Automate the FMS with a motion capture system
The Functional Movement Screen is the gold standard for assessing how well one can move. This blog explores how the FMS can use motion capture systems to automate logging and produce more accurate results
The Functional Movement Screen has become the gold standard for healthcare providers (doctors and trainers) to screen patients/clients for risk of injury by observing whether or not there are dysfunctions in their movement patterns. The FMS aims to identify imbalances in mobility and stability when performing the following 7 functional movements:
- Deep Squat
- Hurdle Step
- In-line Lunge
- Active Straight-leg Raise
- Trunk Stability Push-up
- Rotary Stability
- Shoulder Mobility
These movements are measured using the FMS toolkit.
Each movement is given a score of 0-3, 3 being the best. When all movements are completed a score is produced. Lower scores have been noted to be associated with increased BMI, increased age and decreased activity level. Higher scores have been noted to be associated with increased fitness. Ultimately, these scores inform exercise prescription design.
Example: In many cases, before a trainer designs workout programs for clients, trainers will put their clients through the FMS to better understand their baseline and use their baseline results to develop a program.
While the FMS is the gold standard, it relies on redundant manual entry and human diagnosis. If a doctor uses a pulse oximeter to assess a patients pulse, then a trainer should be using a motion capture system for assessing a clients mobility. With motion capture sensors the FMS score can be automated and become increasingly more personalized.
I tested a wearable motion capture system, which was programmed to automate the logging of the FMS results. This motion capture system linked my movements to my virtual 3D avatar. When I moved, my avatar moved.
As a trainer I can hook my clients up with motion capture sensors and walk them through the FMS. Instead of manually logging their results, the sensors would assist. In doing so I could use software to find deeper insights into my clients movements and in turn design better exercise programs.
One of the sensors that I have used is Notch wearables. They are relatively low cost, $429 and comes with a developer SDK.
Note that the goal here is not to replace the FMS. The goal would be to improve the FMS by adding in machine learning so that trainers and even physical therapists can design better exercise programs. Additionally, computer vision could also be used instead of motion capture sensors. However, until computer vision improves, motion capture sensors will work best.
Lastly, this technology needs to be more user friendly, there needs to better education and greater incentives. The ease of use and need to use matters. If a health care provider is to invest into motion capture technologies, there needs to be a clear ROI.
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