management system: occupancy detection and people counting as well as activity characterization and 3D location extend to a wide variety of buildings technology research areas such as human-centered environmental control including heating adjustment and demand-controlled ventilation, but also security and energy efficient buildings.
Sensor-Based Activity Recognition Inside Smart Building Energy and Comfort Management Systems
2019
The challenge of Smart Building Energy and Comfort Management (BECM) systems is to schedule home appliances according to users’ comfort requirements, while contributing to an efficient and sustainable use of the available energy sources, supplied by either the electricity grid or Renewable Energy Sources (RES). To this aim, BECM systems have to monitor users’ habits and learn their preferences, so that their actions can be predicted and appliances can be scheduled accordingly.This paper stems from the observation that actions are usually performed in sequences that repeat according to a pattern. Therefore, activities can be recognized and predicted as soon as a pattern of actions is detected. The framework proposed in this paper aims to predict activities by analyzing the sequences of actions detected by sensors deployed in a Smart Building. Furthermore, a correlation between subsequent activities is found so that sequences of activities can be predicted. Simulation results show tha...