By Kelly Robb

Plantar intrinsic foot muscles are commonly described as acting as one ‘functional unit’. However, previous human studies of EMG recordings of abductor hallucis and flexor digitorum brevis during walking  have revealed that these muscles have unique onset/offset patterns during the gait cycle. This suggests that they may function more independently than once thought. To our knowledge, there has been no report of EMG recording of the transverse head of adductor hallucis, another plantar intrinsic foot muscle, during walking. In this study, we therefore aimed to 1) develop a fine-wire EMG technique to record EMG of the transverse head of adductor hallucis during gait, and 2) examine this muscle’s functional role during walking.

We recorded bipolar fine-wire EMG of the transverse head of adductor hallucis in 19 feet of 10 young adults during walking. Under ultrasound guidance, we inserted fine-wire electrodes into the transverse head of adductor hallucis muscle via a dorsal forefoot insertional approach. Participants then completed a series of level walking trials over a custom-made walking platform, designed to allow for manipulation of the flooring surface compliance between hard and soft foam. This flooring compliance change modified the muscular demands of the transverse head of adductor hallucis during the stance phase of the gait cycle. We compared and reported the ensemble averages between both flooring surfaces and typical phasic EMG activity of the transverse head of adductor hallucis during level walking.

We found that the transverse head of adductor hallucis appears to contract, neutralizing the role of the tibialis anterior, and acting as a forefoot stabilizer during the propulsive phase of gait. Even more intriguing, the demands of this muscle appear to change when varying the flooring compliance (Figure). These preliminary insights into the functional role of the transverse head of adductor hallucis during walking may prove informative to clinicians treating common forefoot pathologies, including hallux valgus and metatarsalgia symptoms. These results may also be useful for the design of foot orthoses and/or footwear to alleviate the muscular demands of the transverse head of adductor hallucis during walking. Future researchers are encouraged to adopt this fine-wire technique and advance transverse head of adductor hallucis EMG literature across different foot shapes and participants experiencing pathological forefoot deformities.

Figure: Representative data of the EMG bursting pattern of the transverse head of adductor hallucis during walking. When walking on a hard surface (top), peak activity occurs at approximately 15% and 60% of the gait cycle, corresponding to initial contact and propulsion phases of gait. When walking on the softer foam (bottom), there is an absence of EMG activity at initial contact, with the retention of similar bursts in EMG at propulsion.

Publication

Robb, Kelly A., Melady, Hope D. & Perry, Stephen D. (2021) Fine-wire electromyography of the transverse head of adductor hallucis during locomotion. Gait & Posture, 85, 7-13. https://doi.org/10.1016/j.gaitpost.2020.12.020

About the Author

By Maud van den Bogaart

Falls are a major cause of injury and death in older people (1). Based on his appearance, and according to common belief, Santa Claus has been estimated to be around 1750 years of age. Extrapolating the known relationship between age and fall risk, Santa Claus should have an extremely high risk of falling, which is aggravated by his night shift work under winter conditions. Additionally, carrying a load, such as a sack with presents, changes the gait pattern in a way that increases fall risk (2). A fall may preclude Santa from performing his annual chore and puts the happiness of millions of children at risk.

A sensitive parameter to detect fall risk is a person’s gait variability, defined as the stride-to-stride variations of gait kinematics. Indeed, older people at risk of falls are known to walk with increased gait variability compared to their counterparts with a lower fall risk (3). Unfortunately, to date, Santa’s natural habitat lacks a gait analysis setup to quantify his gait variability and assess his fall risk. Moreover, his busy schedule precludes a visit to a gait lab. Therefore, estimates of Santa’s fall risk are based on extrapolation and hence likely unreliable. New techniques based on deep learning, with a high degree of automatization, can assess gait variability in a natural environment from simple video-recordings (4). Using such novel methodology would allow to assess Santa in his natural habitat whilst not hampering his Christmas chores. This markerless method (DeepLabCut) has significant advantages over laboratory-based optoelectronic gait analysis, in that it is free, open-source, applicable in the participant’s natural environment, and requires only a video camera. Therefore, it is likely to be particularly useful for routine gait analysis in clinical settings.

Using DeepLabCut, we investigated Santa’s gait and gait variability, to assess his fall risk using a, presumably authentic, video we found on YouTube (Figure 1). In spite of his likely extremely old age, Santa’s gait variability puts him in a considerably younger age group, with values comparable to those of people between the age of 65 and 70 years old (2, 5). Hence, Santa’s gait variability and therefore fall risk could not be linearly extrapolated with age. Additionally, Santa adopted a different posture and increased gait variability when carrying presents, which was also similar to that of older people aged 65 –to 70 years (Figure 1). Carrying presents indeed increased Santa’s gait variability, which suggests a higher fall risk while making people merry during Christmas time (2).

