The growing wearable, smart home, and IoT design communities would like to reliably detect, interpret, and understand their products’ motion to captivate users in novel ways. Understanding the motion and context of a user is key to recognizing behavior, predicting intentions, and ultimately improving quality of life.
Interpreting motion is hard. Help, however, is on the way through smarter ASSNs — Application Specific Sensor Nodes — such as Bosch Sensortec’s BNO055. In order to better understand their value, let’s first retrace the steps that have taken us to where we are today.
The consumer electronic developments of the 21st century have, among other things, brought high-volume and low-cost manufacturing of motion sensors, which spurred novel ways to interact with our portable devices. This consequently increased the level of engagement and lowered entry barriers for users. Today it’s not uncommon to find adults (and children) intuitively rotating the screen of a smartphone to view a photo in portrait or landscape modes or using 3D remote controls to interact with their smart TVs through hand-waving gestures.
The digitization of analog motion occurs through accelerometers, magnetometers, and gyroscopes, each collecting a portion of the motion in three axes. The accelerometer is primarily used to determine the direction of movement in relation to the Earth’s gravitational pull. The magnetometer is able to detect the Earth’s magnetic north, and the gyroscope measures the device's speed of rotation.
These sensors can, however, be fooled by external factors during everyday use. Lateral acceleration and vibration will introduce errors in calculating the gravity vector. Magnetometers will tend to follow stronger magnetic fields coming, for example, from motors and loudspeakers, that are orders of magnitude stronger than the Earth’s magnetic field. A gyroscope's own inertia (caused by inherent offsets) can result in a measured rotation even when the device is completely still.
Therefore, “sensor fusion” algorithms have been developed to mitigate and eliminate these undesired behaviors. Such algorithms are extremely complex and must be customized to the chosen motion sensors in order to be effective. Developers or “makers,” however, prefer to develop the end-use case, such as an interface device, a wellness/fitness tracker, or a cloud-based app, rather than deal with these low-level algorithms.
Sensor vendors are now offering smarter components with built-in intelligence to help cope with these complexities. For example, with an ASSN — such as BNO055 — designers can rapidly prototype devices containing a gyroscope compensated compass, providing an accurate magnetic heading, ignoring stray magnetic fields that would confuse even the most precise analog compass. With the same sensor node, a robot designer could build a self-balancing robot focusing on the balance algorithms rather than the sensor processing algorithms.
ASSNs are driving the proliferation of sensor fusion, bringing these complex algorithms to the masses and allowing developers to focus on creating world-changing products that forever alter how we relate to one another.