Motion capture – video analysis vs wearable sensors (the pros and cons)

3 mins

As discussed in our blog Movement analysis – types of biomechanics and technologies , there are different ways to measure and analyse movement. However, the most used method is still observation with the naked eye. While the eye is well capable of describing movement, it has its shortcomings. Read more about this in our blog the accuracy and validity of movement analysis with the naked eye.

Since this method of analysis is highly subjective, we are increasingly seeking the support of technological solutions to work more objectively. Where we used to depend on expensive camera systems, nowadays we can measure the geometry of motion using wearable sensors and even mobile phones. In this article, we delve deeper into the differences between various techniques to capture movement in data and their advantages and disadvantages.

Already in the 19th century, we used photography and videography to investigate our own movement behavior[1]. By placing light (retro)reflective spheres – so-called markers – on, for example, the femur and fibula, their angles can be precisely recorded with cameras, allowing him to reconstruct the movement of the body in 3D and calculating joint angles. The use of markers is still considered the golden standard, as movement can be accurately recorded to a degree accuracy.

Due to the high purchase costs of such systems and restrictions in usage, markerless motion capture is also on the rise. Nowadays, it is even possible to record movement using this technique with one or more mobile phones. This, of course, drastically reduces the costs, making the application of this technique increasingly popular.

However, there should be questions about the accuracy of this technology. Deviations of at least 5 to 10 degrees for high-end products[3] and even 10-20 degrees when using mobile phones are quite common. Given that the range of motion of a hip during walking is not much more than 35 degrees, we are already talking about deviations of 15 to more than 30%! Furthermore, at lower frame rates (such as 25, 50, or 100 Hz), it is not possible to accurately calculate speed and acceleration[2], while these are two crucial variables in movement. It is therefore questionable to what extent this technology is already truly suitable for decision-making.

In the case of a gait analysis, the use of cameras also has the disadvantage that the distance that can be covered is limited. This has quite a few disadvantages, especially since walking is a very rhythmic movement that needs time to develop and stabilize. Therefore, treadmills are often used. However, there is a lot of research showing that the walking pattern significantly adjusts when not walking on over ground.[4,5,6]

These problems can be solved by using wearable (IMU) sensors. Because these are close to the body, the accuracy of the measurement is very high (<1 degree), while virtually unlimited distance can be covered in almost any conceivable environment. Moreover, sensors are very affordable these days, making this technique a very good alternative to the use of expensive high-speed cameras as integrated into marker-based motion capture systems and has it proven to be superior to markerless motion capture.

[1] Baker RB. The history of gait analysis before the advent of modern computer. Gait & Posture. 2007, Sept,26(3):331-342. doi:10.1016/j.gaitpost.2006.10.014.

[2] Rowe PJ. The Past and Future of Clinical Biomechanics: Time to deliver on the legacy of pioneers such as Professor John P. Paul (1927-2013). Med Eng Phys. 2019 Oct;72:66-69. doi: 10.1016/j.medengphy.2019.08.009.

[3] Van Hooren B, Pecasse N, Meijer K, Essers JMN. The accuracy of markerless motion capture combined with computer vision techniques for measuring running kinematics. Scand J Med Sci Sports. 2023 Jun;33(6):966-978. doi: 10.1111/sms.14319.

[4] Rozumalski A, Novacheck TF, Griffith CJ, Walt K, Schwartz MH. Treadmill vs. overground running gait during childhood: a qualitative and quantitative analysis. Gait Posture. 2015 Feb;41(2):613-8. doi: 10.1016/j.gaitpost.2015.01.006.

[5] Hollman JH, Watkins MK, Imhoff AC, Braun CE, Akervik KA, Ness DK. A comparison of variability in spatiotemporal gait parameters between treadmill and overground walking conditions. Gait Posture. 2016 Jan;43:204-9. doi: 10.1016/j.gaitpost.2015.09.024.

[6] Hutchinson LA, De Asha AR, Rainbow MJ, Dickinson AWL, Deluzio KJ. A comparison of centre of pressure behaviour and ground reaction force magnitudes when individuals walk overground and on an instrumented treadmill. Gait Posture. 2021 Jan;83:174-176. doi: 10.1016/j.gaitpost.2020.10.025.

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Overground (indoor & outdoor)
Hardware ORYX Knee Stability - Static ORYX Knee Stability - Dynamic ORYX-GO
Motion Data Hub
4G Tablet
Shoe clips

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IMU sensors

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Base Straps

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Over Straps

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Walking / Running gait analysis ORYX Knee Stability - Static ORYX Knee Stability - Dynamic ORYX-GO
Stride duration
Cadence / stride frequency
Ground contact time
Duty factor*
Propulsive velocity (hamstring function)*

* Running analysis only

ROM (+ symmetry) ORYX Knee Stability - Static ORYX Knee Stability - Dynamic ORYX-GO
Micro (Local attractors) ORYX Knee Stability - Static ORYX Knee Stability - Dynamic ORYX-GO
Core stability
Hip lock
Knee stability (Q/H coordination)
Ankle stiffness
Coordination Landscape (varibility)
Meso (Global attractors) ORYX Knee Stability - Static ORYX Knee Stability - Dynamic ORYX-GO
Joint coupling (Hip - Knee)
Macro (Total attractors) ORYX Knee Stability - Static ORYX Knee Stability - Dynamic ORYX-GO
Foot plant projection
Squat (Double & single leg) ORYX Knee Stability - Static ORYX Knee Stability - Dynamic ORYX-GO
ROM joints + LSI