In conclusion, we highly  recommended Santa to train his strength, balance and gait stability before the Christmas season and, if possible, to spread the delivery of presents over a longer period to reduce his risk of falling. The use of deep learning to analyze gait in natural environments and clinical settings is very promising and I expect that it will revolutionize the field of rehabilitation medicine.

Figure 1. Workflow of the gait analysis of video-recordings of Santa Claus walking with and without a Christmas sack with presents in sagittal and frontal plane using deep learning (Deeplabcut; pretrained human gait model). Video-recordings of Santa Claus during one of his practice runs with and without a Christmas sack with presents were collected (https://youtu.be/U9eUqR7uXEE, https://youtu.be/Pbfp04UCMCs, https://youtu.be/phpm0AF3AO4, https://youtu.be/dvn5qujgrjE). The DeepLabCut human-pretrained model was used to retrieve the 2D locations of anatomical landmarks (e.g. ankle, knee, hip and shoulder) during walking (https://github.com/DeepLabCut/DeepLabCut/blob/master/examples/COLAB_DLC_ModelZoo.ipynb). Stride length, time and width were defined by the 2D locations of the ankle joint centers. Seven strides were analyzed per video. Differences in stride length, width and time variability between conditions were tested for significance using the Levene’s Test of Homogeneity of Variance (α=0.05).

References

1. Haagsma JA, Graetz N, Bolliger I, Naghavi M, Higashi H, Mullany EC, et al. The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013. Inj Prev. 2016;22(1):3-18.
2. Walsh GS, Low DC, Arkesteijn M. Effect of stable and unstable load carriage on walking gait variability, dynamic stability and muscle activity of older adults. J Biomech. 2018;73:18-23.
3. Callisaya ML, Blizzard L, Schmidt MD, McGinley JL, Srikanth VK. Ageing and gait variability–a population-based study of older people. Age Ageing. 2010;39(2):191-7.
4. Nath T, Mathis A, Chen AC, Patel A, Bethge M, Mathis MW. Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nat Protoc. 2019;14(7):2152-76.
5. Hollman JH, McDade EM, Petersen RC. Normative spatiotemporal gait parameters in older adults. Gait Posture. 2011;34(1):111-8.

About the Author

By Dr Tiphanie Raffegeau, Dr Brad Fawver and Dr William Young

Fear of falling profoundly affects a person’s balance control and walking, and paradoxically, might increase their risk of falling. The influence of such mobility-related anxiety is often tested by having people stand on the edge of an elevated platform. Based on this approach, our team developed a virtual reality (VR) program to induce mobility-related anxiety and examine the effects of simulated balance threats on balance and walking behavior.

Working in collaboration with software developers in the Spencer S. Eccles Health Science Library at the University of Utah led by Ben Engel, we created a simulation inspired by a popular game: “Richies Plank Experience.” Designed with Unity3D software (Unity Technologies, San Francisco, CA. USA ), a realistic rendering of a local outdoor setting was incorporated to deliver an anxiety-inducing ‘plank-walking’ simulation (Figure 1). The program has a plank-matching feature which uses the handheld controllers to capture four locations at each corner of any straight walkway. The spatial coordinates match the dimensions of the real and virtual walkways so what people see in VR is the same as what they feel when their feet touch the real platform edges. The feeling is enhanced by wearing a pair of virtual sneakers (system accessories worn around the ankle) that track their foot motion in the VR simulation. Finally, the program features an ‘elevator’ function, that raises the walkway level at customizable height and speed.

The effectiveness of the VR height illusion, shown by performance changes from low to high VR heights, is documented in slower turning behavior and direction-dependent standing balance (see Raffegeau et al., 2020a, 2020b). The success of the VR height illusion is supported by increased self-reported cognitive (e.g., worry) and somatic (e.g., tension) anxiety, more mental effort dedicated to the task, and less confidence in one’s ability to complete the task at VR high heights (Raffegeau et al., 2020a). We also detected increased heart rate variability (Coefficient of Variation) when participants stood at high elevations using a commercial heart rate monitor (Polar M430) (Raffegeau et al., 2020b) providing more support for the effectiveness of our paradigm.

In the future, we plan to improve the VR height illusion by determining the best method of delivering the experiment (e.g., timing, transportation to height, etc.) and scaling the virtual foot representation to each person. Future experiments will include obstacles to avoid and added cognitive tasks to study anxiety-related detriments to everyday mobility demands.

The program is accessible through Ben Engel’s github (see Raffegeau et al., 2020a).

Figure 1: Top: Progression of VR program development/design from initial version 1 (left) to current realistic version 2 (right) designed to represent an outdoor location in Park City, Utah. Note, the researcher’s user interface is shown. Bottom: the HTC Vive VR system equipment (left), a person fitted with the VR head mounted display walking on our path (middle), and the real-world path that is matched in the virtual world using the plank matching feature (right).

