Phase resetting behavior in human gait is influenced by treadmill walking speed

Although lower limb strength becomes asymmetrical with age, past studies of aging effects on gait biomechanics have usually analyzed only one limb. This experiment measured how aging and treadmill surface influenced both dominant and nondominant step parameters in older (mean 74.0 y) and young participants (mean 21.9 y). Step-cycle parameters were obtained from 3-dimensional position/time data during preferred-speed walking for 40 trials along a 10 m walkway and for 10 minutes of treadmill walking. Walking speed (young 1.23 m/s, older 1.24 m/s) and step velocity for the two age groups were similar in overground walking but older adults showed significantly slower walking speed (young 1.26 m/s, older 1.05 m/s) and step velocity on the treadmill due to reduced step length and prolonged step time. Older adults had shorter step length than young adults and both groups reduced step length on the treadmill. Step velocity and length of older adults’ dominant limb was asymmetrically larger. Older adults increased the proportion of double support in step time when treadmill walking. This adaptation combined with reduced step velocity and length may preserve balance. The results suggest that bilateral analyses should be employed to accurately describe asymmetric features of gait especially for older adults.

Download Full-text

Effects of Treadmill Walking Speed on Lateral Gastrocnemius Muscle Firing

American Journal of Physical Medicine & Rehabilitation ◽

10.1097/01.phm.0000130033.91894.5a ◽

2004 ◽

Vol 83 (7) ◽

pp. 507-514 ◽

Cited By ~ 10

Author(s):

Edward A. Clancy ◽

Kevin D. Cairns ◽

Patrick O. Riley ◽

Melvin Meister ◽

D. Casey Kerrigan

Keyword(s):

Gastrocnemius Muscle ◽

Walking Speed ◽

Treadmill Walking ◽

Lateral Gastrocnemius

Download Full-text

Assessment of the Effects of Diabetes on Midfoot Joint Pressures Using a Robotic Gait Simulator

Foot & Ankle International ◽

10.3113/fai.2009.0767 ◽

2009 ◽

Vol 30 (8) ◽

pp. 767-772 ◽

Cited By ~ 15

Author(s):

Dong Gil Lee ◽

Brian L. Davis

Keyword(s):

Stance Phase ◽

Walking Speed ◽

Normal Population ◽

Human Gait ◽

Diabetic Patients ◽

Foot Deformity ◽

Charcot Neuroarthropathy ◽

Joint Contact ◽

Peak Pressures ◽

Contact Pressures

Background: One of the more serious diabetic complications is Charcot neuroarthropathy (CN), a disease that results in arch collapse and permanent foot deformity. However, very little is known about the etiology of CN. From a mechanical standpoint, it is likely that there is a “vicious circle” in terms of (i) arch collapse causing increased midfoot joint pressures, and (ii) increased joint contact pressures exacerbating the collapse of midfoot bones. This study focused on assessment of peak joint pressure difference between diabetic and non-diabetic cadaver feet during simulated walking. We hypothesized that joint pressures are higher for diabetics than normal population. Materials and Methods: Sixteen cadaver foot specimens (eight control and eight diabetic specimens) were used in this study. Human gait at 25% of typical walking speed (averaged stance duration of 3.2s) was simulated by a custom-designed Universal Musculoskeletal Simulator. Four medial midfoot joint pressures (the first metatarsocuneiform, the medial naviculocuneiform, the middle naviculocuneiform, and the first intercuneiform) were measured dynamically during full stance. Results: The pressures in each of the four measured midfoot joints were significantly greater in the diabetic feet ( p = 0.015, p = 0.025, p < 0.001, and p = 0.545, respectively). Conclusion: Across all four tested joints, the diabetic cadaver specimens had, on average, 46% higher peak pressures than the control cadaver feet during the simulated stance phase. Clinical Relevance: This finding suggests that diabetic patients could be predisposed to arch collapse even before there are visible signs of bone or joint abnormalities.

