Clinical Application of Inertial Measurement Unit Sensor in Running Gait Analysis

By Jenna Zajac PT, DPT, PhD.

What is an Inertial Measurement Unit (IMU)?An inertial measurement unit is a compact sensor system composed of accelerometers and gyroscopes used to measure and report an object’s linear acceleration, angular velocity, and sometimes magnetic field. IMUs are essential tools for capturing and analyzing human movement, offering precise measurements of motion and orientation in three dimensions. Equipped with accelerometers, gyroscopes, and sometimes magnetometers, IMUs track linear acceleration, angular velocity, and orientation relative to a fixed reference point. This capability makes them invaluable in sports performance analysis, rehabilitation, and biomechanics research. For example, in physical therapy, IMUs can provide real-time feedback on gait patterns or joint movements, helping clinicians assess progress and tailor interventions. In wearable devices, IMUs enable athletes to monitor metrics like stride length, cadence, and balance, enhancing performance and reducing injury risk. Their portability, ease of integration, and ability to function outside laboratory settings make IMUs a powerful tool for understanding and optimizing human movement.

Runeasi Background

Runeasi is a high-end sacral sensor that captures real-time running data in the clinic or remote setting. The sensor is placed directly on the skin for increased accuracy and consistency of measurements. Runeasi allows for wireless transmission where you can either display data in real-time on a tablet or automatically upload entire sessions. It is a valid and reliable sensor that provides data-driven personalized strategies to monitor progress and provide real-time biofeedback to accelerate motor learning. 

A PhD researcher in Belgium, Kurt Schütte, sought to design a sensor that could be trusted and used to examine applied research questions about running fatigue and overuse injuries outside the lab. He sought to validate every step of the way, from raw sensor signals, to running gait event detection, to novel output metrics. His first study revealed that a single trunk worn wearable sensor could reliably detect ‘dynamic instability’ equally as good as the gold standard, prompting several more studies related to dynamic instability and other meaningful metrics such as impact loading. A new level of analysis included machine learning and artificial intelligence to predict when a runner begins to fatigue or when a runner changes training surface. The mission of Kurt’s work was to bring evidence-based technology into the hands of expert clinicians in the real world.