An Ontology Based Smart Home Lab Environment

An Ontology BasedSmart Home Lab Environment Antonio Sanchez November 2009

Eldercare Scenario:Smart HomesTechnological Framework Sensors Decision Module Actuators

Definitions “A smart home can be defined as a residence equipped with computing and information technology which anticipates and responds to the needs of the occupants, working to promote comfort, convenience and security and entertainment through the management of technology within the home and connections to the world beyond” (Francis Aldrich,2003) “We define a smart home environment as one that is able to acquire and apply knowledge about the environment and its inhabitants in order to improve their experience in that environment” (Diane Cook, 2007)

Application: Eldercare • Over 36 million seniors over the age of 65 in the US today • Over 10 million live alone • Fastest growing demographic group in the U.S. • The vast majority (95%) of seniors want to “age in place” • 1.2 billion older people projected worldwide in the year 2025 • Considering the technology available already, most disabled and elders would like to live as independent as possible.

Eldercare Smart home environments for home and residential care High Home care: Independent, external patient Quality of Life Residential care: Assisted living, Nursing facilities Emergency care: Hospitalized, Intensive Care Low $1 $ 10 $100 $1,000 $10,000 Cost/day Source: Paul Timmers “Aging well and independent living with the help of IT”

Smart Homes: Opportunities Information Commercial Applications Sensors Networks Communication Actuators Knowledge Social Aspects • Research Pattern recognition Data mining Ontologies Architectural Ergonomics Databases Multi Agents Technology AI

Automation and AI And so the automation in a smart environment may be viewed as a continuous cybernetic feedback loopwith four main components Sensors: Perceiving the state of the environment by means of multiple sensors Ontology: Reasoning about the state together with task goals and outcomes of possible actions using a defined ontology Actuators: Acting upon the environment to change the state by means of a set of simple but precise and accurate actuators Ambient Interfaces: Taking a broader view of a GUI by making use of the entire physical environment as an interface to digital information.

Automation in real environments The design and effective use of physical components such as sensors, controllers, and smart devices is vital. Smart environment research is conducted in real-world, physical environments Without these physical components, we end up with theoretical algorithms that have no practical use Like all intelligent agents, a smart environment relies on sensory data from the real world.

Ontologies • Smart technologies, software agent software and and middleware technologies have led to the emergence of pervasive or ubiquitous computing • In such environments, hardware and software elements should function autonomously and link and apply knowledge effectively with all the components of the smart home. • An ontology describes concepts and relationships that are important to a domain. • An ontology is formal and explicit specification of a shared conceptualization • In the absence of well-accepted standards for the smart home domain, it is up to the system developers to define the ontology. • In the present work, we use the ELDeR ontology

Source: Saldaña-Jimenez D. Rodríguez, M. D. Espinoza, A.N, García–Vázquez, J.P, “A Context-Aware Component for Identifying Risks Associated to Elder’s Activities of Daily Living”, In IEEE Proceedings PERVASENSE ’09, London, UK

From the tool to the application:MVC and Java Controller View

From the tool to the application:MVC and Java Controller View Model

SDBI: Sunspots in an Ontology driven system • In an elder care application scenario, a smart home should respond to the needs and disabilities of elderly inhabitants to prevent injury or death. • Focus on the safety monitoring components for which the system needs to sense the environment, determine risk, take some immediate actions, and notify caregivers. • Sensor Information Acquisition • Sensor Communication • Sensor Information Fusion • Decision Model and Ontology • Actuator Controllers

SDBI: Design MVC (Model View Controller) programming architecture SDBI is a good tool for teaching programming. The Ontology was developed in Protégé and the rules in Jess and they are given to the system as an external Jar file This system was developed during this summer by two graduate students

GUI Functionality Selecting a Choice triggers a Sun SPOTS interaction with the required sensor simulation Sensors: Sun SPOTS

GUI Functionality Selecting the Read or View button display the ontology file. The Test Ontology button test the ontology. The Start Environment Surveillance reads all the parameters and makes decisions Ontology and rules

