Monthly Archives: June 2011

One of the most common questions we hear from mobile applications developers is, “how good are the sensors on my phone?” This article is part of a series that provides a framework to understand sensor performance. In the last blog article, we discussed methods to interpret the physical state of a mobile device based on sensor measurements. Most algorithms fundamentally rely on regular simultaneous sampling of the sensor data. For example, navigation-grade inertial measurement units often employ 100Hz sampling of all 10 axes. This creates a real-time data flow requirement for sampled sensor data. Many smartphone system architectures that do not sufficiently account for sensor data flow can degrade performance. Common problems are: Sensor interrupts are handled by the application … Continue reading →

One of the most common questions we hear from mobile applications developers is, “how good are the sensors on my phone?” This article is part of a series that provides a framework to understand sensor performance. In the last blog article, we outlined the possible variations among sensor elements in a mobile platform. However, that is merely a fraction of the complexity that faces app developers. Many sensor applications interfaces (API), including Android, do not clearly distinguish data coming from direct sensor hardware from data inferred by combing the outputs from multiple sensors, or “virtual sensors.” Outputs from sensor hardware are observations of a physical phenomenon, e.g. acceleration in meters per second squared (m/s2) or magnetic field strength in micro-tesla … Continue reading →