Moving outside the lab: Markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump

Drazan, John F.; Phillips, William T.; Seethapathi, Nidhi; Hullfish, Todd J.; Baxter, Josh R.

doi:10.1016/j.jbiomech.2021.110547

Cited by 59 publications

(46 citation statements)

References 30 publications

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“…How then can we study the refinement of ecological, complex motor skills over longer timescales? There have been recent efforts to study motor skill acquisition outside the lab environment (Stafford and Dewar, 2014;Bönstrup et al, 2020;Drazan et al, 2021;Haar et al, 2021;Johnson et al, 2021;Tsay et al, 2021b). Online video game platforms, in particular, provide an unprecedented opportunity to reach more than 400 million intrinsically motivated people who voluntarily hone their cognitive, perceptual (Li et al, 2009;Appelbaum et al, 2013), and motor skills over weeks, months and even years (Green and Bavelier, 2003;Eichenbaum et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Long-Term Motor Learning in the “Wild” With High Volume Video Game Data

Listman

Tsay

Kim

et al. 2021

Front. Hum. Neurosci.

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Motor learning occurs over long periods of practice during which motor acuity, the ability to execute actions more accurately, precisely, and in less time, improves. Laboratory-based studies of motor learning are typically limited to a small number of participants and a time frame of minutes to several hours per participant. There is a need to assess the generalizability of theories and findings from lab-based motor learning studies on larger samples and time scales. In addition, laboratory-based studies of motor learning use relatively simple motor tasks which participants are unlikely to be intrinsically motivated to learn, limiting the interpretation of their findings in more ecologically valid settings (“in the wild”). We studied the acquisition and longitudinal refinement of a complex sensorimotor skill embodied in a first-person shooter video game scenario, with a large sample size (N = 7174, 682,564 repeats of the 60 s game) over a period of months. Participants voluntarily practiced the gaming scenario for up to several hours per day up to 100 days. We found improvement in performance accuracy (quantified as hit rate) was modest over time but motor acuity (quantified as hits per second) improved considerably, with 40–60% retention from 1 day to the next. We observed steady improvements in motor acuity across multiple days of video game practice, unlike most motor learning tasks studied in the lab that hit a performance ceiling rather quickly. Learning rate was a non-linear function of baseline performance level, amount of daily practice, and to a lesser extent, number of days between practice sessions. In addition, we found that the benefit of additional practice on any given day was non-monotonic; the greatest improvements in motor acuity were evident with about an hour of practice and 90% of the learning benefit was achieved by practicing 30 min per day. Taken together, these results provide a proof-of-concept in studying motor skill acquisition outside the confines of the traditional laboratory, in the presence of unmeasured confounds, and provide new insights into how a complex motor skill is acquired in an ecologically valid setting and refined across much longer time scales than typically explored.

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Section: Introductionmentioning

confidence: 99%

Long-Term Motor Learning in the “Wild” With High Volume Video Game Data

Listman

Tsay

Kim

et al. 2021

Front. Hum. Neurosci.

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“…In assessing the validity and limits of markerless motion capture, many studies have concurrently compared markerless systems to marker-based systems (Clark et al, 2013;Sandau et al, 2014;Mentiplay et al, 2015;Perrott et al, 2017;Harsted et al, 2019;Tanaka et al, 2019;Tipton et al, 2019;Wochatz et al, 2019;Drazan et al, 2021). In such studies, markerless motion capture has shown great promise.…”

Section: Strengths Agreement Between Markerless and Marker-based Systemsmentioning

