Indications, Benefits and Barriers of 2D Motion Analysis

Original Editor - Thomas Longbottom based on the course by Damien Howell

Top Contributors - Thomas Longbottom, Kim Jackson, Jess Bell and Tarina van der Stockt  

Introduction[edit | edit source]

The observation, analysis and management of movement are necessary elements in addressing movement system impairments.  While the use of 2D slow-motion video can facilitate this process, this technique is under-utilised.[1][2]  The decision of whether or not this technique is right for an individual clinical practice can be facilitated by considering the benefits of this technology, the indications for its use, and the potential barriers that might impede its use.

Applying 2D Video to the Movement System[edit | edit source]

Designed by Freepik Posterior view of gait on treadmill

Physiotherapists are movement specialists. The ability to observe and analyse movement is important in the management of movement system impairment syndromes. Progress has been made in defining a framework to accomplish this in a systematic way, around a set of core movement tasks.[3] A specific challenge has been in maintaining a consistent approach, using a common language around movement constructs related to aspects of motor control such as symmetry, speed, alignment and amplitude.[3] Movement system diagnostic labels and classifications will continue to be developed. The application of 2D slow-motion video analysis is a technique and a tactic that can facilitate this process. Despite the usefulness of this form of motion analysis, it is an under-utilised technique with more than half of surveyed orthopaedic physiotherapists not using the technique in clinical practice.[1]A survey of 261 American Academy of Sport Physical Therapy members found that while over 70% of them did use video motion analysis in their practice, 84% of those members did so with 25% or less of their caseloads.[2]

Johann Windt, et al.,[4] propose a decision-making framework for implementing technology such as 2D motion analysis in the domain of sport. This framework is built around four questions, the first two of which will be addressed in this module:

  1. Will the promised information will be beneficial?
  2. Will the obtained information be reliable?
  3. Will it be possible to integrate, manage and analyse the data effectively?
  4. Will it be reasonable to implement the technology in clinical practice?[4]

Benefits of 2D Motion Analysis[edit | edit source]

The use of slow-motion video has the potential to increase the accuracy of the physiotherapist's analysis of movement. Slow motion, frame-by-frame analysis dramatically increases the ability to observe the details of movement. The ability to slow down the movement and to even freeze the motion at key moments increases observational powers, leading to improved accuracy and validity of the physiotherapist's observations.[5]

Incorporating 2D motion analysis into clinical practice also has the potential to increase engagement with the client. A high degree of engagement between the provider and client can lead to better outcomes, lower costs, and higher levels of client/clinician satisfaction. Increased self-awareness can facilitate engagement, boosting acceptance and aiding the client to see a different perspective. This can then facilitate decision processes and communication. The client and provider can see what movements and specifically what direction of movements are causing symptoms, leading to an understanding of how altering those movements can decrease or even eliminate those symptoms.[5]

Video and image analysis can improve communication, facilitating the conveyance of complex concepts and information not only to the client but to third parties as well. Comprehension can be improved by reinforcement of the information, helping the clinician and the client engage in reflective learning. Videos and images can grab attention and provide inspiration, and they can help overcome language barriers. The visual evidence and resulting validation can be provided to clients, referral sources, and third parties. When a client is sceptical and has not reached the contemplation stage of affecting change, the use of videos and images can be helpful in the client's understanding of what is occurring with movement and how it relates to their symptoms. In cases where the client may be magnifying their symptoms, video analysis can be used to validate the clinician's judgment. In other words, when the clinician is suspicious of the credibility of the patient's reports, video can help distinguish between symptom magnification and credible complaints.[5]


Reliability and Validity of 2D Motion Analysis[edit | edit source]

As noted by Windt et al.,[4] a practitioner considering investment into new technology should ask some important questions including not only whether or not the provided information is helpful, but also if it is trustworthy. In other words, is the information obtained via 2D motion analysis going to be reliable and valid? Are aspects of measurement error acceptable? Can a clinician be confident in decisions made based on that information? Consider the following:

