الفهرس 
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Abstract
This thesis is concerned with the problem of monitoring and tracking human activities.
Real-time recognition and monitoring of the human daily activities attracted lots
of attention from researchers due to its crucial role in emerging elds such as pervasive
healthcare and ambient assisted living. However, despite being an active eld, recent
benchmarks show a diculty in providing robust monitoring of the di erent various
activities under realistic everyday life conditions.
Through this thesis, we present the ubiMonitor system as an accurate real-time
activity monitor using low-cost o -the-shelf three body-worn 3D accelerometers. For a
robust monitoring, ubiMonitor (1) intelligently fuses the accelerometers and the acceleration
in each of the three axes to provide key discriminative features for the di erent
activities based on their physical characteristics, (2) employs a novel hierarchical activity
recognition scheme, and (3) applies an e ective temporal- and context-based
postprocessing stage to remove falsely detected activities and enhance the overall system
accuracy .
Experimental results using real traces from di erent subjects show that ubiMonitor
can achieve an overall accuracy more than 95% with a median latency less than 3
msec. This is better than state-of-the-art by 23.4% in the recognition accuracy with a
reduction of 70% in the sensors used.