We present the design and implementation of the Conductor's Jacket, a unique wearable device that measures physiological and gestural signals, together with the Gesture Construction, a musical software system that interprets these signals and applies them expressively in a musical context. Sixteen sensors have been incorporated into the Conductor's Jacket in such a way as to not encumber or interfere with the gestures of a working orchestra conductor. The Conductor's Jacket system gathers up to sixteen data channels reliably at rates of 3 kHz per channel, and also provides real-time graphical feedback. Unlike many gesture-sensing systems it not only gathers positional and accelerational data but also senses muscle tension from several locations on each arm. The Conductor's Jacket was used to gather conducting data from six subjects, three professional conductors and three students, during twelve hours of rehearsals and performances. Analyses of the data yielded thirty-five significant features that seem to reflect intuitive and natural gestural tendencies, including context-based hand switching, anticipatory 'flatlining' effects, and correlations between respiration and phrasing. The results indicate that muscle tension and respiration signals reflect several significant and expressive characteristics of a conductor's gestures. From these results we present nine hypotheses about human musical expression, including ideas about efficiency, intentionality, polyphony, signal-to-noise ratios, and musical flow state. Finally, this thesis describes the Gesture Construction, a musical software system that analyzes and performs music in real-time based on the performer's gestures and breathing signals. A bank of software filters extracts several of the features that were found in the conductor study, including beat intensities and the alternation between arms. These features are then used to generate real-time expressive effects by shaping the beats, tempos, articulations, dynamics, and note lengths in a musical score.