Page 34 - MSDN Magazine, April 2018

MACHINE LEARNING
Sensors in Sports:
Analyzing Human
Movement with AI
Kevin Ashley, Patty Ryan and Olga Vigdorovich
In the future, athletes will likely be able to open their phones and ask a simple question: “What do I need to do to improve my skills?” We’re still making early steps in sports AI toward answer- ing that fundamental question, but we hope that the productivity Microsoft tools and research are bringing will one day make this an everyday scenario. With many sports, it’s difficult for the human eye to observe all the movements an athlete might make during the course of an activity, but it’s possible to record even unobserv- able data with sensors. And by using machine learning (ML) on this data, the athlete and coach can learn and improve based on precise measurements and analytics. The instrumented athlete is becoming the new competitive advantage.
If the current trend continues, in a few years most sports equip- ment sold in stores will have a smart sensor embedded. Electronics are becoming smaller, lighter and more flexible, and it’s likely we’ll see them embedded in fabrics, shoes, skis, tennis racquets and other types of smart gear. You’ll be able to determine how to apply technology and skills learned in Internet of Things (IoT), mobile apps, Microsoft Azure and ML to sports.
To make adopting this technology easier, we’ve created an open source Sensor Kit, with components to process, measure, analyze and improve sensor measurements of athletic performance. Over time, our aim is to evolve this community Sensor Kit together with electronics and sports equipment companies, and sports associ- ations and enthusiasts. The Sensor Kit and all examples for this article are available at bit.ly/2CmQhzq. This includes code samples for R, Python, C#, Xamarin and Azure Cosmos DB. Figure 1 shows the Winter Sports mobile app, which showcases the use of Sensor Kit and is available for download at winter-sports.co.
The recent dramatic increase in compute power, reliability and affordability of sensor-equipped hardware has made many sce- narios newly viable. And advances in applications of AI on sensor signals produced by athletes deliver new ways to understand and improve athletic performance. For example, an athlete’s sensor sig- nals provide interpretable “activity signatures,” as shown in Figure 2, which allow sports analytics to go beyond gross activity tracking and aggregates, and to measure key elements of a skill or activity. From acceleration generated through a specific turn, to directional
This article discusses:
• Using sensors to collect athlete data
• Using the Sensor Kit to connect sensors with mobile apps and
Azure Cosmos DB
• Processing sensor data with statistical tools, such as R, to extract
activity signatures
• Calculating an athlete’s load from g-forces using Python
Technologies discussed:
Azure Cosmos DB, R, Python, C#, Sensor Hardware
Code download available at:
bit.ly/2CmQhzq
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