Human Walking: The Gait Cycle

Electrostatic detection is remarkably developed and has been employed to detect human activity for years. In this paper, an induction electrostatic detector is designed, and used to measure human walking signals. The gait signals of totally six segments of the same object are measured in the experiment. An algorithm is proposed to obtain accurate gait cycle. The original signals are transformed to correlation coefficient series. Peaks of correlation coefficient series is picked out as the same phase point instead of peaks of walking signal. The time between very second peaks is defined as gait cycle of object. Based on the gait cycle, we discussed the recurrence property as the characteristics of human beings. It is expected that the slope of the recurrence line can be used as an index, and will indicate the personal particularity of objects in detection and their physical conditions.

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Human-Inspired Algebraic Curves for Wearable Robot Control

Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare ◽

10.1115/dscc2018-9061 ◽

2018 ◽

Cited By ~ 2

Author(s):

Alireza Mohammadi ◽

Robert D. Gregg

Keyword(s):

Robot Control ◽

Gait Cycle ◽

Algebraic Curves ◽

Controller Design ◽

Rehabilitation Robotics ◽

Relative Degree ◽

Human Walking ◽

Wearable Robot ◽

Walking Gait ◽

Robot Controller

Having unified representations of human walking gait data is of paramount importance for wearable robot control. In the rehabilitation robotics literature, control approaches that unify the gait cycle of wearable robots are more appealing than the conventional approaches that rely on dividing the gait cycle into several periods, each with their own distinct controllers. In this article we propose employing algebraic curves to represent human walking data for wearable robot controller design. In order to generate algebraic curves from human walking data, we employ the 3L fitting algorithm, a tool developed in the pattern recognition literature for fitting implicit polynomial curves to given datasets. For an impedance model of the knee joint motion driven by the hip angle signal, we provide conditions by which the generated algebraic curves satisfy a robust relative degree condition throughout the entire walking gait cycle. The robust relative degree property makes the algebraic curve representation of walking gaits amenable to various nonlinear output tracking controller design techniques.

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Stance and swing phase costs in human walking

Journal of The Royal Society Interface ◽

10.1098/rsif.2010.0084 ◽

2010 ◽

Vol 7 (50) ◽

pp. 1329-1340 ◽

Cited By ~ 102

Author(s):

Brian R. Umberger

Keyword(s):

Stance Phase ◽

Gait Cycle ◽

Metabolic Cost ◽

Swing Phase ◽

Metabolic Energy ◽

Human Walking ◽

Total Cost ◽

Support Costs ◽

Stride Rate ◽

Single Limb Support

Leg swing in human walking has historically been viewed as a passive motion with little metabolic cost. Recent estimates of leg swing costs are equivocal, covering a range from 10 to 33 per cent of the net cost of walking. There has also been a debate as to whether the periods of double-limb support during the stance phase dominate the cost of walking. Part of this uncertainty is because of our inability to measure metabolic energy consumption in individual muscles during locomotion. Therefore, the purpose of this study was to investigate the metabolic cost of walking using a modelling approach that allowed instantaneous energy consumption rates in individual muscles to be estimated over the full gait cycle. At a typical walking speed and stride rate, leg swing represented 29 per cent of the total muscular cost. During the stance phase, the double-limb and single-limb support periods accounted for 27 and 44 per cent of the total cost, respectively. Performing step-to-step transitions, which encompasses more than just the double-support periods, represented 37 per cent of the total cost of walking. Increasing stride rate at a constant speed led to greater double-limb support costs, lower swing phase costs and no change in single-limb support costs. Together, these results provide unique insight as to how metabolic energy is expended over the human gait cycle.