Low height VR walk video:

High height VR walk video:

Publications

Raffegeau, T.E., Fawver, B., Young, W. R., Williams, A. M., Lohse, K. R., and Fino, P. C. (2020a) The direction of postural threat alters standing balance control when standing at virtual elevation, Experimental Brain Research, 238(11), 2653 2663. https://www.sciencedirect.com/science/article/pii/S0966636220300072?via%3Dihub

Raffegeau, T.E., Fawver, B., Clark, M., Engle, B., Young, W. R., Williams, A. M., Lohse, K. R., and Fino, P. C. (2020b) The feasibility of using virtual reality to induce mobility-related anxiety during turning, Gait & Posture, 77, 6-13. https://doi.org/10.1016/j.gaitpost.2020.01.006

About the Author

By Dr Matthew King

Investigations of biomechanics are an ever-growing field of scientific research, with the assessment of “gait” being a popular choice to inform both clinical and experimental directions. However, what cannot be gleaned from the term “gait” is the type of locomotion that is being studied. Are the participants walking, running, crawling or jogging?

Initially, the study of human movement was so novel, that the term “gait” may have been all-encompassing. Over time, the term “gait” has been substituted to describe walking. However, “gait” is not mutually exclusive to walking, nor humans. Horses gallop and trot, fish swim, and humans run (just to name a few). What is not know is the task (or type of locomotion) being performed – as “gait” is the pattern produced during a mode of locomotion, not locomotion itself.

To demonstrate the ambiguous use of the term “gait”, our recent publication reviewed the 319 papers published in Gait and Posture in 2019. In summary, approximately half of the papers that directly evaluated locomotion described the task as “gait” in the title with no quantifier (noun, verb, or gerund) for the actual task being performed (FIG).

Our primary goal for completing this paper and outlining this information, is to shed light on various, and often incorrect, use of the word “gait”. Together, as clinicians, academics, and scientists alike, we must rise to the challenge in being more literal in what we study and why. In particular with the use of the word “gait”. In doing so, search strategies will be streamlined, titles will provide insight into the task studied, and the ability for individuals to locate relevant research studies to further their evidence-based practice will be simplified.

Figure. Use of the word “gait” in the titles of papers published in Gait and Posture in 2019

Publication

Gill, N., Kean, C., & King, M. G. (2020). Let’s take the dog for a gait…. Gait & Posture, 79, pp. 1-2. doi: https://doi.org/10.1016/j.gaitpost.2020.03.018

About the Author

Among older adults, gait impairments threaten functional independence. Gait decline is associated with, and predictive of, numerous adverse health outcomes. These include falls, mobility impairment, cognitive decline, dementia, and even mortality. Traditional laboratory and clinic-based evaluations of gait have provided important insights but are limited by the “snapshot” nature in an environment that can only attempt to mimic every-day settings. Wearable technology enables continuous monitoring of gait in free-living environments and represents an ecologically valid, complementary way of measuring gait function. Previous findings show that in-lab and real-world measures of gait differ but the how and why is unclear.  As a step towards a better understanding of these gaps, we directly compared in-lab usual-walking and dual-task walking (i.e., while serially subtracting 3 from a predefined 3-digit number) to daily-living measures of gait.

One hundred and fifty older adults with a history of falling participated in the study. Gait speed was derived from an accelerometer placed on the lower back during both daily-living and in-lab settings. A histogram of all 30 seconds daily-living walking bouts was determined for each subject. Then, each subject’s typical (percentile 50, median), worst (percentile 10) and best (percentile 90) values over the week were determined and compared to gait speed during in-lab usual-walking and dual-task walking.

As expected, in-lab gait speed was slower during dual-task walking (94.7±22.2 cm/s), compared to usual-walking (100.5±21.5 cm/s). In-lab gait speed during usual-walking was significantly different compared to the worst (68.5±10.1 cm/s), typical (96.5±17.9 cm/s) and best (117.2±22.5 cm/s) daily-living values. Gait speed during in-lab dual-task walking was similar to typical daily-living values (see Figure 1). ICC assessment and Bland-Altman plots indicated that in-lab values do not reliably reflect the daily-walking values.

These results suggest that gait values measured during relatively long daily-living walking bouts are more comparable to those obtained in the lab during a challenging dual-task condition. Still, the values measured in the lab do not reliably reflect daily-living measures and potentially reflect different constructs of what a person can do versus what they actually do at home. This suggests that an older adult’s typical daily-life gait cannot be estimated by simply measuring walking in a structured, laboratory setting. Dual-tasking partially accounts for the differences between in-lab and free-living walking but additional work is needed to better understand the multiple factors that contribute to these differences and their impact on assessment and prediction of changes in mobility in older adults.

