The STABILO DigiPen is a sensor-enhanced writing instrument with internal data processing capabilities and an external datalink for communication with compatible devices. It will register accelerations and its position in space and correct this position data for drift. At the same time, it can be used as a regular ballpoint pen. Motion data can be stored in 64 Mbit of internal memory or transmitted via a Bluetooth Low Energy (BLE) wireless connection to a connected device.
The interaction between the user and the pen will be primarily by writing. Operating modes can be selected by removing rsp. replacing the cap and by inserting rsp. removing a USB cable at the rear end of the product. These are the only ways of interaction, and no switches are provided. The current operating mode is indicated by a green and a red LED at the rear shaft which will either blink (active mode) or be continuously illuminated (connected mode).
Mode 1: Motion Tracking
Since the pen can measure acceleration and angular velocities around all three axes and updates its attitude information every 5 ms, it can be used for motion tracking in 3D. The internal force sensor can be activated by an optional button, so a proportional, user-selectable scalar parameter can be added. This allows the pen to be used as an ergonomic computer mouse with force-sensitive input or as a 3D motion controller in a virtual or augmented reality system.
Mode 2: Motor Skill Measurement
The product can be used in a schooling or therapeutic environment during handwriting training and can report to the connected device how well the user has advanced in the training of handwriting motor skills. This is done by analyzing the general motion pattern (up- and downstrokes, circles) and measuring the number of velocity peaks within a single stroke rsp. the relative size and alignment of horizontal and vertical parts of the motion.
Mode 3: Pattern Recognition
For users with developed handwriting skills, the sensor signals of the DigiPen can be fed into a pattern matching algorithm which will run on the connected device. The sensor fusion in the product will normalize the sensor readings, subtract drift and reduce the data volume such that the data link to the connected device does not need more energy than necessary. On the connected device, individual characters are modeled by Hidden Markov Models (HMMs) and concatenated to word models. A statistical language model is used to enhance recognition performance and restrict the search space. The resulting text output can be displayed and stored on the connected device.