Modeling Interaction in Multi-Resident Activities

In this paper we investigate the problem of modeling multi-resident activities. Specifically, we explore different approaches based on Hidden Markov Models (HMMs) to deal with parallel activities and cooperative activities. We propose an HMM-based method, called CL-HMM, where activity labels as well as observation labels of different residents are combined to generate the corresponding sequence of activities as well as the corresponding sequence of observations on which a conventional HMM is applied. We also propose a Linked HMM (LHMM) in which activities of all residents are linked at each time step. We compare these two models to baseline models which are Coupled HMM (CHMM) and Parallel HMM (PHMM). The experimental results show that the proposed models outperform CHMM and PHMM when tested on parallel and cooperative activities

Unsupervised Recognition of Multi-Resident Activities in Smart-Homes

Several methods have been proposed in the last two decades to recognize human activities based on sensor data acquired in smart-homes. While most existing methods assume the presence of a single inhabitant, a few techniques tackle the challenging issue of multi-resident activity recognition. To the best of our knowledge, all existing methods for multi-inhabitant activity recognition require the acquisition of a labeled training set of activities and sensor events. Unfortunately, activity labeling is costly and may disrupt the users' privacy. In this article, we introduce a novel technique to recognize multi-inhabitant activities without the need of labeled datasets. Our technique relies on an unlabeled sensor data stream acquired from a single resident, and on ontological reasoning to extract probabilistic associations among sensor events and activities. Extensive experiments with a large dataset of multi-inhabitant activities show that our technique achieves an average accuracy very close to the one of state-of-the-art supervised methods, without requiring the acquisition of labeled data

A review of smart homes in healthcare

The technology of Smart Homes (SH), as an instance of ambient assisted living technologies, is designed to assist the homes’ residents accomplishing their daily-living activities and thus having a better quality of life while preserving their privacy. A SH system is usually equipped with a collection of inter-related software and hardware components to monitor the living space by capturing the behaviour of the resident and understanding his activities. By doing so the system can inform about risky situations and take actions on behalf of the resident to his satisfaction. The present survey will address technologies and analysis methods and bring examples of the state of the art research studies in order to provide background for the research community. In particular, the survey will expose infrastructure technologies such as sensors and communication platforms along with artificial intelligence techniques used for modeling and recognizing activities. A brief overview of approaches used to develop Human–Computer interfaces for SH systems is given. The survey also highlights the challenges and research trends in this area

Exploring entropy measurements to identify multi-occupancy in activities of daily living

Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another individual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors

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

BUILDING INCLUSIVE RESPONSES TO CLIMATE HAZARDS: AN INTERSECTIONAL ANALYSIS OF WILDFIRE IN NORTHERN SASKATCHEWAN

Climate hazards such as wildfires are not just ecological, but also profoundly social. These hazards—and responses to them—are shaped by, and become layered onto, existing political, economic, and social landscapes, resulting in different experiences and vulnerabilities for people from diverse social locations (e.g. gender, race, ethnicity, age, place). Intersectionality has been promoted as a theoretical lens for understanding these differential vulnerabilities, but its empirical application to climate hazards research and practice is limited, particularly in the global North. In response, this research develops and applies a multi-leveled intersectionality theoretical framework to examine how residents in a jurisdictionally complex and socially diverse region of northern Saskatchewan experience and respond to wildfire. Using a qualitative case study design, this study employs media analysis, semi-structured interviews, and photovoice to investigate how residents experienced a major wildfire event and how these experiences interact with and are shaped by social discourses, social structures, and power relations. The results of this research are reported in three core manuscripts, each of which brings a separate level of intersectional analysis to the forefront. The first manuscript demonstrates that mainstream media largely reflected and reinforced a characterization of wildfire response that is highly gendered and exclusionary and discusses the discursive and material implications of such framings. The second manuscript illustrates that impacts to locally significant values are experienced differently across intersections of identity and that these differences are influenced by social structures such as histories of colonization and gendered norms and expectations. The third manuscript highlights how emergency and wildfire management institutions support pathways for adaptation that are characterized by resistance and incremental change, resulting in uneven inclusion of diverse voices, knowledges, and experiences. These manuscripts also reveal how residents and local communities enacted their agency to challenge dominant discourses, respond to locally significant impacts and losses, and advocate for more transformative approaches to wildfire response, recovery, and adaptation. The core contributions of this research are threefold. The study: 1) operationalizes intersectionality to examine how identity attributes operate within social discourse, residents’ experiences, and institutions relevant to a major wildfire event; 2) applies the chosen methods together in a way that enables the multi-level intersectional analysis; and 3) points toward practical strategies for building emergency management and adaptation planning processes that are inclusive and representative of a diverse range of society as communities in northern Saskatchewan continue to live with fire in the future