ARM and Sensor Platforms deliver an open source framework for sensor devices
Cambridge, UK – ARM® and Sensor Platforms Inc. are extending their collaboration to the Open Sensor Platform (OSP) to simplify development of embedded sensor-based products utilising ARM architecture. Contributions to the OSP framework will enable ARM CMSIS, the ARM RTX RTOS, and compatibility with the ARM mbed™ SDK and mbed platforms.
OSP provides a framework for the deployment of sensor fusion hubs for ARM-based solutions in mobile computing, wearables and IoT devices. This allows developers to rapidly create intelligent products enabled by standards-based software and hardware which are easy for developers to deploy and manage. The benefits can be summarised as follows:
• Sensor manufacturers: the ability to quickly integrate their sensors into new products, and to demonstrate their capability to prospective developers or OEMs
• Sensor Hub MCU vendors: gain access to a complete sensor ecosystem
• Sensor Fusion developers: availability to existing open source algorithms; can modify or create their own algorithms or buy commercial third-party libraries such as the FreeMotion™ Library (available from Sensor Platforms, Inc.)
• OEMs: the ability to quickly evaluate different sensors, sensor hubs, and sensor algorithms which will allow them to both develop faster and differentiate products.
“Open source initiatives are critical to accelerating innovation in the embedded market,” said Willard Tu, Director of Embedded Solutions Marketing, ARM. “The ecosystem building around ARM’s mbed platform is one of our efforts to spur entrepreneurial efforts and we expect our contributions to OSP to have a similar, galvanising effect in the developer community.”
“OSP will unlock a whole new generation of sensor innovation, as developers can now focus on building great devices instead of building interfaces,” said Kevin Shaw, Chief Technology Officer, Sensor Platforms, Inc. “OSP allows the software and algorithms to be abstracted from the underlying architecture. With this, we expect to see a growing need for best-in-class algorithms providing amazing contextual capabilities.”