Recently, Dongyao Chen’s team from John Hopcroft Center for Computer Science, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, published one latest and extraordinary research result “Automatic Calibration of Magnetic Tracking” in the top international conference MobiCom 2022 (International Conference on Mobile Computing and Networking). Dongyao Chen is Corresponding author.

Magnetic sensing is emerging as an enabling technology for various engaging applications. Representative use cases include high-accuracy posture tracking, human-machine interaction, and haptic sensing. This technology uses multiple MEMS magnetometers to capture the changing magnetic field at a close distance. However, magnetometers are susceptible to real-world disturbances, such as hard- and soft-iron effects. As a result, users need to perform a cumbersome and lengthy calibration process frequently, severely limiting the usability of magnetic tracking.

To remove/mitigate this limitation, we propose MAGIC (MAGneto-meter automatIc Calibration), a systematic framework to automatically calibrate both soft- and hard-iron disturbances for a MEMS magnetometer array. To minimize the need for user intervention, we introduce a novel auto-triggering module. Unlike the legacy manual calibration method, MAGIC achieves superior calibration performance (e.g., for tracking applications) with minimal user attention. Via empirical studies, we show MAGIC also incurs marginal overhead and cost, such as a total energy cost of 0.108 J.

Dongyao Chen, Assistant Professor of Shanghai Jiao Tong University. He received his Ph.D from the Department of Computer Science and Engineering at the University of Michigan, Ann Arbor. His research focuses on the system and security of Internet of Things (IoT), exploring the integration of machine learning with real life.