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Differences Between Local and Orbital Dynamic Stability During Human Walking

Journal of Biomechanical Engineering ◽

10.1115/1.2746383 ◽

2006 ◽

Vol 129 (4) ◽

pp. 586-593 ◽

Cited By ~ 122

Author(s):

Jonathan B. Dingwell ◽

Hyun Gu Kang

Keyword(s):

Gait Cycle ◽

Sagittal Plane ◽

Orbital Stability ◽

Human Walking ◽

Joint Angles ◽

Global Instability ◽

Small Perturbations ◽

Floquet Multipliers ◽

Plane Joint ◽

Condition Interaction

Currently there is no commonly accepted way to define, much less quantify, locomotor stability. In engineering, “orbital stability” is defined using Floquet multipliers that quantify how purely periodic systems respond to perturbations discretely from one cycle to the next. For aperiodic systems, “local stability” is defined by local divergence exponents that quantify how the system responds to very small perturbations continuously in real time. Triaxial trunk accelerations and lower extremity sagittal plane joint angles were recorded from ten young healthy subjects as they walked for 10min over level ground and on a motorized treadmill at the same speed. Maximum Floquet multipliers (Max FM) were computed at each percent of the gait cycle (from 0% to 100%) for each time series to quantify the orbital stability of these movements. Analyses of variance comparing Max FM values between walking conditions and correlations between Max FM values and previously published local divergence exponent results were computed. All subjects exhibited orbitally stable walking kinematics (i.e., magnitudes of Max FM<1.0), even though these same kinematics were previously found to be locally unstable. Variations in orbital stability across the gait cycle were generally small and exhibited no systematic patterns. Walking on the treadmill led to small, but statistically significant improvements in the orbital stability of mediolateral (p=0.040) and vertical (p=0.038) trunk accelerations and ankle joint kinematics (p=0.002). However, these improvements were not exhibited by all subjects (p⩽0.012 for subject × condition interaction effects). Correlations between Max FM values and previously published local divergence exponents were inconsistent and 11 of the 12 comparisons made were not statistically significant (r2⩽19.8%; p⩾0.049). Thus, the variability inherent in human walking, which manifests itself as local instability, does not substantially adversely affect the orbital stability of walking. The results of this study will allow future efforts to gain a better understanding of where the boundaries lie between locally unstable movements that remain orbitally stable and those that lead to global instability (i.e., falling).

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A Model of Gait Cycle Variability During Human Walking

Advances in Cognitive Neurodynamics (V) - Advances in Cognitive Neurodynamics ◽

10.1007/978-981-10-0207-6_13 ◽

2016 ◽

pp. 79-84 ◽

Cited By ~ 1

Author(s):

Chunjiang Fu ◽

Yasuyuki Suzuki ◽

Ken Kiyono ◽

Taishin Nomura

Keyword(s):

Gait Cycle ◽

Human Walking

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Quantifying Dynamic Characteristics of Human Walking for Comprehensive Gait Cycle

Journal of Biomechanical Engineering ◽

10.1115/1.4024755 ◽

2013 ◽

Vol 135 (9) ◽

Cited By ~ 36

Author(s):

Carlotta Mummolo ◽

Luigi Mangialardi ◽

Joo H. Kim

Keyword(s):

Dynamic Characteristics ◽

Gait Cycle ◽

Center Of Mass ◽

The Body ◽

Human Walking ◽

Static Balance ◽

Time Varying ◽

Biped Walking ◽

Foot Model ◽

Normal Human

Normal human walking typically consists of phases during which the body is statically unbalanced while maintaining dynamic stability. Quantifying the dynamic characteristics of human walking can provide better understanding of gait principles. We introduce a novel quantitative index, the dynamic gait measure (DGM), for comprehensive gait cycle. The DGM quantifies the effects of inertia and the static balance instability in terms of zero-moment point and ground projection of center of mass and incorporates the time-varying foot support region (FSR) and the threshold between static and dynamic walking. Also, a framework of determining the DGM from experimental data is introduced, in which the gait cycle segmentation is further refined. A multisegmental foot model is integrated into a biped system to reconstruct the walking motion from experiments, which demonstrates the time-varying FSR for different subphases. The proof-of-concept results of the DGM from a gait experiment are demonstrated. The DGM results are analyzed along with other established features and indices of normal human walking. The DGM provides a measure of static balance instability of biped walking during each (sub)phase as well as the entire gait cycle. The DGM of normal human walking has the potential to provide some scientific insights in understanding biped walking principles, which can also be useful for their engineering and clinical applications.

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