A golfer stands in the dreaded sand trap, carefully considering how to balance his weight as he eyes the ball. He takes a few practice swings. If he swings too deeply, he’ll hit the ground and lose another stroke. It’s a tough shot, but he swings without hesitation. Embedded in his club are microelectromechanical system (MEMS) devices—tiny machines with elements about the thickness of a human hair. These devices aren’t going to swing the club for him, but he’s been using them to analyze his swing and practice this shot. Maybe this time he’ll make it.
The wild popularity of smartphones and wearables has been driving down the cost of MEMS devices, including accelerometers, gyroscopes, magnetometers, and pressure sensors. These minuscule chips help to count steps, track calories burned, and monitor heart rate. Such data are useful, sure, but while these devices may nudge users to be more active, they don’t actually improve a swing, a punch, or a kick. To do so means moving sensors off the wrist and into sports gear—and that’s quickly happening. Indeed, you can now buy sensor-based equipment that can boost your performance, not only for golf but also for tennis, baseball, boxing, and soccer.
Measuring the often-complex motion of an athlete takes many more metrics than just tracking steps. Consider the sport of sculling: To gauge the efficiency of a rower as his oar moves through the phases of catch, drive, release, and recovery, you need to track the movement of his legs, back, and arms. If you want to analyze a baseball player as he whips the bat around, you need to consider rotational angles and swing speed.
The current boom in sensor-laden sports equipment is an outgrowth of the dramatic drop in sensor prices and the development of technology that makes it easy to integrate data from multiple sensors. In the past, synthesizing the data outputs from multiple sensors in real time was a nightmare. Figuring out how to make sense of streams of data flying into a general-purpose processor—so-called sensor fusion—presented a real hurdle to developing sports-training products that used such data, even though the need was clear.
In the past couple of years, though, a number of companies have come out with off-the-shelf technology for sensor fusion. Now, rather than develop an algorithm from scratch that measures linear acceleration, for example—which is useful in measuring the speed of a kicked or hit ball—a design engineer can use a third-party linear-acceleration algorithm to measure motion across the x, y, and z axes.
Sensor-fusion hardware and software have also taken on another tricky task: managing power consumption, which is key if you want to avoid having to frequently charge your smart balls, bats, boxing gloves, tennis rackets, or golf clubs. These systems may, for example, turn off a gyroscope, which is notoriously power hungry, when not in use. They may allocate work to the most efficient sensors, like an accelerometer or an accelerometer-magnetometer combination instead of a gyro, if it can do the job.
While both sensor manufacturers and the companies that make tools to process sensor data agree that sensor fusion is a good idea, they have tackled the implementation problem in different ways, creating systems that range from very proprietary to essentially open.
On the proprietary end of that spectrum, the makers of sensors and MEMS—such as Analog Devices, Bosch Sensortec, Freescale, InvenSense, Kionix, and STMicroelectronics—are running their own sensor-fusion algorithms on the chips they build that include multiple sensors and a microcontroller, for example Bosch Sensortec’s BN0055. Some of these manufacturers also provide application programming interfaces that can run on other hardware but work only with one company’s brand of sensors. The combo sensor-microcontroller chips allow for data batching, which means that they’ll store sensor readings in a buffer until the application processor is awake. This power-saving mechanism is a big benefit of sensor fusion.
In a more flexible approach, Hillcrest Labs, PNI Sensor, and QuickLogic have developed stand-alone sensor-fusion coprocessors that support chips from a variety of sensor makers. These hardware-software systems poll the sensors for data, push it to their own low-power coprocessors, and manage the data using proprietary algorithms.
Finally, my organization, MEMS & Sensors Industry Group, has rolled out an open-source option that can help designers jump-start development of systems that require the use of sensor fusion. Our Accelerated Innovation Community, which began in late 2014, makes some of the most popular sensor-fusion algorithms available for free to design engineers.
With cheap sensors and simple sensor fusion on the market, the current wave of smart sports gear is not likely to crest anytime soon.
Sensors have already been inserted into just about every type of sports equipment that can be swung, punched, or worn, and more are in development. Noel C. Perkins of the University of Michigan and William Clark of the University of Pittsburgh developed one recent example, a smart baseball bat add-on. The prototype is a small, sensor-laden circuit board affixed to the knob end of a bat. It contains MEMS chipsets that measure the three components of the bat’s acceleration as well as rotation rates around three orthogonal axes. A smartphone collects the data and analyzes the swing time, speed, control, and other factors that relate to a successful hit.
The University of Michigan has licensed the technology to Clark’s new company, Diamond Kinetics, which in 2014 rolled out its first commercial product, SwingTracker, a lightweight sensing accessory that attaches temporarily to the knob of the bat. The technology, which Perkins originally developed to improve his fly fishing, can and likely will be applied to other sports.
Sensors in athletic clothing, already in limited use by elite athletes, will soon become accessible to ordinary consumers. These can be used to analyze body motion for multiple sports. Such wearable gear is still in the nascent stages of evolution. But more than one startup has already begun to sell sensor-equipped workout clothes. California-based MAD Apparel, for example, claims that its US $400 Athos Upper Body Package analyzes in real time how hard an athlete’s muscles are working by using a shirt with 14 embedded biosensors and a hardware “core” positioned over the heart.
Another maker of smart clothing is the Dutch company Xsens Technologies, which Fairchild Semiconductor purchased in April 2014. Xsens is adapting its full-body motion-tracking suits (used to make animated movies and video games) to sports applications. Independent researchers are using Xsens’s bodysuits for such sports as ski jumping, shot put, archery, baseball pitching, rowing, and even wingsuit flight.
And, in another sign that smart clothing is moving toward the mass market, Under Armour chief digital officer Robin Thurston has said his company is developing smart clothing and expects to have commercial products within the next two to three years.
For many sports, the remaining challenge lies not in collecting data but in making that information accessible and meaningful. Both professional athletes and casual sports enthusiasts want to ensure that they are maximizing the effects of their time at the gym. That’s coming.
Within a few years, our MEMS-enabled golfer should have plenty of help for his game: His smart eyewear will give him a hole-by-hole analysis of the course and will record and instantly play back each shot while MEMS motion sensors in his shoes will help him position his feet. His clothes will have sensors sewn into the fabric so he can adjust his hips and legs appropriately. He’ll swing his MEMS-based golf club to strike a MEMS-enabled golf ball, connecting just right. The ball will land on the green, transmitting its short distance to the hole immediately to his smartphone. At long last, he’ll really enjoy the sport of golf.
About the Author
Karen Lightman is executive director of MEMS & Sensors Industry Group. She began exploring microelectromechanical systems in sports a few years ago when her daughter sustained a serious concussion while skiing. Lightman realized that a more advanced ski helmet, with built-in MEMS-based concussion-detection technology, could have provided potentially valuable information to her daughter’s medical team. She also suggests that sensors in ski-lift passes might help detect dangerous skiers and prevent accidents.
This article appears in the March 2016 print issue as “Silicon Gets Sporty.”