Finger gesture recognition using glove-like interfaces are very accurate for sensing individual finger positions by employing a gamut of sensors. However, for the same reason, they are also very costly, cumbersome and unaesthetic for use in artistic scenarios such as gesture based music composition platforms like Virginia Tech's Linux Laptop Orchestra. Wearable computing has shown promising results in increasing portability as well as enhancing proprioceptive perception of the wearers' body. In this thesis, we present the proof-of-concept for designing a novel muscle-machine interface for interpreting human thumb motion as a 2-dimensional joystick employing mechanomyographic signals. Infrared camera based systems such as Microsoft Digits and ultrasound sensor based systems such as Chirp Microsystems' Chirp gesture recognizers are elegant solutions, but have line-of-sight sensing limitations. Here, we present a low-cost and wearable joystick designed as a wristband which captures muscle sounds, also called mechanomyographic signals. The interface learns from user's thumb gestures and finally interprets these motions as one of the four kinds of thumb movements. We obtained an overall classification accuracy of 81.5% for all motions and 90.5% on a modified metric. Results obtained from the user study indicate that mechanomyography based wearable thumb-joystick is a feasible design idea worthy of further study. / Master of Science