Intelligent Coordination and Automation for Smart Home Accessories

Smarthome accessories are rapidly becoming more popular. Although many companies are making devices to take advantage of this market, most of the created smart devices are actually unintelligent. Currently, these smart home devices require meticulous, tedious configuration to get any sort of enhanced usability over their analog counterparts. We propose building a general model using machine learning and data science to automatically learn a user\u27s smart accessory usage to predict their configuration. We have identified the requirements, collected data, recognized the risks, implemented the system, and have met the goals we set out to accomplish

Improving Home Automation by Discovering Regularly Occurring Device Usage Patterns

The data stream captured by recording inhabitantdevice interactions in an environment can be mined to discover significant patterns, which an intelligent agent could use to automate device interactions. However, this knowledge discovery problem is complicated by several challenges, such as excessive noise in the data, data that does not naturally exist as transactions, a need to operate in real time, and a domain where frequency may not be the best discriminator. In this paper, we propose a novel data mining technique that addresses these challenges and discovers regularly-occurring interactions with a smart home. We also discuss a case study that shows the data mining technique can improve the accuracy of two prediction algorithms, thus demonstrating multiple uses for a home automation system. Finally, we present an analysis of the algorithm and results obtained using inhabitant interactions. 1

Discovering human activities from binary data in smart homes

With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual’s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods

Measuring Urban Vibrancy of Residential Communities Using Big Crowdsourced Geotagged Data

The pervasiveness of mobile and sensing technologies today has facilitated the creation of Big Crowdsourced Geotagged Data (BCGD) from individual users in real time and at different locations in the city. Such ubiquitous user-generated data allow us to infer various patterns of human behavior, which helps us understand the interactions between humans and cities. In this article, we aim to analyze BCGD, including mobile consumption check-ins, urban geography data, and human mobility data, to learn a model that can unveil the impact of urban geography and human mobility on the vibrancy of residential communities. Vibrant communities are defined as places that show diverse and frequent consumer activities. To effectively identify such vibrant communities, we propose a supervised data mining system to learn and mimic the unique spatial configuration patterns and social interaction patterns of vibrant communities using urban geography and human mobility data. Specifically, to prepare the benchmark vibrancy scores of communities for training, we first propose a fused scoring method by fusing the frequency and the diversity of consumer activities using mobile check-in data. Besides, we define and extract the features of spatial configuration and social interaction for each community by mining urban geography and human mobility data. In addition, we strategically combine a pairwise ranking objective with a sparsity regularization to learn a predictor of community vibrancy. And we develop an effective solution for the optimization problem. Finally, our experiment is instantiated on BCGD including real estate, point of interests, taxi and bus GPS trajectories, and mobile check-ins in Beijing. The experimental results demonstrate the competitive performances of both the extracted features and the proposed model. Our results suggest that a structurally diverse community usually shows higher social interaction and better business performance, and incompatible land uses may decrease the vibrancy of a community. Our studies demonstrate the potential of how to best make use of BCGD to create local economic matrices and sustain urban vibrancy in a fast, cheap, and meaningful way

The Minimum Description Length Principle for Pattern Mining: A Survey

This is about the Minimum Description Length (MDL) principle applied to pattern mining. The length of this description is kept to the minimum. Mining patterns is a core task in data analysis and, beyond issues of efficient enumeration, the selection of patterns constitutes a major challenge. The MDL principle, a model selection method grounded in information theory, has been applied to pattern mining with the aim to obtain compact high-quality sets of patterns. After giving an outline of relevant concepts from information theory and coding, as well as of work on the theory behind the MDL and similar principles, we review MDL-based methods for mining various types of data and patterns. Finally, we open a discussion on some issues regarding these methods, and highlight currently active related data analysis problems

Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders’ requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9)

