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Running is one of the most popular sports with hundreds of millions of participants worldwide. Good running form is the key to fast, efficient, and injury-free running. Existing kinematic analysis technologies, such as high-speed camera systems, are expensive, difficult to operate, and exclusive to sports physiology laboratories and elite athletes. Miniature MEMS-based motion sensors enable portable high-precision kinematic analysis, but suffer from high energy consumption hence short battery lifetime, especially for continued online analysis for running. This paper presents Gazelle, a wearable online analysis system for running that is compact, lightweight, accurate, and highly energy efficient; intended for runners of all levels. To enable long-term maintenance-free mobile analysis for running, Sparse Adaptive Sensing (SAS) is proposed, which selectively identifies the best sampling points to maintain high accuracy while greatly reducing sensing and analysis energy overheads. Experimental results demonstrate 97.7 percent accuracy with 76.9 to 99 percent reduced energy consumption (83.6 percent average reduction under real-world testing)-a one-order-of-magnitude improvement over existing solutions. SAS enables > 200 days of continuous high-precision operation using only a coin-cell battery. Since 2014, Gazelle has been used by over 100 elite and recreational runners during daily training and at top-level races like the Kona Ironman World Championships and New York Marathon.
Liu et al. (Mon,) studied this question.
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