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.

Wearables are a leading category in the Internet of Things. Compared with mainstream mobile phones, wearables target one order of magnitude form factor reduction, and offer the potential of providing ubiquitous, personalized services to end users. Aggressive reduction in size imposes serious limits on battery capacity. Wearables are equipped with a range of sensors, such as accelerometers and gyroscopes. Most economical sensors were developed for mobile phones, with energy consumptions more appropriate for phones than for ultra-compact wearables. This article describes the energy challenges for wearable sensing technologies, with a primary focus on the most widely used wearable sensors: MEMS-based inertial measurement units. Using sports and fitness wearables as the pilot application, we analyze the energy characteristics of MEMS IMU data sensing, analysis, and wireless communication. We then discuss the technologies needed to solve the power and energy consumptions challenges for wearables.