A gestural human computer interface for Smart Health
Abstract
For centuries, man was forced to live a highly active lifestyle with food being a precious commodity.
Technological advances in the past few decades have resulted in increasingly sedentary lifestyles
and a surfeit of calorie dense foods. This has resulted in a global epidemic of obesity and a host of
associated health problems. One way to address this problem is to incorporate a higher level of
physical activity into the workday. The objective of this thesis is to design a low cost gestural human
computer interface for the recognition of vigorous gestures. We demonstrate that an action
vocabulary of eight intuitive gestures can be recognized by the use of inexpensive accelerometers
and a computationally simple approach involving Principal Component Analysis and Naïve Bayes
classification. The accuracy is comparable to more computationally intensive approaches. The
actions can be mapped to commands for controlling commonly used applications like e-mail and
customized to individual preferences. There is a significant rise in pulse rate during these actions
comparable to light aerobic activity. This has the potential to mitigate the harmful effects of
sedentary work habits by raising the rate of metabolism with minimal impact on productivity.
Table of Contents
Introduction -- Related work -- Model for gesture recognition -- Application -- Implementation -- Evaluation of gestural human computer interface for Smart Health -- Conclusion and future work
Degree
M.S.