Sequential Scoring Algorithms and Sequential User Input Optimization
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Eng, Nathan
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This thesis proposal explores the impact of passive haptic feedback gloves on piano skill acquisition. These passive haptic feedback gloves operate by providing haptic feedback corresponding to fingerings in a music piece, with the goal of establishing a form of “muscle memory”. User performance is evaluated and scored using the Needleman-Wunsch (NW) algorithm, originally designed for DNA sequence alignment. The algorithm compares user-generated note sequences against the intended sequence of notes, allowing for assessment of proficiency. Additionally, the paper addresses challenges related to software performance in recording user input and investigates optimizations to ensure accurate scoring. Preliminary results indicate a 15% improvement in piano skill acquisition with the use of passive haptic learning gloves. The study further discusses potential confounds such as participant practice habits and software bugs, along with recommendations for future research. Overall, the findings suggest that passive haptic learning gloves have the potential to positively impact piano learning rates, pending further refinement and optimization.
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Undergraduate Research Option Thesis