Figure: Effect of setting on gait speed

Publication

Hillel, Inbar, et al. “Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring.” European Review of Aging and Physical Activity 16.1 (2019): 6.‏ doi: 10.1186/s11556-019-0214-5

About the Author

By Dr Dimitra Blana.

Computer code is increasingly used to analyse human movement in the clinic. Motion capture, often combined with electromyography and imaging, produces large amounts of data that need to be appropriately analysed and visualised to provide clinical insights. As a result, coding has become part of the clinical workflow, and manufacturers such as Vicon have added support for programming languages (e.g. Matlab and Python) in their software.

Adding customized code can enhance the outcomes of movement analysis, but not all researchers and laboratory staff have the skills or time to learn how to develop their own code. Some people would be interested in learning but do not know how to start, as it is not easy to find beginner-friendly coding tutorials for movement analysis. Others do not have the time or desire to code themselves but would like to be able to quickly find and use code suitable for their analyses. Finally, people with technical skills often have to “reinvent the wheel”, as they are unaware of code already developed by other laboratories.

Discussing this with colleagues at the Orthotic Research & Locomotor Assessment Unit in Oswestry, a UK clinical movement analysis lab, we decided to create a collaborative platform, where anyone working on movement analysis can share code and documentation. We successfully applied to CMAS (the Clinical Movement Analysis Society of the UK and Ireland) for a small grant, and the CMAS open-code project was born.

We are building our project on Github, the most commonly used software development platform. With guidance from the Mozilla Foundation Open Leaders Programme, we are aiming to design an inclusive project that is welcoming to people of all backgrounds and levels of coding experience. There is still a lot to do, but the initial response from the community has been very encouraging: we have an increasing number of contributors who are sharing code, and are using our channels to ask questions, discuss common issues and meet fellow movement analysis enthusiasts.

Our aim is for this platform to grow and become an international hub of collaboration on coding for movement analysis. We invite all of you to get involved: join our mailing list, our Slack workspace, have a look at our code of conduct and contributing guidelines, and start sharing!

Figure: Example of clinical movement analysis. Photo credit: Ed Chadwick

Publication

Opencode project link: https://cmasuki.github.io/

About the Author

by Dr Susanne van der Veen.

Gait analysis on a treadmill can provide data with a high resolution over a large number of repeated steps. Commercially available treadmills often incorporate force plates, which allow for the identification of steps based on excursion of the centre of pressure (CoP). Previous studies suggest that CoP gait event detection corresponds well with kinematic-derived detection of the same events, at least during steady-state gait in healthy people. However, the accuracy of gait event detection during enforced gait patterns and pathological gait has not yet been tested. To address this caveat, we validated gait event detection on a single force-plate embedded treadmill with the ability to project targets on the belt to elicit altered gait patterns. And, with the help of my Ph.D. supervisors and a very helpful reviewing process this resulted in my first ‘first’ author paper.

We collected gait data of young healthy adults and stroke survivors during target stepping on the C-Mill treadmill (MotekForcelink, Culenborg, The Netherlands). Gait events were detected using one kinetic and two kinematic methods: 1) vertices of centre of pressure (CoP) cyclograms, 2) vertical velocity and position of the heel marker, and 3) anterior velocity and position of the heel and toe markers. A comparison of these methods revealed that centre of pressure-based gait event detection was most accurate. The kinematic method 2 (vertical velocity of the heel marker) was unsuccessful in 20% and method 3 (anterior velocity of the heel marker) was unsuccessful in 7% when compared to method 1 (CoP based). Both kinematic algorithms detected gait events significantly earlier compared to CoP gait event detection, which can be explained by the fact that the foot is placed on the surface before it is actually loaded. In addition, the high percentage of gait events which were missed by kinematic methods suggest that CoP gait event detection may be more appropriate for altered gait patterns.

Traditionally, gait event detection is applied during steady-state walking. Because of the importance of both accurate detection of gait events in pathological gait and when adapting steps in response to environmental cues, we compared the performance of gait event detection methods during targeted stepping. Based on the results, we conclude that gait event detection can be done more accurately in pathological (paretic) gait as well as in adapted gait patterns (walking to enforced walking patterns) using a CoP-based gait event detection. Our findings suggest that treadmills with embedded force plates (like the C-Mill) can be readily implemented for gait analysis in research and clinical practice to allow for a wide variety of automated gait training.

Publication

van der Veen, S. M., Hammerbeck, U., Baker, R. J., & Hollands, K. L. (2018). Validation of gait event detection by centre of pressure during target stepping in healthy and paretic gait. Journal of Biomechanics. https://doi.org/10.1016/J.JBIOMECH.2018.07.039

About the Author