IMU Gait Analysis

Studying how people walk is an important task in healthcare, sports, and rehabilitation. It helps us spot walking issues, track recovery progress, and even design better treatments for conditions like stroke or Parkinson’s. One great tool for this is the 6-axis Inertial Measurement Unit (IMU). These devices combine a 3-axis accelerometer and a 3-axis gyroscope, and are very handy for collecting data about movement. You can stick them on a shoe, strap them to a leg, or embed them in a wearable device to track how someone moves.

But here’s the catch: accelerometers and gyroscopes, while powerful, have their downsides. Accelerometers measure linear acceleration and can estimate how far someone moves or how fast, but they’re noisy and can be thrown off by quick jolts or vibrations. Gyroscopes, which measure rotational velocity, are great for capturing smooth and precise turning motions, like the angle of a foot during a step. However, they tend to drift over time, leading to small errors that snowball into big ones.

This is where sensor fusion has a lot to offer. By combining data from both sensors, fusion algorithms can balance the strengths of each to help compensate for the weaknesses of the other. Gyroscope data can stabilize noisy accelerometer readings, and accelerometer data can reduce gyroscope drift. The sensor fusion algorithms discussed in this paper help this combination work smoothly, producing clean, reliable data about how someone walks.

Benefits of accurate gait analysis include helping to predict falls in older adults, tracking how well a patient is recovering from surgery, or even fine-tuning athletic performance. Without sensor fusion, IMU data would be too messy or unreliable to perform these functions. Continue for a deep dive into how sensor fusion works in the context of the 6-axis IMU.