Context-aware Computing System Research Articles

인간 적응형 가전기기를 위한 거주자 심박동 기반 신체활동량 추정

Intelligent homes consist of ubiquitous sensors, home networks, and a context-aware computing system. These homes are expected to offer many services such as intelligent air-conditioning, lighting control, health monitoring, and home security. In order to realize these services, many researchers have worked on various research topics including smart sensors with low power consumption, home network protocols, resident and location detection, context-awareness, and scenario and service control. This paper presents the real-time metabolic rate estimation method that is based on measured heart rate for human adaptive appliance (air-conditioner, lighting etc.). This estimation results can provide valuable information to control smart appliances so that they can adjust themselves according to the status of residents. The heart rate based method has been experimentally compared with the location-based method on a test bed.

Introduction to the special issue on multimedia intelligent services and technologies

The rapid development of digital media technologies enables the emergence of novel media content types for eCommerce, eEducation, and digital entertainment. On the other hand, the advances in communication and microelectronics have led to a transition from traditional personal computer-centric to more intuitive human-centric information access modes and the embedment of computer systems throughout the natural environment. This type of computation is generally known as Pervasive or Ubiquitous Computing. It can also be referred to as ambient intelligence, or talking about media environments as ambient media. It allows a person to use a variety of devices and sensor networks seamlessly embedded throughout our daily life, such as personal digital assistant (PDA), smart electronics, sensors, as well as personal computer, to access those media contents. The combination of these two trends (emergence of media and pervasive computing) holds the potential of providing a user with seamless and ubiquitous access to rich and dynamic multimedia resources from anywhere and anytime. With the emergence of ubiquitous and pervasive computation, distributed devices embedded in the natural human environment are getting more and more intelligent. This enables more advanced multimedia services far beyond simple video streaming and communication services. These services are currently on the brink of emergence, and include, e.g., personalized media environments, smart homes, semantic locative media, and context aware computer systems. There are several new technical challenges for effective and appropriate content delivery to pervasive user-terminals. First, different users may have different needs. Nowadays, multimedia content is absolutely overabundant, while the portion of which a user really desired is rather small. Smart personalization techniques, where content is aggregated, either delivered in push and/or pull schemes are required. Second, pervasive end devices usually have various capabilities (e.g., screen size, color depth, video/audio codec, memory, CPU speed, and electrical power), and network connection and transmission bandwidth might be changing dynamically. Therefore, intelligent multimedia services are expected for efficient storage, search, filtering, adaptation, and presentation of media content, to deliver a ubiquitous and personalized media experience to end users. Multimedia intelligent services and technologies have attracted increasing interest in both industry and academia over the last decade. Submissions to this special issue come from an open call for papers as well as from selected papers presented at the 6th International Conference on Ubiquitous Intelligence and Computing (UIC-09) held at Brisbane, Australia, July 7–9, 2009. A large number of reviewers assisted us in the review process. In order to ensure high reviewing Z. Yu (&) School of Computer Science, Northwestern Polytechnical University, Xi’an, China e-mail: zhiwenyu@nwpu.edu.cn

Technological and Research Perspectives of Old-Age Ubiquitous Computing

Technological advances allow networked and context-aware computer systems to be integrated into devices, environments, everyday objects, and garments. The idea is that such smart environments and artifacts are able to perceive the users’ context and activity, anticipate their needs, and act to provide proactive support. Old-age ubiquitous computing has the potential to increase the length of time that both individuals and groups are able to lead independent lives, even at an advanced age. Examples of this range from smart reading-glasses that are capable of reading articles to the user, all the way to body implants designed to increase human perceptive, cognitive, and motor capabilities.

HMM machine learning and inference for Activities of Daily Living recognition

In a human-centric smart space, Activities of Daily Living (ADL) analysis can provide very useful information for elder care and long-term care services. ADL is defined as an assessment of a person's functional status. Many recent researches concentrate on designing a good Context Aware Computing System to automate the actions necessarily triggered by ADL recognitions. Implementing a correct ADL recognition engine is a hard work, but will repay the system with lower inference errors and higher system dependability. A good ADL recognition engine is required to adjust its inference strategy based on the learning capability in order to avoid a high error rate, especially in real world inputs with a significant difference as compared to those in the training phase. In this paper, we proposed a powerful inference engine based on the Hidden Markov Model, called the Adaptive Learning Hidden Markov Model (ALHMM), which combines the Viterbi and Baum---Welch algorithms to enhance the accuracy and learning capability. The assessments of ALHMM are conducted on the Python platform and show the practical feasibility of Activity Recognition in residential homes. Such a technique can provide the key answer required for advancing the state-of-the-art in context-aware computing and applications in real life.

Resident Location-Recognition Algorithm Using a Bayesian Classifier in the PIR Sensor-Based Indoor Location-Aware System

Intelligent home service systems consist of ubiquitous sensors, a home network, and a context-aware computing system that together collect residential environment information and provide intelligent services such as controlling the environment or lighting. Determining a resident's location in the smart home or smart office is a key to such a system. This correspondence presents an enhanced location-recognition algorithm using a Bayesian classifier for the pyroelectric infrared sensor-based indoor location-aware system that is a nonterminal-based location-aware system proposed in a previous paper. This correspondence compares the conventional and enhanced location-recognition algorithms and their performance. The feasibility of the system is evaluated experimentally on a test bed.