By Ryan Brodie - Canadian Sport Institute Pacific
Here you will find links to other articles and resources foucused on the topic of running and gait analysis using IMU sensors. Using freely available libraries and tools in Python, R or Matlab, one can extract, report on and compare all sorts of performance and mechanically relevant metrics.
Updated Nov. 15, 2023, 10:11 p.m.Basic Data Handling and Visualization
In these tutorials, we will be using and re-using many basic processes.
Basic Accelerometry Visualization defines and breaks down the basic_timeseries function
Signal Processing and Metric Extraction
Basic Signal Filtering - This tutorial leads through the process of applying filters of differnent parameters to accelerometry data. It shows the impact of each of these decisions on the resultant signal.
Time and Frequency Domain Analysis - Both time and requency domain provide opportunities for generating important metrics from an activity such as running. Event detection and pattern recognition are both discussed here.
Peak Detection and Event Detection - Peak detection and threshold crossing are two primary methods of event detection in cyclic signals such as you find in running, rowing, swimming and other cyclic activities.
Bringing it To Life
A simple, working signal processing workflow - This app allows you to investiate data collected during running, and see how different decisions along the processing pipeline can impact our ultimate outcomes. This has impact for metric generation, and from that, gait assessment.