Running into peak performance and recovery with inertial motion capture technology

A prevalent myth is that running is a cause of many injuries. More correctly, injuries are hastily brought on by both inadequate recuperation and bad running technique. Overusing muscles causes between 50 and 75 percent of running-related ailments, including runner's knee, stress fractures, and torn hamstrings. In actuality, running is more frequently seen as a fantastic activity for building muscle. The team at Movella has been using our own Xsens DOT development platform to analyze and track runners’ gaits, running styles and more to develop a demonstration app, which can aid training and recovery for runners. We tested the app on 11 male participants during the Enschede Half Marathon in April 2022.

 

The application is in its early conception stage, with the team using the Enschede marathon as an testing ground. Frank Wouda and Jason Konrath, leading the development team, share insights into the data collected at the half marathon, what this data means for runners and the use of the Movella’s Xsens DOT platform.

 

What data did you collect during the marathon?

 

Frank: During the Enschede Marathon, we recorded the runner’s speed, whether the speed stayed consistent, dropped or increased during the run. We analyze step frequency and step length to understand the running style of each runner. The pelvis is really important to keep aligned when running; Xsens Dot motion tracking technology enables us to analyze the runner’s pelvic tilt, rotation and obliquity. The pelvis is a complex structure that rotates on all axes and surrounding the pelvis lay strong muscles that power push-off. Before initial contact, these strong muscles are already active to reduce collision with the ground. The subtle angles of the pelvis provide information on how you initiate your leg movement, so ensuring the pelvis is aligned is critical to preventing injury for distance runners.

 

 

There are apps for running that already exist in the market. What makes this application unique?

 

Jason: There are many well-known running apps that track distance and pace or provide a variety of coaching and community functions, but there are currently no applications that track or analyze data based on an individual’s body throughout training sessions.

Frank: A slight change in your biomechanics may result in changes in impact forces and will have consequences for injury risk and running efficiency. With Sports Assist, you can understand how shock propagates through the body by looking at the impact on the shin bone and hips- we call this 'shock absorption. Understanding your body's symmetry can help you prevent injuries.

 

What does the app monitor, and why?

 

Frank: While most running apps monitor speed, duration and total distance, our app takes this further by also monitoring the individual based on how their body is moving. Duty Factor monitors the time that a users’ foot is in contact with the ground relative to their stride. Having a low Duty Factor indicates that you utilize elastic energy, which is optimal for faster running. The app also breaks down the user’s running style, detailing where someone may be pushing one foot harder compared to the other, and where the feet differ in step length. The app establishes gait parameters that will explain how long your foot is in contact with the ground each step.

Jason: Understanding what areas you are possibly overexerting is also an aspect the app relays. Users will have a breakdown of their pelvis orientation. An excessive rotation can indicate several things: a narrow gait, insufficient arm swing to compensate and/or too much emphasis on the push-off. Understanding the braking force on the pelvis is especially important to consider in understanding the efficiency of running. The app helps users understand their impact forces while running. A slight change in your biomechanics may result in changes in impact forces and will have consequences for injury risk and your running efficiency.

 

 

What did you find from the study?

 

Jason: The best way to answer this is to zone in one participant, as an example of the kind of data we got from the test. In participant number 4, for example, we observed that their left and right Pelvis rotation appeared to be well balanced for each side, around 8-9 degrees. Pelvic obliquity was slightly smaller on the right side (3 degrees vs 6 degrees in many cases), indicating not as much ‘hip drop’. This is also reflected in impact metrics. It appears to even out more towards the end of the run, likely as the right step lengths started reducing. A gradual reduction in the step length can be observed in the right leg. This is also reflected in impact metrics for the right leg. The right leg appears to experience larger impacts than the left leg, which is seen from both the lower leg sensors (9%), and hip sensors (11%). This reflects a bordering functional asymmetry on the right leg, but is not significant. The larger impacts on the right also correspond with larger contralateral pelvic obliquity.

 

How has technology shaped your approach to developing the app?

Frank: It was a straightforward process to incorporate Xsens motion capture technology within the flutter frameworks, which led to an overall faster development time for the app itself.

Jason: Specific algorithms were employed that allowed the identification of the appropriate kinematic parameters. Many of these parameters are known throughout the literature to influence running in both performance and rehabilitation settings. This application demonstrated a user-friendly interface to acquire the data and provide comprehensive reporting of the aforementioned parameters

 

How do you look to use the technology in the future?

Jason: We have really enjoyed using the technology so far- it's been incredible for us to see the app pick up on runners’ movements and gait as the marathon went on. The trial has helped us understand what new metrics we could measure as we continue to develop the app.

Frank: The technology was applied for a relatively small group of runners. We want to extend this group by including more variation in fitness, run styles, body types, etc. This allows us to further improve on attachment methods and user experience. Additionally, a bigger dataset makes development of new features to gain more in depth runner insights easier.