In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.

A Survey on Ambient-Assisted Living Tools for Older Adults

Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

Sensor-Based Activity Recognition

A review of smart homes-Present state and future challenges

http://www.eis.mdx.ac.uk/staffpages/juanaugusto/CookAJ2009.pdf

Review: Ambient intelligence: Technologies, applications, and opportunities

A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its annotation is described and made available to the community. Through a number of experiments we show how the hidden Markov model and conditional random fields perform in recognizing activities. We achieve a timeslice accuracy of 95.6% and a class accuracy of 79.4%.

Accurate activity recognition in a home setting

Research groups in both academia and industry have developed prototype systems to demonstrate the benefits of pervasive computing in various application domains. Unfortunately, many first-generation pervasive computing systems lack the ability to evolve as new technologies emerge or as an application domain matures. To address this limitation, the University of Florida's Mobile and Pervasive Computing Laboratory is developing programmable pervasive spaces in which a smart space exists as both a runtime environment and a software library. Service discovery and gateway protocols automatically integrate system components using generic middleware that maintains a service definition for each sensor and actuator in the space. The Gator Tech Smart House in Gainesville, Florida, is the culmination of more than five years of research in pervasive and mobile computing. The project's goal is to create assistive environments such as homes that can sense themselves and their residents and enact mappings between the physical world and remote monitoring and intervention services.

The Gator Tech Smart House: a programmable pervasive space

The Pervasive Computing Laboratory at the University of Florida is dedicated to creating smart environments and assistants to enable elderly persons to live a longer and a more independent life at home. By achieving this goal, technology will increase the chances of successful aging despite an ailing health care system (e.g. Medicaid). One of the essential services required to maximize the intelligence of a smart environment is an indoor precision tracking system. Such system allows the smart home to make proactive decisions to better serve its occupants by enabling context-awareness instead of being solely reactive to their commands. This paper presents our hands-on experience and lessons learnt from our first phase work to build up a smart home infrastructure for the elderly. We review location tracking technology and describe the rationale behind our choice of the emerging ultrasonic sensor technology. We give an overview of the House of Matilda (an in-laboratory mock up house) and describe our design of a precision in-door tracking system. We also describe an OSGi-based robust framework that abstracts the ultrasonic technology into a standard service to enable the creation of tracking based applications by third party, and to facilitate the collaboration among various devices and other OSGi services. Finally, we describe three pervasive computing applications that use the location-tracking system which we have implemented in Matilda's house.

Enabling location-aware pervasive computing applications for the elderly

A method of remote surveillance and assisted care using a mobile communication device can include determining a location of a user. The method can also include adjusting at least one camera according to the location, and capturing an image of an area around the location. Further, the method can include transmitting the image to a remote information processing system.

Remote surveillance and assisted care using a mobile communication device

The use of pervasive computing technology such as java smart phones and multi-modal sensors in smart homes of the future can potentially enhance elders’ independence and quality of life. We present ongoing research projects whose goal is to reduce the demand on elder’s attention and effort while performing daily tasks. We present three applications: a Mobile Patient Care-Giving Assistant (mPCA), a General Reminder System (GRS), and an Augmented Awareness System (AAS). The mPCA application is a cognitive assistant designed to improve the independence of liveat-home for Alzheimer Disease (AD) patients. GRS is a reminder application targetted to elders with dementia. AAS is a notification application that boosts the elder awareness about certain events in the surrounding (mail delivery, water leak, etc.) We present these applications and discuss the OSGi-based framework on which these applications are built.

Youssef Kaddoura

Papers

The Gator Tech Smart House: a programmable pervasive space

Enabling location-aware pervasive computing applications for the elderly

Remote surveillance and assisted care using a mobile communication device

Smart Phone Based Cognitive Assistant

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