Mining Muscle Use Data for Fatigue Reduction in IndyCar

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This paper discusses how data analytics can be conducted on muscle usage in extreme racing conditions to find actionable insights for the driver. One of the important insights is how to minimize driver’s muscle fatigue during a race, because IndyCar has regulations forbidding the use of power steering. This paper tackles two technological challenges: 1. data validation on noisy signal obtained from wearable device in extreme condition, 2. data cultivation to find actionable insights for the driver from heterogeneous racing data. First, we propose a data quality assessment technique based on supervised learning, enabling the judgment of whether data is reliable or not. This qualitative analysis revealed that the data validation method works with 99.5% accuracy to classify data as reliable or not. We also provide the real-time wearable monitoring tools to remind driver of wearable’s detachment from the driver’s body. Second, using the clean data classified by the first step, we propose a data visualization tool based on unsupervised learning that enables the driver or mechanics to discover useful feedback. We identified and demonstrated several actionable insights, such as identifying potential improvement points or potential relaxation points.

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