Motion Tracking using Magnetic Induction Sensors

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Programs & missions

A multi-panel diagram illustrating a human motion recognition system. On the left, a back view silhouette of a person shows labeled transmitter (TX1–TX8) and receiver (RX) sensor placements on the arms, waist, and legs. In the center, a simplified stick-figure skeleton displays motion capture markers labeled M1–M15 at the head, shoulders, elbows, hips, knees, and feet. On the right, a 3D skeletal model highlights joint angles at the lower leg and ankle (e.g., θ_TX8 and related components). Along the bottom, time-series motion signals (“MI-motion data”) are shown feeding into a deep recurrent neural network (RNN), which outputs predicted activity labels such as Walk, Run, and Jump.

This project was originally initiated by Dr. Negar Golestani during her time as a Ph.D. student in the MiXIL laboratory. The foundational idea was introduced in her 2017 paper, “Magnetic Induction Communications for Wireless Body Area Network”. Building on this, the 2020 publication in Nature Communications (Golestani & Moghaddam, 2020) presented a novel human activity recognition (HAR) system using magnetic induction (MI) signals and deep recurrent neural networks. This system offers a low-power, secure, and accurate alternative to conventional wearable sensors by capturing relative motion through MI-based coil networks.

Currently, the project is being extended in collaboration with the ACME Laboratory at USC, with the goal of advancing from activity recognition to full-body human motion tracking.

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