A distinguishing characteristic that separates humans from other primates is the ability to walk upright on two legs, known as bipedalism.
This characteristic provided us with evolutionary advantages such as a greater field-of-vision, access to increased resources, and the ability to utilize our arms for tasks other than locomotion. In other words, the way we stand, walk, run, jump, and skip is fundamentally part of what makes us human.
Given its importance to human development and every-day life, gait has been considered a fundamental movement skill (10). Accordingly, gait has become one of the most popular movement patterns for professionals to assess in clinical and exercise settings. Traditional gait assessments use motion-tracking technology to compute body posture, joint angles, center of mass trajectory, and sometimes ground reaction forces. These data can be used by clinicians to assess why someone may have previously injured themselves, how to mitigate the risk of future injury and improve the efficiency of how someone moves. More recently, advances in Chaos Theory and machine learning have paved the way for novel motion analysis techniques. These will allow practitioners to garner new insights- some as crazy as being able to recognize people’s emotional state from how they walk (6)!
A considerable challenge to implementing gait assessments in practical settings is the massive costs associated with creating a gait laboratory. Not only is the motion capture equipment expensive (ranging from $10,000-$100,000), but they also require additional software and expertise to calibrate, collect, process, and analyze the data before its interpretation and implementation by a clinician. Additionally, it then necessitates that the clinician manages data storage solutions to recall this data in the future. Although some open-source software alternatives make collecting these data less expensive, this still takes valuable time away from performing clinical activities.
Curv leverages your smartphone and advances in computer vision to put a biomechanics laboratory in your pocket. Curv collects, processes, interprets, and stores motion data automatically with your smartphone camera. Therefore, you spend less time fumbling with expensive laboratory equipment and more time working with your client.
Here is a brief visual of how Curv works:
Although many clinicians already perform gait assessments without any motion-tracking technology, there are two main limitations of current practice. The first is that trained professionals are neither accurate nor reliable when visually observing three-dimensional human motion (8). The second is that some valuable insights, such as stride rate variability, are impossible to compute visually. After all, we’re only human.
Curv can measure various metrics such as cadence, stride length, trunk lean, cross gait, pelvic and shoulder obliquity, and frontal plane knee motion. However, three key metrics are worth highlighting because they are either impractical or impossible for practitioners to observe visually while also being clinically relevant:
1. Vertical Center of Mass (CoM) Displacement
2. Tibia Acceleration
3. Stride Rate Variability
First, there is a strong relationship between the sacrum's vertical displacement during walking and oxygen consumption (i.e. a global measure of walking efficiency) (7). However, merely reducing CoM vertical displacement by modulating stride length or by providing visual feedback may not reduce metabolic or mechanical energy expenditure while walking (4). This paradoxical finding is due to the increased work required of the lower extremity to coordinate a movement pattern that minimizes vertical displacement (1). Further, numerous coordination strategies influence one's center of mass displacement (e.g. ipsi- and contra-lateral knee flexion, pelvic obliquity and heel-rise) (2). Curv provides practitioners with an objective indicator of whether there is excessive (or too little) vertical displacement and how their clients produce this motion. Reliable identification of these coordination strategies will assist with the creation of personalized treatment plans for the client.
Secondly, although clinicians may successfully observe vertical CoM displacement metrics such as tibial accelerations must be computed using technology. High tibial accelerations are associated with increased risk for tibial stress fractures (9,12). Therefore, they are not only a clinically relevant metric for identifying risk factors for injury but can also be used to provide feedback during rehabilitation programs (3,11). This metric will be available in the future for devices with LiDAR (light detecting and ranging) hardware that uses lasers to measure depth.
Finally, stride-rate variability (SRV) can be a powerful metric for clinicians to compute, which is impossible to visualize otherwise. Therefore, clinicians must use motion-tracking technology to assess this. For example, although two people may have virtually identical average stride-rates, their SRV may be markedly different. Consider the following data from (5):
Although both participants have nearly identical average stride times, their step-to-step variability (i.e. SRV) is much different. Importantly, SRV (both its magnitude and structure) is related to a person’s fall risk, frailty, and even neuromuscular disorders. Simultaneously, improvements in muscle function from therapy are associated with SRV changes and improved clinical outcomes (5). Therefore, Curv can unearth these, and other, advanced measurements which are more sensitive to clinically relevant outcomes.
The Curv workflow modernizes both your clinical workflow and business. Assume that you paid, conservatively, $30,000 to obtain the equipment and software to perform gait assessments and charge $200 per assessment. This requires the clinic to conduct 150 assessments before breaking even! By leveraging the latest machine learning technology, Curv allows clinicians to offer gait assessments in a much more straightforward and quicker way than previously possible. Simultaneously, it will enable the clinic to operate at a profit immediately. These diversified revenue streams are vital for clinics to maximize long-term, sustainable growth. In addition to these financial benefits, the implementation of tech in your clinic can help promote your clients' buy-in. Clients love sharing the excellent tools, methods, and exercises that their therapist used to build them back up to health. Therefore, Curv enables you to provide a service level that will allow you to perform your job at a higher level while simultaneously working as an advertising vehicle.
1. Bertram, JEA and Hasaneini, SJ. Neglected losses and key costs: tracking the energetics of walking and running. J Exp Biol 216: 933–938, 2013.
2. Croce, UD, Riley, PO, Lelas, JL, and Kerrigan, DC. A refined view of the determinants of gait. Gait Posture 14: 79–84, 2001.
3. Crowell, HP and Davis, IS. Gait retraining to reduce lower extremity loading in runners. Clin Biomech 26: 78–83, 2011.
4. Gordon, KE, Ferris, DP, and Kuo, AD. Metabolic and Mechanical Energy Costs of Reducing Vertical Center of Mass Movement During Gait. Arch Phys Med Rehabil 90: 136–144, 2009.
5. Hausdorff, JM. Gait variability: methods, modeling and meaning. J NeuroEngineering Rehabil 2: 19, 2005.
6. Janssen, D, Schöllhorn, WI, Lubienetzki, J, Fölling, K, Kokenge, H, and Davids, K. Recognition of Emotions in Gait Patterns by Means of Artificial Neural Nets. J Nonverbal Behav 32: 79–92, 2008.
7. Kerrigan, DC, Viramontes, BE, Corcoran, PJ, and LaRaia, PJ. Measured versus predicted vertical displacement of the sacrum during gait as a tool to measure biomechanical gait performance. Am J Phys Med Rehabil 74: 3–8, 1995.
8. Krosshaug, T, Nakamae, A, Boden, B, Engebretsen, L, Smith, G, Slauterbeck, J, et al. Estimating 3D joint kinematics from video sequences of running and cutting maneuvers--assessing the accuracy of simple visual inspection. Gait Posture 26: 378–85, 2007.
9. Milner, CE, Ferber, R, Pollard, CD, Hamill, J, and Davis, IS. Biomechanical Factors Associated with Tibial Stress Fracture in Female Runners: Med Sci Sports Exerc 38: 323–328, 2006.
10. Newell, KM. What are Fundamental Motor Skills and What is Fundamental About Them? J Mot Learn Dev 1–35, 2020.
11. Sheerin, KR, Reid, D, Taylor, D, and Besier, TF. The effectiveness of real-time haptic feedback gait retraining for reducing resultant tibial acceleration with runners. Phys Ther Sport 43: 173–180, 2020.
12. Warden, SJ, Davis, IS, and Fredericson, M. Management and Prevention of Bone Stress Injuries in Long-Distance Runners. J Orthop Sports Phys Ther 44: 749–765, 2014.
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