Download Full-text

Changes in Spatiotemporal Measures and Variability During User-Driven Treadmill, Fixed-Speed Treadmill, and Overground Walking in Young Adults: A Pilot Study

Journal of Applied Biomechanics ◽

10.1123/jab.2020-0109 ◽

2021 ◽

pp. 1-5

Author(s):

Hillary H. Holmes ◽

Randall T. Fawcett ◽

Jaimie A. Roper

Keyword(s):

Young Adults ◽

Motor Control ◽

Pilot Study ◽

Human Health ◽

Gait Cycle ◽

Walking Speed ◽

Treadmill Walking ◽

Overground Walking ◽

Control Patterns ◽

Integral Indicator

Walking is an integral indicator of human health commonly investigated while walking overground and with the use of a treadmill. Unlike fixed-speed treadmills, overground walking is dependent on the preferred walking speed under the individuals’ control. Thus, user-driven treadmills may have the ability to better simulate the characteristics of overground walking. This pilot study is the first investigation to compare a user-driven treadmill, a fixed-speed treadmill, and overground walking to understand differences in variability and mean spatiotemporal measures across walking environments. Participants walked fastest overground compared to both fixed and user-driven treadmill conditions. However, gait cycle speed variability in the fixed-speed treadmill condition was significantly lower than the user-driven and overground conditions, with no significant differences present between overground and user-driven treadmill walking. The lack of differences in variability between the user-driven treadmill and overground walking may indicate that the user-driven treadmill can better simulate the variability of overground walking, potentially leading to more natural adaptation and motor control patterns of walking.

Download Full-text

Stability-normalised walking speed: A new approach for human gait perturbation research

Journal of Biomechanics ◽

10.1016/j.jbiomech.2019.02.016 ◽

2019 ◽

Vol 87 ◽

pp. 48-53 ◽

Cited By ~ 7

Author(s):

Christopher McCrum ◽

Paul Willems ◽

Kiros Karamanidis ◽

Kenneth Meijer

Keyword(s):

Walking Speed ◽

Human Gait ◽

New Approach

Download Full-text

Measuring Gait-Event-Related Brain Potentials (gERPs) during Instructed and Spontaneous Treadmill Walking: Technical Solutions and Automated Classification through Artificial Neural Networks

Applied Sciences ◽

10.3390/app10165405 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5405

Author(s):

Cornelia Herbert ◽

Michael Munz

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Pilot Study ◽

Treadmill Walking ◽

Human Gait ◽

Brain Potentials ◽

Eeg Data ◽

Continuous Eeg ◽

Artificial Neural ◽

Technical Solutions

The investigation of the neural correlates of human gait, as measured by means of non-invasive electroencephalography (EEG), is of central importance for the understanding of human gait and for novel developments in gait rehabilitation. Particularly, gait-event-related brain potentials (gERPs) may provide information about the functional role of cortical brain regions in human gait control. The purpose of this paper is to explore possible experimental and technical solutions for time-sensitive analysis of human gait ERPs during spontaneous and instructed treadmill walking. A solution (hardware/software) for synchronous recording of gait and EEG data was developed, tested and piloted. The solution consists of a custom-made USB synchronization interface, a time-synchronization module, and a data-merging module, allowing the temporal synchronization of recording devices, time-sensitive extraction of gait markers for the analysis of gERPs, and the training of artificial neural networks. In the present manuscript, the hardware and software components were tested with the following devices: A treadmill with an integrated pressure plate for gait analysis (zebris FDM-T) and an Acticap non-wireless 32-channel EEG system (Brain Products GmbH). The usability and validity of the developed solution was investigated in a pilot study (n = 3 healthy participants, n = 3 females, mean age = 22.75 years). The recorded continuous EEG data were segmented into epochs according to the detected gait markers for the analysis of gERPs. Finally, the EEG epochs were used to train a deep learning artificial neural network as classifier of gait phases. The results obtained in this pilot study, although preliminary, support the feasibility of the solution for the application of gait-related EEG analysis.

Download Full-text