Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Context-based interaction design for smart homes

This paper presents the notion of Context-based Activity Design (CoBAD) that represents context with its dynamic changes and normative activities in an interactive system design. The development of CoBAD requires an appropriate context ontology model and inference mechanisms. The incorporation of norms and information field theory into Context State Transition Model, and the implementation of new conflict resolution strategies based on the specific situation are discussed. A demonstration of CoBAD using a human agent scenario in a smart home is also presented. Finally, a method of treating conflicting norms in multiple information fields is proposed.

Big data, smart homes and ambient assisted living

Objectives: To discuss how current research in the area of smart homes and ambient assisted living will be influenced by the use of big data. Methods: A scoping review of literature published in scientific journals and conference proceedings was performed, focusing on smart homes, ambient assisted living and big data over the years 2011-2014. Results: The health and social care market has lagged behind other markets when it comes to the introduction of innovative IT solutions and the market faces a number of challenges as the use of big data will increase. First, there is a need for a sustainable and trustful information chain where the needed information can be transferred from all producers to all consumers in a structured way. Second, there is a need for big data strategies and policies to manage the new situation where information is handled and transferred independently of the place of the expertise. Finally, there is a possibility to develop new and innovative business models for a market that supports cloud computing, social media, crowdsourcing etc. Conclusions: The interdisciplinary area of big data, smart homes and ambient assisted living is no longer only of interest for IT developers, it is also of interest for decision makers as customers make more informed choices among today's services. In the future it will be of importance to make information usable for managers and improve decision making, tailor smart home services based on big data, develop new business models, increase competition and identify policies to ensure privacy, security and liability.

Dynamic privacy in a smart house environment

A smart house can be regarded as a surveillance environment in which the person being observed carries out activities that range from intimate to more public. What can be observed depends on the activity, the person observing (e.g. a carer) and policy. In assisted living smart house environments, a single privacy policy, applied throughout, would be either too invasive for an occupant, or too restrictive for an observer, due to the conflicting goals of surveillance and private environments. Hence, we propose a dynamic method for altering the level of privacy in the environment based on the context, the situation within the environment, encompassing factors relevant to ensuring the occupant's safety and privacy. The context is mapped to an appropriate level of privacy, which is implemented by controlling access to data sources (e.g. video) using data hiding techniques. The aim of this work is to decrease the invasiveness of the technology, while retaining the purpose of the system.

Spatial activity recognition in a smart home environment using a chemotactic model

Spatial activity recognition is challenging due to the amount of noise incorporated during video tracking in everyday environments. We address the spatial recognition problem with a biologically-inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to change motile behaviour in relation to environmental gradients. Through adoption of chemotactic principles, we propose the chemotactic model and evaluate its performance in a smart house environment. The model exhibits greater than 99% recognition performance with a diverse six class dataset and outperforms the Hidden Markov Model (HMM). The approach also maintains high accuracy (90-99%) with small training sets of one to five sequences. Importantly, unlike other low-level spatial activity recognition models, we show that the chemotactic model is capable of recognising simple interwoven activities.

Smart grid

All over the world, electrical power systems are encountering radical change stimulated by the urgent need to decarbonize electricity supply, to swap aging resources and to make effective application of swiftly evolving information and communication technologies (ICTs). All of these goals converge toward one direction; ‘Smart Grid.’ The Smart Grid can be described as the transparent, seamless, and instantaneous two-way delivery of energy information, enabling the electricity industry to better manage energy delivery and transmission and empowering consumers to have more control over energy decisions. Basically, the vision of Smart Grid is to provide much better visibility to lower-voltage networks as well as to permit the involvement of consumers in the function of the power system, mostly through smart meters and Smart Homes. A Smart Grid incorporates the features of advanced ICTs to convey real-time information and facilitate the almost instantaneous stability of supply and demand on the electrical grid. The operational data collected by Smart Grid and its sub-systems will allow system operators to quickly recognize the best line of attack to protect against attacks, susceptibility, and so on, sourced by a variety of incidents. However, Smart Grid initially depends upon knowing and researching key performance components and developing the proper education program to equip current and future workforce with the knowledge and skills for exploitation of this greatly advanced system. The aim of this chapter is to provide a basic discussion of the Smart Grid concept, evolution and components of Smart Grid, environmental impacts of Smart Grid and then in some detail, to describe the technologies that are required for its realization. Even though the Smart Grid concept is not yet fully defined, the chapter will be helpful in describing the key enabling technologies and thus allowing the reader to play a part in the debate over the future of the Smart Grid. The chapter concludes with the experimental description and results of developing a hybrid prediction method for solar power which is applicable to successfully implement the ‘Smart Grid.’

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Computer Aided Design Algorithmic Technique for Very Large Scale Integrated Circuit Design and Smart Home Embedded System Design

To tackle the challenges at circuit level and system level VLSI and embedded system design, this dissertation proposes various novel algorithms to explore the efficient solutions. At the circuit level, a new reliability-driven minimum cost Steiner routing and layer assignment scheme is proposed, and the first transceiver insertion algorithmic framework for the optical interconnect is proposed. At the system level, a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems, which optimizes system energy consumption under stochastic fault occurrences, is proposed. The embedded system design is also widely used in the smart home area for improving health, wellbeing and quality of life. The proposed scheduling scheme for multiprocessor embedded systems is hence extended to handle the energy consumption scheduling issues for smart homes. The extended scheme can arrange the household appliances for operation to minimize monetary expense of a customer based on the time-varying pricing model.

The Messaging Kettle: It’s IoTea time

We demonstrate a working prototype of a Messaging Kettle designed to facilitate asynchronous communication and enable a sense of presence between adult children and their older parents living abroad. Our goal is to offer a human centred critique of the Internet of Things, which has largely been conceived without consideration of the people who will use the things, and rather has traditionally moved from a technology oriented perspective. In the case of smart homes this approach has produced a wide array of projects focused on monitoring the habits of the elderly, recognizing anomalies and alerting the caregivers. In contrast we propose to focus on engagement and reciprocity, building on the rituals associated to habitually used and cherished objects. We conclude revisiting the technology oriented framework for the Internet of Things to include our observations on people’s perspective on smart communicating objects.