  • We know that observational movement analysis does provide accurate and reliable judgments by clinicians classifying movement patterns.[7][8][9]
  • There is evidence that the use of smartphone applications for kinematic gait analysis is valid, with acceptable levels of measurement error.[10]
  • The ability to analyse the video in slow motion and the appropriate placement of the camera relative to the patient are important factors in achieving precision if looking at kinematic measures.[10] The use of protocols to record movement of the body segment of interest from specific camera placements provides consistency. Much like observational gait analysis, 2D video analysis can be conducted to view gait from sagittal and frontal planes. Finkbiner et al.[10] were able to use smartphones to record gait in the sagittal plane and obtain valid kinematic measures.
  • While using 3D video analysis may be the gold standard, there is evidence that 2D video analysis of movement in the sagittal plane possesses acceptable intra-rater and inter-rater reliability regardless of the experience level of the analyst in cases of repeated analysis on the same subject.[11]

Indications for 2D Motion Analysis[edit | edit source]

Slow-motion video and image analysis can aid the shift from ambiguity to accuracy and understanding. The technique can be useful for the clinician who may be lost or confused when trying to understand a complex and/or unusual client. If the clinician thinks atypical movement is observed but is not certain, or if more time and data is needed to observe the movement where clear diagnosis or classification is lacking, 2D motion analysis may be indicated. In cases where the client or perhaps the clinician is sceptical of the movement abnormality, or in legal cases where more rigorous documentation of the movement is needed, the clinician may opt to use this technique. The clinician may also want to use this method when performing case studies or other research endeavours with goals of publication or other dissemination.[5]

Designed by Freepik Exercise instruction using video of patient

From a clinical standpoint, when resources such as full-length mirrors are not available, potentially due to the client interaction taking place in a home health setting versus in the clinic, use of video may be useful in providing visual feedback of movement performance. Highlighting atypical movement in the video can aid prevention of injury and also improve performance. The clinician may also find it beneficial to provide the client with personalised video, recorded on the client's own device, of the client performing the exercises as instructed in order to enhance the home exercise programme. This could include audio instructions as well as video demonstration.[5]

Finally, the clinician should consider the benefits of storing the visual data of a wealth of patient experiences and thereby providing details for reflective learning and professional development. Dr. Arlan Cohn, writing under the pseudonym of Oscar London, considered the conundrum of experience without a record of learning. "The more patients a doctor sees, the fewer journals he has time to read. If he isn't careful, he can end up after forty years with a wealth of experience and a poverty of intellect."[12]

Barriers to Implementation[edit | edit source]

There are multiple potential barriers to implementing the use of 2D motion analysis in clinical practice. For some, the perceived difficulty in mastering new technology may be an obstacle. There are ways to approach this, including accessing YouTube and other sites where "How-to" videos may be posted. It is not uncommon to have a friend or colleague who is adept at managing new technology and who could be relied on for assistance. Technology companies often have a help desk that one may utilise during the learning process.[5]

Privacy and confidentiality can be a significant barrier. Laws and regulation around the protection of patient information must be adhered to, and these likely vary depending on one's location and the governing bodies associated with the area of practice.

  • One way to address the issue of privacy is to obtain the patient's permission. A statement can be added to a written informed consent that the patient signs. Here is an example statement that could be added to the informed consent document: "Photos and videos taken during the evaluation and treatment will be used in educational tools. By signing below, I consent to the use of photos and videos in a professional manner." [5]
    • The use of a dedicated clinic tablet or smartphone that can be locked away at the end of the day rather than a personal device can aid the protection of the confidential information.[14]
    • It may also be helpful to frame the image so that the client's face or identifiable features are not included. It is also important to ensure that no one else is inadvertently captured in the background of the photo/video.[5]
    • If there are concerns about storing the information, then the recording can simply be removed once it has been analysed or used to provide information to the client. This may be a less desirable option as it limits the functionality of the video, but it does meet the need of preserving privacy for the client.[5]
    • Finally, using the client's device is a way to capture the desired video and be able to refer to it for comparison following a time of clinical intervention without having concerns about liability on the part of the clinician for maintenance of confidentiality in storing the images.[5]

