Hamon: An Activity Recognition Framework for Health Monitoring Support in Home Environments

• Main Author: Alhamid, Mohammed
• Format: Others
• Language: en
• Published: University of Ottawa (Canada) 2013
• Subjects: Computer Science.
• Online Access: http://hdl.handle.net/10393/28704; http://dx.doi.org/10.20381/ruor-19397

Nowadays, technology advances accelerate the quality and type of services provided for health care and especially for monitoring health conditions. Sensors have become more powerful to sense different physiological signs and have the ability to be worn on the human body using wireless communication modules. A variety of software tools have been developed to help in processing a variance list of vital signs by analyzing and visualizing data generated by multiple sensors. In this thesis, we introduced a Health signs and Activity recognition MONitoring framework (Hamon). Hamon, of German origin meaning a home protector, is designed to be an enabling prototype for health monitoring applications. Using off-the-shelf sensors, we implemented an activity detection framework for detecting five types of activity: falling, lying down, sitting, standing, and walking. The framework collects and analyzes sensory data in real-time, and provides different feedback to the users. In addition, it can generate alerts based on the detected events and store the data collected to a medical sever. Context information such as the weather condition, the type of activity, and physiological data collected such as the heart rate, is also integrated in the framework. A number of challenges have been addressed in this thesis including: the design and the implementation of the framework, the selection and the implementation of the activity recognition algorithm, and the classification method used for detection.