confidence: 99%

“…In such studies, markerless motion capture has shown great promise. Specifically, focusing on the ability to detect lower extremity movement, multiple studies have indicated that markerless motion capture can efficiently capture spatiotemporal joint kinematic variables (Clark et al, 2013;Sandau et al, 2014;Mentiplay et al, 2015;Rocha et al, 2018) with moderate-to-high agreement during tasks such as a single leg squat (Perrott et al, 2017;Kotsifaki et al, 2018;Tipton et al, 2019), vertical jump (Drazan et al, 2021), countermovement jump (Kotsifaki et al, 2018), stair climbing (Ogawa et al, 2017), walking (Ceseracciu et al, 2014;Sandau et al, 2014;Kanko et al, 2021;Pagnon et al, 2021;Stenum et al, 2021;Takeda et al, 2021;Vafadar et al, 2021), running (Corazza et al, 2006;Macpherson et al, 2016;Pagnon et al, 2021), gymnastics tasks (Corazza et al, 2006(Corazza et al, , 2010Mündermann et al, 2007), and clinical evaluations (Eltoukhy et al, 2017;Mauntel et al, 2021). To date, the highest accuracy with markerless motion capture has been achieved when fitting a prior articulated model to a 3D surface visual hull reconstruction using matching algorithms (Corazza et al, 2006(Corazza et al, , 2007(Corazza et al, , 2008(Corazza et al, , 2010Mündermann et al, 2006bMündermann et al, , 2007.…”

Section: Strengths Agreement Between Markerless and Marker-based Systemsmentioning

confidence: 99%

“…To date, the highest accuracy with markerless motion capture has been achieved when fitting a prior articulated model to a 3D surface visual hull reconstruction using matching algorithms (Corazza et al, 2006(Corazza et al, , 2007(Corazza et al, , 2008(Corazza et al, , 2010Mündermann et al, 2006bMündermann et al, , 2007. More recently, however, the application of deep learning algorithms, keypoint detection approaches for biomechanical assessment are beginning to show similar or greater accuracies and illustrate significant promise for the future of markerless motion capture in the sports medicine domain (Drazan et al, 2021;Kanko et al, 2021;Needham et al, 2021;Pagnon et al, 2021;Stenum et al, 2021;Vafadar et al, 2021). It is important to note that the majority of validation studies utilizing markerless motion capture to assess joint kinematics evaluate relatively slow movements such as walking, or single plane motions such as the sagittal plane during jumping.…”

Section: Strengths Agreement Between Markerless and Marker-based Systemsmentioning

confidence: 99%

“…Unfortunately, the research into the development and improvement of these algorithms is outside the scope for most biomechanics research and, thus, the applicability of several of these algorithms for biomechanical use is still widely unknown (Mündermann et al, 2006a;Colyer et al, 2018). That being said, more recently researchers have begun to evaluate the more popular human pose estimation algorithms (e.g., OpenPose, DeepLabCut, AlphaPose) for their accuracy when applying them to the human movement sciences domain (Drazan et al, 2021;Needham et al, 2021;Pagnon et al, 2021;Stenum et al, 2021;Takeda et al, 2021) and perhaps with the growing popularity of applying markerless motion capture to the sports medicine domain, there will be a push for more biomechanically accurate algorithms. The benefit of using machine learningbased algorithms for estimating human movement is that these algorithms can be continually refined.…”

Section: Use Of Machine Learning Algorithmsmentioning

confidence: 99%

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A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport

Armitano-Lago

Dominic

Kiefer

2022

Front. Sports Act. Living

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Markerless motion capture systems are promising for the assessment of movement in more real world research and clinical settings. While the technology has come a long way in the last 20 years, it is important for researchers and clinicians to understand the capacities and considerations for implementing these types of systems. The current review provides a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis related to the successful adoption of markerless motion capture technology for the assessment of lower-limb musculoskeletal kinematics in sport medicine and performance settings. 31 articles met the a priori inclusion criteria of this analysis. Findings from the analysis indicate that the improving accuracy of these systems via the refinement of machine learning algorithms, combined with their cost efficacy and the enhanced ecological validity outweighs the current weaknesses and threats. Further, the analysis makes clear that there is a need for multidisciplinary collaboration between sport scientists and computer vision scientists to develop accurate clinical and research applications that are specific to sport. While work remains to be done for broad application, markerless motion capture technology is currently on a positive trajectory and the data from this analysis provide an efficient roadmap toward widespread adoption.

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Computational Ethology: Short Review of Current Sensors and Artificial Intelligence Based Methods

Aguilar-Moreno

Graña

2023

Communications in Computer and Information Science

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Moving outside the lab: Markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump

Cited by 59 publications

References 30 publications

Long-Term Motor Learning in the “Wild” With High Volume Video Game Data

Long-Term Motor Learning in the “Wild” With High Volume Video Game Data

A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport

Computational Ethology: Short Review of Current Sensors and Artificial Intelligence Based Methods

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