Another barrier to the use of 2D motion analysis in clinical practice relates to the potential difficulties integrating the video into the medical record. If the medical record is electronic, transmitting the video files must be accomplished in a secure fashion. There are emerging applications to facilitate this process, with varying levels of cost associated with their use. A few examples are listed here:

  • Waba Medical Pics: This app for the Apple iPad is designed to allow the secure transfer of clinical images into an image-management system.
  • Secure Clinical Image Transfer: This app works with iOS, Android, and Blackberry to facilitate the secure transfer of images into an electronic medical record.
  • Clinical Uploader: This is a mobile app designed by medialogix to be used with their information management systems. It is compatible with iOS and Android.
  • PicSafe: This iOS app removes any metadata from the images and encrypts them before transmitting. It also includes a patient consent feature.

Resources[edit | edit source]

References[edit | edit source]

  1. 1.0 1.1 Hensley CP, Millican D, Hamilton N, Yang A, Lee J, Chang AH. Video-Based Motion Analysis Use: A National Survey of Orthopedic Physical Therapists. Physical Therapy 2020;100(10):1759–70.
  2. 2.0 2.1 Hensley CP, Lenihan EM, Pratt K, Shah A, O'Donnell E, Nee P-C, et al. Patterns of video-based motion analysis use among sports physical therapists. Physical Therapy in Sport. 2021;50:159–65.
  3. 3.0 3.1 Quinn L, Riley N, Tyrell CM, Judd DL, Gill-Body KM, Hedman LD, et al.. A Framework for Movement Analysis of Tasks: Recommendations From the Academy of Neurologic Physical Therapy’s Movement System Task Force. Physical Therapy 2021;101(9).
  4. 4.0 4.1 4.2 Windt J, Macdonald K, Taylor D, Zumbo BD, Sporer BC, Martin DT. “To Tech or Not to Tech?” A Critical Decision-Making Framework for Implementing Technology in Sport. Journal of Athletic Training 2020;55(9):902–10.
  5. 5.0 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 Damien Howell. Indications, Benefits and Barriers of 2D Motion Analysis. Plus Course. 2021.
  6. DamienHowellPT . The Advantage of Slow Motion Analysis. Available from: [last accessed 18/1/2022]
  7. McGinley JL, Goldie PA, Greenwood KM, Olney SJ. Accuracy and reliability of observational gait analysis data: Judgments of push-off in gait after stroke. Physical Therapy. 2003;83(2):146–60.
  8. Zablotny C, Hilton T, Riek L, Kneiss J, Tome J, Houck J. Validity of visual assessment of SIT to stand after hip fracture. Journal of Geriatric Physical Therapy. 2020;43(1):12–9.
  9. Harris-Hayes M, Steger-May K, Koh C, Royer N, Graci V, Salsich G. Classification of lower extremity movement patterns based on visual assessment: Reliability and correlation with 2-dimensional video analysis. Journal of Athletic Training. 2014;49(3):304–10.
  10. 10.0 10.1 10.2 Finkbiner MJ, Gaina KM, McRandall MC, Wolf MM, Pardo VM, Reid K, et al. Video Movement Analysis using smartphones (vimas): A pilot study. Journal of Visualized Experiments. 2017;(121).
  11. Reinking MF, Dugan L, Ripple N, Schleper K, Scholz H, Spadino J, et al. Reliability of two-dimensional video-based running gait analysis. International Journal of Sports Physical Therapy. 2018;13(3):453–61.
  12. London O. Review the World Literature Fortnightly. In: Kill as few patients as possible: And Fifty-six other essays on how to be the world's best doctor. Berkeley: Ten Speed Press; 2008. p. 57.
  13. Seh AH, Zarour M, Alenezi M, Sarkar AK, Agrawal A, Kumar R, et al. Healthcare data breaches: Insights and implications. Healthcare. 2020;8(2):133.
  14. Chan N, Charette J, O Dumestre D. Should ‘smart phones’ be used for patient photography? Plastic Surgery. 2016;24(01):32–4.