Keeping that premise as the company mission, Sensor Platforms rolled out on March 26th a library of software algorithms and middleware designed, according to company claims, “to interpret users’ contexts and intents” by using data from multiple sensors in mobile devices.
Commonly found MEMS sensors in today’s smart phones and tablets include accelerometers, magnetometers, gyroscopes and barometers.
In keeping with the trend for continuous integration where possible the idea of integrating multiple MEMS sensors is attractive. However, one unanswered question confronting today’s MEMS component suppliers and system designers is: “Who will determine the sensor architecture, where the processing will reside and the motherboard-level sensor fusion architecture?”
While some companies may integrate all these sensors in a single monolithic device and wrap everything in smart components (although not an easy feat), Sensor Platforms has chosen another approach: offer sensor fusion in software that’s hardware agnostic.
The company says that its single code-base software can be used across platforms. It can be run in its entirety on an apps processor, on a sensor hub, or spread over the system.
That gives one clear advantage to Sensor Platforms’ software-based sensor fusion, said Chen: flexibility. “Our software allows system designers to pick and choose different supply sources for each sensor. The flexibility in sourcing is critical since these sensors come at different price and performance points.”
Another advantage of Sensor Platforms’ software lies in the conservation of sensor power, according to the company.
Throwing more sensors into a mobile device is one thing. But how to minimize the associated power consumption is another.
Chen noted, “Up to 10mW is added in power consumption when sensors are in use.” Sensor Platforms’ software library, called “FreeMotion Library,” comes with a proprietary algorithm that can “turn off power hungry sensors, like the gyroscope, and emulate its function with lower power sensors when user movements are slow.” That translates into “dropping sensor power consumption by 90 percent,” he