# Quality Assessment of a Foot-Mounted Inertial Measurement Unit System to Measure On-Field Spatiotemporal Acceleration Metrics

## TL;DR

This study assesses the accuracy of a foot-mounted IMU system for measuring sprinting metrics in athletes, comparing it to a 3D motion analysis system.

## Contribution

The study provides empirical validation of a low-cost IMU system for field-based spatiotemporal gait analysis in sprinting.

## Key findings

- The IMU system showed a root mean square error of 0.22 m for stride length and 0.04 s for stride duration.
- Mean biases for peak velocity and instantaneous velocity were −0.67 m/s and −0.51 m/s, respectively.
- The system demonstrated acceptable accuracy for stride length, duration, and cadence in sprinting.

## Abstract

(1) Background: The use of wearable technology for assessing running biomechanics in field-based sports has increased in recent years. Inertial measurement units (IMUs) are low-cost, non-invasive devices capable of estimating spatiotemporal gait-related metrics during overground locomotion. This study evaluated the accuracy and concurrent validity of a foot-mounted IMU system for estimating sprinting kinematics. (2) Method: Twenty-five elite and sub-elite athletes completed four maximal 10-metre fly efforts, with their kinematics measured concurrently using a three-dimensional motion analysis system and IMUs. (3) Result: The foot-mounted IMU system’s root mean square errors for stride length and duration were 0.22 m and 0.04 s, respectively. Mean biases (95% level of agreement) were −0.67 m · s−1 (−1.19; −0.14) for peak velocity, −0.51 m · s−1 (−1.10; 0.09) for instantaneous velocity, and 0.17 m · s−2 (−1.04; 1.37) for instantaneous acceleration. Stride length, duration, and cadence were −0.07 m (−0.36; 0.23), 0.02 s (−0.02; 0.06), and −4.64 strides · min−1 (−15.82; 6.53), respectively. (4) Conclusions: End users implementing this technology in research and practice should interpret this study’s findings relative to their analytical objectives, logistical resources, and operational constraints. Therefore, its adoption should be guided by the specific performance metrics of interest and the extent to which the system’s capabilities align with the outcomes the end user aims to achieve.