  The goal of this project is to create an inexpensive, lightweight, wearable
assistive device that can measure hand or finger movements accurately enough to
identify a range of hand gestures. One eventual application is to provide
assistive technology and sign language detection for the hearing impaired. My
system, called LiTe (Light-based Technology), uses optical fibers embedded into
a wristband. The wrist is an optimal place for the band since the light
propagation in the optical fibers is impacted even by the slight movements of
the tendons in the wrist when gestures are performed. The prototype
incorporates light dependent resistors to measure these light propagation
changes. When creating LiTe, I considered a variety of fiber materials, light
frequencies, and physical shapes to optimize the tendon movement detection so
that it can be accurately correlated with different gestures. I implemented and
evaluated two approaches for gesture recognition. The first uses an algorithm
that combines moving averages of sensor readings with gesture sensor reading
signatures to determine the current gesture. The second uses a neural network
trained on a labelled set of gesture readings to recognize gestures. Using the
signature-based approach, I was able to achieve a 99.8% accuracy at recognizing
distinct gestures. Using the neural network the recognition accuracy was 98.8%.
This shows that high accuracy is feasible using both approaches. The results
indicate that this novel method of using fiber optics-based sensors is a
promising first step to creating a gesture recognition system.
