Date of Graduation


Document Type


Degree Name

Master of Science in Computer Engineering (MSCmpE)

Degree Level



Computer Science & Computer Engineering


James P. Parkerson

Committee Member

Nilanjan Banerjee

Second Committee Member

Craig W. Thompson


Applied sciences, Data glove, Gesture recognition, Human computer interaction, Micro-harvesting, Paralysis, Sensor systems


Paralysis and motor-impairments can greatly reduce the autonomy and quality of life of a patient while presenting a major recurring cost in home-healthcare. Augmented with a non-invasive wearable sensor system and home-automation equipment, the patient can regain a level of autonomy at a fraction of the cost of home nurses. A system which utilizes sensor fusion, low-power digital components, and smartphone cellular capabilities can extend the usefulness of such a system to allow greater adaptivity for patients with various needs. This thesis develops such a system as a Bluetooth enabled glove device which communicates with a remote web server to control smart-devices within the home. The power consumption of the system is considered as a major component to allow the system to operate while requiring little maintenance, allowing for greater patient autonomy. The system is evaluated in terms of power consumption and accuracy to prove its viability as a home accessibility tool.