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An Ontology-Based Hybrid Approach to Activity
Modeling for Smart Homes
Abstract
Activity models play a critical role for activity recognition and assistance in
ambient assisted living. Existing approaches to activity modeling suffer from a
number of problems, e.g., model reusability, and incompleteness. In an effort to
address these problems, we introduce an ontology-based hybrid approach to
activity modeling that combines domain knowledge basedmodel specification and
data-driven model learning. Central to the approach is an iterative process that
begins with “seed” activity models created by ontological engineering. The “seed”
models are deployed, and subsequently evolved through incremental activity
discovery and model update. While our previous work has detailed ontological
activity modeling and activity recognition, this paper focuses on the systematic
hybrid approach and associated methods and inference rules for learning new
activities and user activity profiles. The approach has been implemented in a
feature rich assistive living system. Analysis of the experiments conducted has
been undertaken in an effort to test and evaluate the activity learning algorithms
and associated mechanisms.

2. System Analysis
Existing System:
The Existing system is data driven approach, the drawback of this system is
related to model applicability and reusability. A data-driven approach is sensitive
to unseen data which makes it difficult to apply the ADL models which have been
learnt from one person to another person. This means that with a data-driven
approach, an activity model for different users has to be learnt separately. The third
drawback is the incompleteness of activity models which is closely related to the
aforementioned two issues. With the data-driven approach, every activity model
for all the ADLs for every user needs to be learnt in order to create complete ADL
models. Given the large number of ADLs this is a huge challenge, if indeed not
impossible, in practice. To mitigate the aforementioned problems, researchers have
recently started applying transfer learning techniques to activity modeling and
recognition by reusing resources and knowledge. This involves transferring the
source datasets, or features or models, from one user to another in different
settings. An alternative to the data-driven approach is to manually define activity
models by making use of rich, prior knowledge, and domain heuristics.
Proposed System:
This approach is motivated by the observation that most ADLs are daily
routines which normally take place within a specific circumstance of time,
location, and space with relatively fixed types of objects. Using formal knowledge
acquisition and modeling technologies, activity models can be created by means of
various knowledge modeling tools. As this approach is closely related to

3. knowledge engineering, it is referred to as a knowledge driven approach. A
knowledge-driven approach improves model reusability by modeling activities at
multiple levels of abstraction to create both generalized and specialized ADL
models. For example, ontological activity modeling can model a generic ADL as
an ontological activity class and an individual-specific ADL as an instance of the
corresponding activity class. ADL modeling is not a one-off effort, instead, a
multiphase iterative process that interleaves knowledge-based model specifications
and data-driven model learning. The process consists of two key phases.
Ontological activity modeling creates activity models at two levels of
abstractions, namely as ontological activity concepts and their instances,
respectively. Ontological activity concepts represent generic coarse-grained
activity models applicable and reusable for all users, thus solving the reusability
problem. The seed ADL models are then used in applications for activity
recognition at the second phase. In the third phase, the activity classification results
from the second phase are analyzed to discover new activities and user profiles.
These learnt activity patterns are in turn used to update the ADL models, thus
solving the incompleteness problem. Once the first phase completes, the remaining
two-phase process can be iterated many times to incrementally evolve the ADL
models, leading to complete, accurate, and up-to-date ADL models. This paper
makes three main contributions. First, we develop a hybrid approach to activity
modeling that combines the strengths of data- and knowledge-driven approaches to
support an incremental modeling process. The approach is built upon the work in
however, extends it by incorporating the learning capabilities to provide a viable
solution for addressing existing problems relating to ADL modeling. Second, we
develop a learning method to discover activities that are performed by users but
have not yet been modeled. Third, we define the characteristics of a user activity
profile and develop analysis methods and associated inference rules to learn a

4. user’s activity profiles, i.e., the specific way the user performs activities. The
learning methods of activity profiles can detect the changing manner an activity is
performed, thus allowing ADL models to adapt over time. We have implemented
the approach in a feature-rich assistive living system.
Modules:
1. Activity Module.
2. Categories.
3. Carer module.
4. User Module.
5. Reports Module.
Module description:
1. Activity Module:
In the Activity module, the user can register the User Activities,after
registering the activity the e-mail will go to the respected carer along with the
registered activity details.In “An Ontology-Based Hybrid Approach to Activity
Modelling for Smart Homes” project , user can reuse the already created
activities which are created by other users and his activities also.
This module is accessed by all the users but for carer registering the
activity functionality is restricted, carer can see only the activities which are
assigned to him Admin/Resident, Relative can use this module.
2. Categories module:

5. This module is accessible to only for Admin/Resident.Using this module
they can register the Activity Types and Sub-Categories in Each activity type.
For an example:
Activity Type:Cooking
Sub-Categories are : Breakfast,Lunch,Snacks,Supper…..
3. Carers module:
This module is accessed by Admin/Resident,Carer .Using this
module admin/resident only having the permissions to add/edit/delete the
carer.where as coming to carer role they can update the details they don’t have
the permissions to add/delete the carer.
4. Users Module:
This module is accessible to admin/Resident only, the other roles don’t have
the permission to access this module using this module the admin can register
the users.
If the admin wants to register the carer user details then the cares should be
avavliable in the carer module(should register under carer module).
5. Reports Module:
Reports module is accessible to admin/Resident roles only,Using this module
,the admin can perform the search on all created useractivities and re-used
activities and based on the search they can generate the reports to excel.They can
do the search by carer,by category,between the dates.Dates is the mandatory fields.

6. Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 256 Mb.
Software Requirements:
• Operating system : - Windows 7.
• Front End : - Visual Studio 2008sp1.
• Coding Language : - Asp.net.
• Backend-database :- SQL Server 2008