Discovering Interpretable Muscle Activation Patterns with the Temporal Data Mining Method

Abstract

The understanding of complex muscle coordination is an important goal in human movement science. There are numerous applications in medicine, sports, and robotics. The coordination process can be studied by observing complex, often cyclic movements, which are dynamically repeated in an almost identical manner. In this paper we demonstrate how interpretable temporal patterns can be discovered within raw EMG measurements collected from tests in professional In-Line Speed Skating. We show how the Temporal Data Mining Method, a general framework to discover knowledge in multivariate time series, can be used to extract such temporal patterns. This representation of complex muscle coordination opens up new possibilities to optimize, manipulate, or imitate the movements.

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