Mahdy Nabaee

Date of Award


Degree Type


Degree Name

Master of Applied Science (MASc)


Electrical and Computer Engineering


Shahram Shirani


M. Jamal Deen




As the number of elderly persons as well as their fraction of the total population
continues to rise, especially in the developed countries, providing an appropriate living
environment for them using smart home technology is rapidly gaining attention. Two
important tasks of a smart home technology are monitoring the daily activities and
the vital signs of the elderly to improve their quality of life and to monitor existing
or the onset of health abnormalities. In this thesis, we focus on the monitoring of
taking medicine by the elderly person using vision sensors (low-cost cameras). This
task is important since it helps both the person and the doctor in the treatment
of illnesses of elderly persons. The allocated resources of communication bandwidth
between the sensor nodes and the computational power, used for this task, affect
the implementation cost. Therefore, it is desired to develop an effective scheme
which efficiently allocates bandwidth and computational resources to achieve a high
reliability (detection performance) at low cost.
In this thesis, we have proposed two different approaches to solve this detection
and monitoring problem. As the input data are video frames, captured by cameras
from the same scene, the frames have inter-view redundancy. Taking advantage of
this inter-view redundancy, we proposed a video coding classification scheme based
on separate encoding and joint decoding, and have obtained significant compression
improvement compared to existing techniques. In the second approach, we studied
different parts of the detection and monitoring system to find an efficient design
for distribution of different event detection parts between the nodes and the central
processing unit so that the allocated resources are reduced. In this scheme, the
useful information of the frames are extracted in the form of their main features
such that decision making based on these features is the same as decision making
based on the raw frames. As a result, we could propose a new scheme which requires
significantly less bandwidth and computational resources while achieving the same
detection performance.

McMaster University Library

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