Abstract The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R -squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system.
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Predicting overstriding with wearable IMUs during treadmill and overground running
Running injuries are prevalent, but their exact mechanisms remain unknown largely due to limited real-world biomechanical analysis. Reducing overstriding, the horizontal distance that the foot lands ahead of the body, may be relevant to reducing injury risk. Here, we leverage the geometric relationship between overstriding and lower extremity sagittal segment angles to demonstrate that wearable inertial measurement units (IMUs) can predict overstriding during treadmill and overground running in the laboratory. Ten recreational runners matched their strides to a metronome to systematically vary overstriding during constant-speed treadmill running and showed similar overstriding variation during comfortable-speed overground running. Linear mixed models were used to analyze repeated measures of overstriding and sagittal segment angles measured with motion capture and IMUs. Sagittal segment angles measured with IMUs explained 95% and 98% of the variance in overstriding during treadmill and overground running, respectively. We also found that sagittal segment angles measured with IMUs correlated with peak braking force and explained 88% and 80% of the variance during treadmill and overground running, respectively. This study highlights the potential for IMUs to provide insights into landing and loading patterns over time in real-world running environments, and motivates future research on feedback to modify form and prevent injury.
more » « less- PAR ID:
- 10495593
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Objective: To evaluate the differences between walking on an advanced robotic locomotion interface called the Treadport and walking overground with healthy subjects.
Background: Previous studies have compared treadmill-based and overground walking in terms of gait parameters. The Treadport’s unique features including self-selected speed capability, large belt, kinesthetic force feedback, and virtual reality environment distinguish it from other locomotion interfaces and could provide a natural walking experience for the users.
Method: Young, healthy subjects ( N = 17) walked 10 meters 10 times each for both overground and the Treadport environments. Comparison between walking conditions used spatiotemporal and kinematic parameters. In addition, electromyographic data was collected for five of the 17 subjects to compare muscle activity between the two conditions.
Results: Gait on the Treadport was found to have no significant differences ( p > .05) with overground walking in terms of hip and knee joint angles, cadence and stride length and stride speed, and muscle activation of the four muscle groups measured. Differences ( p < .05) were observed in ankle dorsiflexion which was reduced by 2.47 ± 0.01 degrees on the Treadport.
Conclusion: Walking overground and on the Treadport is highly correlated and not significantly different in 13 of 14 parameters.
Application: This study suggests that the Treadport creates an environment for natural walking experience, where natural gait of users is almost preserved, with great potential to be useful for other applications, such as gait rehabilitation of individuals with walking impairments.
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To fully understand brain processes in the real world, it is necessary to record and quantitatively analyse brain processes during real world human experiences. Mobile electroencephalography (EEG) and physiological data sensors provide new opportunities for studying humans outside of the laboratory. The purpose of this study was to document data from high-density EEG and mobile physiological sensors while humans performed a visual search task both on a treadmill in a laboratory setting and overground in a natural outdoor setting. The data set includes 49 young, healthy participants on an outdoor arboretum path and on a treadmill in a laboratory with a large virtual reality screen. The data provide a valuable research tool for scientists interested in signal processing, electrocortical brain processes, mobile brain imaging, and brain-computer interfaces based on mobile EEG. Given the comparison data between laboratory and real world conditions, researchers can test the viability of new processing algorithms across conditions or investigate changes in electrocortical activity related to behavioural dynamics coded into the data.more » « less
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Step length is a critical gait parameter that allows a quantitative assessment of gait asymmetry. Gait asymmetry can lead to many potential health threats such as joint degeneration, difficult balance control, and gait inefficiency. Therefore, accurate step length estimation is essential to understand gait asymmetry and provide appropriate clinical interventions or gait training programs. The conventional method for step length measurement relies on using foot-mounted inertial measurement units (IMUs). However, this may not be suitable for real-world applications due to sensor signal drift and the potential obtrusiveness of using distal sensors. To overcome this challenge, we propose a deep convolutional neural network-based step length estimation using only proximal wearable sensors (hip goniometer, trunk IMU, and thigh IMU) capable of generalizing to various walking speeds. To evaluate this approach, we utilized treadmill data collected from sixteen able-bodied subjects at different walking speeds. We tested our optimized model on the overground walking data. Our CNN model estimated the step length with an average mean absolute error of 2.89 ± 0.89 cm across all subjects and walking speeds. Since wearable sensors and CNN models are easily deployable in real-time, our study findings can provide personalized real-time step length monitoring in wearable assistive devices and gait training programs.more » « less
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Background: Athletes, especially female athletes, experience high rates of tibial bone stress injuries (BSIs). Knowledge of tibial loads during walking and running is needed to understand injury mechanisms and design safe running progression programs.
Purpose: To examine tibial loads as a function of gait speed in male and female runners.
Study Design: Controlled laboratory study.
Methods: Kinematic and kinetic data were collected on 40 recreational runners (20 female, 20 male) during 4 instrumented gait speed conditions on a treadmill (walk, preferred run, slow run, fast run). Musculoskeletal modeling, using participant-specific magnetic resonance imaging and motion data, was used to estimate tibial stress. Peak tibial stress and stress-time impulse were analyzed using 2-factor multivariate analyses of variance (speed*sex) and post hoc comparisons (α = .05). Bone geometry and tibial forces and moments were examined.
Results: Peak compression was influenced by speed ( P < .001); increasing speed generally increased tibial compression in both sexes. Women displayed greater increases in peak tension ( P = .001) and shear ( P < .001) than men when transitioning from walking to running. Further, women displayed greater peak tibial stress overall ( P < .001). Compressive and tensile stress-time impulse varied by speed ( P < .001) and sex ( P = .006); impulse was lower during running than walking and greater in women. A shear stress-time impulse interaction ( P < .001) indicated that women displayed greater impulse relative to men when changing from a walk to a run. Compared with men, women displayed smaller tibiae ( P < .001) and disproportionately lower tibial forces ( P≤ .001-.035).
Conclusion: Peak tibial stress increased with gait speed, with a 2-fold increase in running relative to walking. Women displayed greater tibial stress than men and greater increases in stress when shifting from walking to running. Sex differences appear to be the result of smaller bone geometry in women and tibial forces that were not proportionately lower, given the womens’ smaller stature and lower mass relative to men.
Clinical Relevance: These results may inform interventions to regulate running-related training loads and highlight a need to increase bone strength in women. Lower relative bone strength in women may contribute to a sex bias in tibial BSIs, and female runners may benefit from a slower progression when initiating a running program.
