Non-Intrusive Occupancy Detection Methods and Models

Occupants in the built environment impact facility energy consumption and indoor air quality. Predicting the presence of occupants within the built environment can therefore be used to manage these factors while providing additional benefits in terms of emergency management and future space utilization. Detecting occupancy requires a combination of sensors and models to accurate assess data collected within facilities to predict occupancy. This thesis investigated occupancy detection through a non-invasive data collection sensors and model. Specifically, this thesis sought to answer two research questions examining the ability of a radial basis function to accurately predict occupancy when generated from data collected from two facilities. Generated models were evaluated on the data from which they were derived, self-estimation, as well as applied to other areas within the same facility, cross-estimation. The motivation, sensors and models, were discussed to establish a framework. The principle implications of this research is to reduce energy consumption by knowing when the built environment is occupied through the use of non-invasive data collection sensors supplying inputs into a model. The resulting accuracy rates of the derived models ranged from 48% - 68% when using three collected parameters: temperature, relative humidity and carbon dioxide

Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

Occupancy monitoring and prediction in ambient intelligent environment

Occupancy monitoring and prediction as an influential factor in the extraction of occupants' behavioural patterns for the realisation of ambient intelligent environments is addressed in this research. The proposed occupancy monitoring technique uses occupancy detection sensors with unobtrusive features to monitor occupancy in the environment. Initially the occupancy detection is conducted for a purely single-occupant environment. Then, it is extended to the multipleoccupant environment and associated problems are investigated. Along with the occupancy monitoring, it is aimed to supply prediction techniques with a suitable occupancy signal as the input which can enhance efforts in developing ambient intelligent environments. By predicting the occupancy pattern of monitored occupants, safety, security, the convenience of occupants, and energy saving can be improved. Elderly care and supporting people with health problems like dementia and Alzheimer disease are amongst the applications of such an environment. In the research, environments are considered in different scenarios based on the complexity of the problem including single-occupant and multiple-occupant scenarios. Using simple sensory devices instead of visual equipment without any impact on privacy and her/his normal daily activity, an occupant is monitored in a living or working environment in the single-occupant scenario. ZigBee wireless communication technology is used to collect signals from sensory devices such as motion detection sensors and door contact sensors. All these technologies together including sensors, wireless communication, and tagging are integrated as a wireless sensory agent

Computer vision based posture estimation and fall detection.

Falls are a major health problem, especially in the elderly population. Increasing fall events demands a high quality of service and dedicated medical treatment which is an economic burden. Serious injuries due to fall can cost lives in the absence of immediate care and support. There- fore, a monitoring system that can accurately detect fall events and generate instant alerts for immediate care is extremely necessary. To address this problem, this research aims to develop a computer vision-based fall detection system. This study proposes fall detection in three stages: (A) Detection of human silhouette and recognition of the pose, (B) Detection of the human as three regions for different postures including fall and (C) Recognise fall and non-fall using locations of human body regions as distinguishing features. The first stages of work comprise human silhouette detection and identification of activities in the form of different poses. Identifying a pose is important to understand a fall event where a change of pose defines its characteristics. A fall event comprises of sequential change of poses and ends up in a lying pose. Initial pose during a fall can be standing, sitting or bending but the final pose is usually a lying pose. It would, therefore, be beneficial if lying pose is recognised more accurately than other normal activities such as standing, sitting, bending or crawling to address a fall. Hence in the first stage, Background Subtraction (BS) is used to detect human silhouette. After background subtraction, the foreground images were used in a Convolutional Neural Network (CNN) to recognise different poses. The RGB and the Depth images were captured from a Kinect Sensor. The fusion of RGB and Depth images were explored for feeding to a convolutional neural net- work. Depth together with RGB complimented each other to overcome their weakness respectively and proved to be a significant strategy. The classification was performed using CNN to recognise different activities with 81% accuracy on validation. The other challenge in fall detection is the tracking of a person during a fall. Background Subtraction is not sufficient to track a fallen person especially when there are lighting and viewpoint variations in the environment and present of another object like furniture, a pet or even another person. Furthermore, tracking be- comes tougher during the fall in comparison to normal activities like walking or sitting because the rate of change pose is higher during a fall. To overcome this, the idea is to locate the regions in the body in every frame and consider it as a stable tracking strategy. The location of the body parts provides crucial information to distinguish falls from the other normal activities as the person is detected all the time during these activities. Hence the second stage of this research consists of posture detection using the pose estimation technique. This research proposes to use CNN based pose estimation using simplified human postures. The available joints are grouped according to three regions: Head, Torso and Leg and then finally fed to the CNN model with just three inputs instead of several available joints. This strategy added stability in pose detection and proved to be more effective against complex poses observed during a fall. To train the CNN model, transfer learning technique was used. The model was able to achieve 96.7% accuracy in detecting the three regions on different human postures on the publicly available dataset. A system which considers all the lying poses as falls can also generate a higher false alarm. Lying on bed or sofa can easily generate a fall alarm if they are recognised as falls. Hence, it is important to recognise actual fall by considering a sequence of frames that defines a fall and not just the lying pose. In the third and final stage, this study proposes Long Short-Term Memory (LSTM) recurrent networks-based fall detection. The proposed LSTM model uses the detected three region’s location as input features. LSTM is capable of using contextual information from the sequential input patterns. Therefore, the LSTM model was fed with location features of different postures in a sequence for training. The model was able to learn fall patterns and distinguish them from other activities with 88.33% accuracy. Furthermore, the precision of the fall class was 1.0. This is highly desirable in the case of fall detection as there is no false alarm and this means that the cost incurred in calling medical support for a false alarm can be completely avoided

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things