A review of smart homes-Present state and future challenges

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive, and anticipatory communications. Such innovative interaction paradigms make AmI technology a suitable candidate for developing various real life solutions, including in the healthcare domain. This survey will discuss the emergence of AmI techniques in the healthcare domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of AmI, such as smart environments and wearable medical devices. We will summarize the state-of-the-art artificial intelligence (AI) methodologies used for developing AmI system in the healthcare domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions), and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

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A Survey on Ambient Intelligence in Healthcare

Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies

In order for humans and robots to interact in an effective and intuitive manner, robots must obtain information about the human affective state in response to the robot's actions. This secondary mode of interactive communication is hypothesized to permit a more natural collaboration, similar to the "body language" interaction between two cooperating humans. This paper describes the implementation and validation of a hidden Markov model (HMM) for estimating human affective state in real time, using robot motions as the stimulus. Inputs to the system are physiological signals such as heart rate, perspiration rate, and facial muscle contraction. Affective state was estimated using a two- dimensional valence-arousal representation. A robot manipulator was used to generate motions expected during human-robot interaction, and human subjects were asked to report their response to these motions. The human physiological response was also measured. Robot motions were generated using both a nominal potential field planner and a recently reported safe motion planner that minimizes the potential collision forces along the path. The robot motions were tested with 36 subjects. This data was used to train and validate the HMM model. The results of the HMM affective estimation are also compared to a previously implemented fuzzy inference engine.

Affective State Estimation for Human–Robot Interaction

Mobile phones seem to present the perfect user interface for interacting with smart environments, e.g. smart-home systems, as they are nowadays ubiquitous and equipped with an increasing amount of sensors and interface components, such as multi-touch screens. After giving an overview on related work this paper presents the adapted design methodology proposed by Wobbrock et al. (2009) for the development of a gesture-based user interface to a smart-home system. The findings for the new domain, device and gesture space are presented and compared to findings by Wobbrock et al. (2009) . Three additional steps are described: A small pre-test survey, a mapping and a memory test and a performance test of the implemented system. This paper shows the adaptability of the approach described by Wobbrock et al. (2009) for three-dimensional gestures in the smart-home domain. Elicited gestures are described and a first implementation of a user interface based on these gestures is presented.

I'm home: Defining and evaluating a gesture set for smart-home control

This paper introduces a new robotic smart house, Intelligent Sweet Home, developed at KAIST in Korea, which is based on several robotic agents and aims at testing advanced concepts for independent living of the elderly and people with disabilities. The work focuses on technical solutions for human-friendly assistance in motion/mobility and advanced human-machine interfaces that provide simple control of all assistive robotic systems and home-installed appliances. The smart house concept includes an intelligent bed, intelligent wheelchair, and robotic hoist for effortless transfer of the user between bed and wheelchair. The design solutions comply with most of the users' requirements and suggestions collected by a special questionnaire survey of people with disabilities. The smart house responds to the user's commands as well as to the recognized intentions of the user. Various interfaces, based on hand gestures, voice, body movement, and posture, have been studied and tested. The paper describes the overall system structure and explains the design and functionality of some main system components.

Robotic smart house to assist people with movement disabilities

Some major issues of human-friendly man-machine interaction/interfaces in an intelligent residential system are dealt with to cope with increasing needs of the elderly and the handicapped for a higher level of living conditions. Specifically, the existing systems in this area are first examined and the inadequacy of some current remote-control interfaces for the elderly/handicapped is pointed out. Then, some intention-reading techniques are introduced for the elderly and/or the physically handicapped, for easier and more convenient use of machines and engineering systems.

Intention reading is essential in human-friendly interfaces for the elderly and the handicapped

In this paper, we describe the "soft remote control system" that provides easy control of home installed appliances to improve the inhabitant's comfort. This system is feasible to control various home appliances naturally by recognizing the user's hand gestures in the "intelligent sweet home". The user selects the device that he/her wants to control by pointing it with his/her hand. Then, the user can command operation of the desired function via a predefined hand gesture. The approach complements the inconvenience of the conventional remote controllers giving additional freedom to persons with movement deficits and people without disabilities.

Soft remote control system in the intelligent sweet home

The smart house under consideration is a service-integrated complex system to assist older persons and/or people with disabilities. The primary goal of the system is to achieve independent living by various robotic devices and systems. Such a system is treated as a human-in-the loop system in which humanrobot interaction takes place intensely and frequently. Ba sed on our experiences of having designed and implemented a smart house environment, called Intelligent Sweet Home (ISH), we present a framework of realizing human-friendly HRI (human-robot interaction) module with various effective techniques of computational intelligence. More specifically, we partiti on the robotic tasks of HRI module into three groups in consideration of the level of specificity, fuzzine ss or uncertainty of the context of the system, and present effective interaction method for each case. We fi rst show a task planning algorithm and its architecture to deal with well-structured tasks autonomously by a simplified set of commands of the user instead of inconvenient manual operations. To provide with capability of interacting in a human-friendly way in a fuzzy context, it is proposed that the robot should make use of human bio-signals as input of the HRI module as shown in a hand gesture recognition system, called a soft remote control system. Finally we discuss a probabilistic fuzzy rule-based life-l ong learning system, equipped with intention reading capability by learning human behavioral patterns, which is introduced as a solution in uncertain and time-varying situations.

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Intelligent Interaction For Human-Friendly Service Robot in Smart House Environment

In this paper, we propose an Advanced Soft Remote Control System so as to endow the users with the ability to control various home appliances instead of individual remote controller for each appliance and to command naturally at various places without being conscious of the view direction of the cameras. Through the developed system, the user first selects the device that he/her wants to control by pointing it with his/her hand. Then, the user can command operation of the desired functions via 10 predefined basic hand motion commands. By the experiment, we can get 97.1% recognition rate during offline test and 96.5% recognition rate during online test. The developed system complements some inconveniences of conventional remote controllers specially by giving additional freedom to persons with movement deficits and people without disabilities.

Jun-Hyeong Do

Papers

Robotic smart house to assist people with movement disabilities

Intention reading is essential in human-friendly interfaces for the elderly and the handicapped

Soft remote control system in the intelligent sweet home

Intelligent Interaction For Human-Friendly Service Robot in Smart House Environment

Advanced soft remote control system using hand gesture