High-speed 2D video analysis has become the go-to tool for physical therapists and athletic trainers who work with runners. And rightly so! It only requires a good camera set-up and a treadmill. This makes it an inexpensive, efficient, and user-friendly alternative compared to sophisticated laboratory based 3D motion analysis . However, what we are fundamentally doing with 2D video analysis is attempting to quantify a qualitative assessment. There are some practical shortfalls with video analysis that are stealing your precious time. Here is why!
7 shortfalls of 2D video analysis
1. Over-simplifying complex movement of running
The words of Goeffrey Dyson in his 1962 book “The Mechanics of Athletics” should ring a bell: “Running can be considered both simple and difficult - simple because it is an instinctive, habitual skill, performed at some time by all but the most unfortunate, - but difficult in its mechanical complexity”. With slow-motion video analysis we try to reduce the complexity of running by focusing on running footages of key events such as initial contact, mid-stance, push-off, or flight phase. Unfortunately, this means that you sometimes lose the opportunity to look at the quality of movement. By analyzing running movement at certain only limited snap-shots of the entire gait cycle, we often forget to see the wood for the trees.
2. Capturing a pattern, not the driving forces
Through those key events, we try to understand how the body is loaded by the impact (i.e., kinetics), every time the foot hits the ground. Based on a few research findings on the relationship between running patterns and kinetics, we often make assumptions how this happens. Although, do we truly know that a heel striker always has a higher impact compared to a forefoot striker? Or that a shorter stride length is always reducing the impact?
3. Capturing data based on anatomical landmarks
During a video-assessment, we want to quantify running form with data in side and back/frontal view (sagittal and frontal planes). We try to measure several parameters linked to pronation, contralateral pelvic drop, hip adduction angle, vertical displacement. The measurement of these parameters is based on specific landmarks of the body. The problem is that strict standardization, correct marker placement and tight clothing are necessary to become reliable as possible.
4. Comparing videos requires standardization and processing time
Before you start a video-assessment, you have to create a standardized set-up with sufficient space (e.g., 2m behind your treadmill for frontal plane analysis, and 1m next to treadmill for sagittal plane) to compare 2 different runs. Camera positioning, lighting, clothing, etc. are all factors that can interfere with the reliability of your measurement – and thus affect the quality of your service too. To find the exact timing of kinematic events such as foot touchdown or moment of mid-stance (lowest landing position) requires slowly advancing the video frame by frame. Multiply this time spent by the number of parameters you want to extract (for both frontal and sagittal plane views).
5. Spatiotemporal data remains a tough cookie
Do you want to know what your ground contact time and flight time are? Ground contact time can range from 80 to 400ms, flight time even from 0 to 150ms. Quantifying spatio-temporal parameters requires consistent –standardization when events begin and end. Due to the short timescale, frames per second can’t be high enough. Most smartphones can go up to 120 frames per second. That’s a good starting point, but it can be better.
6. Based on a limited number of steps
To be as efficient as possible, we mostly use 1 running step and analyze from 1 frame for each key event in the sagittal and frontal plane. Can we take conclusions with only one frame? Research has shown that the mean of at least 7 steps should be included to get reliable 2-D video analysis (Dingenen et al., 2018). How many of us are using the recommended number of steps in practice?
7. Limited (mostly) to confined indoor spaces
Indoor treadmill testing is still a good way to study the mechanics of running under well-controlled and reproducible conditions. Research showed that treadmill-based analysis of running mechanics can be generalized to overground running mechanics (Riley et al., 2008; Van Hooren et al.,2020). However, we stay in an artificial environment. Do you know how a runner is responding to the different outdoor elements; the surface, weather, fatigue? For example, our research at KU Leuven demonstrated that running on outdoor woodchips reduced the impact while increasing the dynamic instability of running (Schutte et al., 2016; Boey et al., 2016).
Shifting paradigms: Wearable technology on its way
At the moment, 2D video analysis stays a little brother of current golden standard 3D motion capture analysis. Wearable technology is emerging widely, from commercial to professional devices, to help quantify running in the real world. High-resolution IMU devices are capable of measuring detailed and reliable information about spatiotemporal, kinetic, and kinematic data. Ultimately, switching to wearable technology (that is evidence based) can save you precious time and provide comprehensive analysis". Here are a number of reasons why switching to Runeasi wearable technology can save you precious time AND provide comprehensive analysis:
- Captures complex movements and compensations of running
- Holistic: kinematic, spatiotemporal, and kinetic metrics
- Minimal standardization: One correct anatomical position to take care of
- Accurate and automated spatiotemporal data
- Record every running step in a session
- Record indoors or outdoors in the real-world (e.g., tracks, trails, or pitch)
- Automated post-processing and results generation
How to move your practice forward: Wearable technology and 2D video analysis hand in hand
Wearable technology and video analysis can work together. Instead of focusing on quantifying your video analysis, it’s a better idea to put your precious time into looking at how your runner is moving qualitatively. This doesn’t mean that you don’t look anymore at the key events. Instead, you can explain your objective data obtained from wearable technology, with your video images and quality assessment. Instead of using your time to make a report, you can use it for analysis, interpretation, making your plan of action, and giving constructive feedback in line with the individual needs of the runner.