人間の行為選好と信頼感に対応したホームエージェントの設計と実効的評価に関する研究

人間の生活の利便性や操作性などの向上を目的として，家庭で使われる機器（家電機器）が開発されてきた．一方，多機能な家電機器が住宅内にあふれ，住民（ユーザ）による操作量が増えてしまった．そこで，近年，ユーザに代わって家電操作を実行するホームエージェントが提案されている．従来のホームエージェントの研究では，主に「家電機器の状態，ユーザの状態，ユーザの行動履歴から，ユーザの行動パターンを学習し，家電機器を自動操作（操作代行）すること」を目指している．また，ホームエージェントによる操作代行は，実際にホームエージェントシステムを組み込んだ住宅を用意した実証実験が行われている．しかし，ユーザのためにホームエージェントが操作代行を行うにも関わらず，ユーザ目線が欠けたホームエージェントが多い．ホームエージェントは学習した行動パターンに沿って一方的に操作代行を行い，ユーザの都合を無視している．また，ホームエージェントの評価では様々な被験者での実験が必要であるが，実験では実証実験の環境に被験者が数か月に渡って実際に住む必要があり，多くの評価実験が行えない．本論文は，ホームエージェントによる操作代行に対するユーザの捉え方を重視し，ユーザの行為選好と信頼感に対応したホームエージェントの設計と実効的な評価方法を提案する．ユーザの行為選好とは，家電操作には，ユーザが操作代行を望む操作だけではなく，ユーザ自身の楽しみなどの理由により操作代行を望まない操作があるということである．そのため，ユーザ自身が楽しむための家電操作の存在に着目したホームエージェントの設計が必要である。また，ホームエージェントの操作代行の信頼性に対するユーザの信頼感とは，ホームエージェントの操作代行の性能をユーザが見極めることで，ホームエージェントへの信頼感を持つことにあたる．ホームエージェントはユーザの行動を完ぺきに予測することができないため，ホームエージェントによる操作代行に対してユーザが信頼感を抱くことは重要である．本論文は以下のように構成されている．まず第1章で本論文の目的を述べる．ホームエージェントが操作代行を行うときに，ユーザ目線を考慮することが重要であり，また，ホームエージェントを評価する際には様々な被験者で行うことが必要である．本論文では，ユーザ目線としてユーザの行為選好と信頼感に着目し，それらに対応したホームエージェントの設計を示し，さらにホームエージェントの実効的な評価方法を提案する．本論文では，従来のホームエージェントに対して3つの問題点を示した．1つ目は，家電操作には，操作代行を望む操作だけではなく，ユーザ自身の楽しみなどの理由により操作代行を望まない操作があることに注意していなかったことである．2つ目はホームエージェントの操作代行の信頼性に対するユーザの信頼感を考慮していなかったことである．3つ目は評価実験に時間がかかることである．第2章ではホームエージェントに関する研究について述べ，第1章で挙げた3つの問題点の分析を行う．第3章では，1つ目の問題点に対して，ユーザ自身が楽しむための操作の存在に着目し，ユーザの行為選好の推測方法を提案する．ただし，ユーザの行為選好はユーザの考え方のため，ホームエージェントは知り得ない．そのため，ユーザが行為選好をホームエージェントに指示する必要があるが，ユーザが必ず行為選好を指示するとは限らない．そこで，ホームエージェントがユーザの行為選好を推測する方法を示す．さらに，ユーザの行為選好の推測方法については，ホームエージェントへの適用事例を示す．第4章では，2つ目の問題点に対して，ホームエージェントに対するユーザの信頼性の醸成方法について述べる．従来研究では，ユーザがホームエージェントの性能を見極め，ユーザから指示を出すという過程を通じて信頼感を醸成していくことが提案されている．しかし，ユーザがホームエージェントの予測内容を監視する必要があった．そこで，従来よりもホームエージェントの性能理解とユーザからの指示を容易にするために，人間同士での対話をまねることと，客観的な指標を取り入れ，信頼感の醸成方法の高度化と効率化を示す．第5章では，3つ目の問題点に対して，シミュレーションを用いた実効的な評価手法を提案する．評価実験にシミュレーションを用いることで，評価実験の短時間化ができる．しかし，シミュレーションでは実験者が想定できる典型ユーザでの評価になってしまう点とホームエージェントごとに評価環境を構築しなければならない点という問題があった．まず，被験者の多様性を拡大するためにアンケートに基づいたシミュレーションによる評価方法を示す．次に，一部の修正や追加によって評価環境を作成できるように，ホームエージェントの評価環境に必要な基本構造とする共通プラットフォームを示す．また，アンケートを取り入れることで様々な被験者で評価実験を行える評価方法を示す．第6章では，第5章で提案した評価方法を用いて行った第3章で示したユーザの行為選好の推測方法と第4章で示したホームエージェントの信頼感の醸成方法の高度化と効率化の検証について述べる．第7章では，本研究の成果をまとめ，今後の展望を述べる．以上のように，ユーザ目線を取り入れたホームエージェントの重要性を示し，複雑化するホームエージェントを効率的に評価する手法を提供した．電気通信大学201