We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity, which can provide useful insights for activity models, have not been exploited to their full potentials by mining algorithms. In this paper, we utilize temporal features for activity recognition and prediction in a single smart home setting. We discover activity patterns and temporal relations such as the order of activities from real data to develop a prompting system. Analysis of real data collected from smart homes was used to validate the proposed method.
The University of Sydney（悉尼大学）；RMIT University（皇家墨尔本理工大学）
We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise during interactions between the resident and IoT services in smart homes. We propose a generic knowledge graph to represent the relations between IoT services and environment entities. We also profile a generic knowledge graph to a specific smart home setting based on the context information. We propose a conflict taxonomy to capture different types of conflicts in a single resident smart home setting. A conflict detection algorithm is proposed to identify potential conflicts using the profiled kn…
We aim to investigate the potential impacts of smart homes on human behavior. To this end, we simulate a series of human models capable of performing various activities inside a reinforcement learning-based smart home. We then investigate the possibility of human behavior being altered as a result of the smart home and the human model adapting to one-another. We design a semi-Markov decision process human task interleaving model based on hierarchical reinforcement learning that learns to make decisions to either pursue or leave an activity. We then integrate our human model in the smart home w…
SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homes
Midea Group（美的集团）；Beijing University of Posts and Telecommunications（北京邮电大学）；Donghua University（东华大学）；The University of Sydney（悉尼大学）；Peking University（北京大学）
SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Jilin University（吉林大学）；The Chinese University of Hong Kong（香港中文大学）；Pengcheng Laboratory（鹏城实验室）
Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. But new challenges are emerging every day. In this paper, we present recent algori…
Identifying and Addressing User-level Security Concerns in Smart Homes Using "Smaller" LLMs
Department of CSE Bangladesh University of Engineering（计算机科学与工程系 bangladesh 工程大学）；Concordia Institute for Information Systems Engineering Concordia University Montreal, Canada（信息系统工程研究所 博士大学 蒙特利尔 加拿大）
How can we accurately recommend actions for users to control their devices at home? Action recommendation for smart home has attracted increasing attention due to its potential impact on the markets of virtual assistants and Internet of Things (IoT). However, designing an effective action recommender system for smart home is challenging because it requires handling context correlations, considering both queried contexts and previous histories of users, and dealing with capricious intentions in history. In this work, we propose SmartSense, an accurate action recommendation method for smart home…
Queen’s University（女王大学）；Royal Military College of Canada（加拿大皇家军事学院）
The technologies used in smart homes have recently improved to learn the user preferences from feedback in order to enhance the user convenience and quality of experience. Most smart homes learn a uniform model to represent the thermal preferences of users, which generally fails when the pool of occupants includes people with different sensitivities to temperature, for instance due to age and physiological factors. Thus, a smart home with a single optimal policy may fail to provide comfort when a new user with a different preference is integrated into the home. In this paper, we propose a Baye…
Designing smart home services is a complex task when multiple services with a large number of sensors and actuators are deployed simultaneously. It may rely on knowledge-based or data-driven approaches. The former can use rule-based methods to design services statically, and the latter can use learning methods to discover inhabitants' preferences dynamically. However, neither of these approaches is entirely satisfactory because rules cannot cover all possible situations that may change, and learning methods may make decisions that are sometimes incomprehensible to the inhabitant. In this paper…
Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)
School of Interactive Computing, Georgia Institute of Technology（佐治亚理工学院交互计算学院）；School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）
Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments
Fraunhofer Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所）；University of Siegen（锡根大学）；Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用科学大学）
SmartHomeSecure: Automated Detection and Repair of Smart Home Configuration Errors Using Large Language Models
Myers-Lawson School of Construction, Virginia Polytechnic Institute and State University（Myers-Lawson工程学院，弗吉尼亚理工学院和州立大学）；School of Technology, Eastern Illinois University（技术学院，东伊利诺伊大学）
The population is aging, and becoming more tech-savvy. The United Nations predicts that by 2050, one in six people in the world will be over age 65 (up from one in 11 in 2019), and this increases to one in four in Europe and Northern America. Meanwhile, the proportion of American adults over 65 who own a smartphone has risen 24 percentage points from 2013-2017, and the majority have Internet in their homes. Smart devices and smart home technology have profound potential to transform how people age, their ability to live independently in later years, and their interactions with their circle of …
The electricity consumption of buildings composes a major part of the city's energy consumption. Electricity consumption forecasting enables the development of home energy management systems resulting in the future design of more sustainable houses and a decrease in total energy consumption. Energy performance in buildings is influenced by many factors like ambient temperature, humidity, and a variety of electrical devices. Therefore, multivariate prediction methods are preferred rather than univariate. The Honda Smart Home US data set was selected to compare three methods for minimizing forec…
Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；PengCheng Laboratory（鹏城实验室）；The Chinese University of Hong Kong（香港中文大学）；University of Warwick（华威大学）
Smart homes are becoming ubiquitous, but they are not Americans with Disability Act (ADA) compliant. Smart homes equipped with ADA compliant appliances and services are critical for people with disabilities (i.e., visual impairments and limited mobility) to improve independence, safety, and quality of life. Despite all advancements in smart home technologies, some fundamental design and implementation issues remain. For example, people with disabilities often feel insecure to respond when someone knocks on the door or rings the doorbell. In this paper, we present an intelligent system called "…
Energy saving in smart homes based on consumer behaviour: A case study
Institute of Business Information Systems Research and Development（商业信息系统研发研究所）；University of Applied Sciences and Arts Northwestern Switzerland（瑞士西北应用科学与艺术大学）；digitalSTROM AG（digitalSTROM股份公司）