GUI Functionality Selecting a Choice triggers an actuator interaction with the required action simulation. Actuators: Email, Firecracker, Camera

SDBI:Ontology dataflow • Using ontology module SDBI receives the data of the sensors and makes the relation between the sensor: • Temperature increment, • Gas • Smoke • Motion Detection • and the required actions based on its rules

SDBI: Basic Rules

SDBI: GUI with S/O/A consoles The objects related sensors report in the console their communication flow with the Sunspots The ontology and rule base system report the triggered rules Actuator performance is reported in this console

SDBI:X10 & Email services Besides the starting X 10 services emails are sent to caregivers with images of the event

Smart Home: Ubiquitous Computing I will need your password if you want toasted bread “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it” Mac Weiser, November 1991 Thank for your attention Questions?

References Burnell-Ball, L. Sanchez, A. Priest, J. Hannon, C. “The Crescent Lab: A smart home lab for students” in ENC’06 Seventh Mexican International Conference on Computer Science, SLP, Mexico September, 2006. IEEE Computer Society Alamitos, Ca. Denkowsky M, Hannon C., Sanchez A. “Spoken commands in a Smart Home: An iterative approach to the Sphinx Algorithm” in MICAI 2007: Advances in Artificial Intelligence Alexander Gelbukh, Angel Kuri (Eds) Lecture Notes in AI Springer-Verlag Volume 4827, 2007 Sanchez, A. Hannon C. Garcia J.P, Garcia C. Ceballos H & Cetina, O, Service Robots on a Smart Home Lab” in Workshop on ServiceRobots of the 7th MICAI, Mexico, November 2008. Sanchez, A. Tercero, R. Saldaña D, Burnell-Ball L “SDBI:An Ontology Based Smart Home Lab Environment” to be presented at the WORKSHOP ON INTELLIGENT LEARNING ENVIRONMENTS of MICAI 2009, Mexico Novemeber 2009

Eldercare Scenario Linda is 78 years old. She tends to forget things such as to leave food in the stove. One day she was cooking dinner and she heard a noise in the backyard, so she went away leaving the pot in the stove for long time.

Eldercare Scenario Linda is 78 years old. She tends to forget things such as to leave food in the stove. One day she was cooking dinner and she heard a noise in the backyard, so she went away leaving the pot in the stove for long time. She went away for a long time and forgot abut the food in the stove. Later on that stared a fire

Eldercare Scenario Fortunally, the house has a system which sensed the smoke and the temperature. After the system sensed the conditions, it took corrective actions such as: Turning on the alarm Turning on a fire extinguhisher Sending a message to her caregiver This is the scenario we want to address today

Actuator Driver:Using X 10 with FireCracker • X10 is a well known 1975 open industry standard for communication electronic using power line wiring to control any type of appliances. • X- 10 interfaces have the advantage of inexpensive pricing and ready availability, however they are often hampered by noisy signals and long delays. Wireless communication is achieved using a simple FirecrakerTM .

Wireless Sensors Here are some of the measurements we can get from available sensors in the market The key is to access that information within a reliable network In 2007 Sun came out with a simple device to do so Source: D. Cook and S. Das (ed) Smart Environments J Wiley 2004

SunSPOTS: Connecting Sensors • Simple embedded platform for development of radio-controlled sensor networks, robotics, and smart home electronics • Device Drivers are programmed in Java • Wireless Communication • Overlay Network - CTP, IPv6/LowPan • Mesh Networking - AODV, LQRP • Mobile • Built in Lithium Ion battery • with USB charger • Aware and Active • Able to sense and • affect surroundings • Secure • Built-in high grade • ECC public key cryptography

Summer 2009 Goals Create the required communication between the sensors controlled by SunSPOTS and a central computer system Apply the ELDeR ontology to drive the a prototype loop for sensors/actuators Create a knowlodge base for diferents risks Sends notification to the actuators according to sensors information

Development Framework Design an MVC model Programming done in Java Interface SunSPOTS using NetBeans Use OWL, Protégé and JESS to integrate the ontology Use the room cameras to send email message of the given resulting events

An Ontology Based Smart Home Lab Environment