Periodic Gait Research Articles

Accuracy between optical and inertial motion capture systems for assessing trunk speed during preferred gait and transition periods

Motion capture through inertial sensors is becoming popular, but its accuracy to describe kinematics during changes in walking speed is unknown. The aim of this study was to determine the accuracy of trunk speed extracted using an inertial motion system compared to a gold standard optical motion system, during steady walking and stationary periods. Eleven participants walked on pre-established paths marked on the floor. Between each lap, a 1-second stationary transition period at the initial position was included prior to the next lap. Resultant trunk speed during the walking and transition periods were extracted from an inertial (240 Hz sampling rate) and an optical system (120 Hz sampling rate) to calculate the agreement (Pearson’s correlation coefficient) and relative root mean square errors between both systems. The agreement for the resultant trunk speed between the inertial system and the optical system was strong (0.67 < r ≤ 0.9) for both walking and transition periods. Moreover, relative root mean square error during the transition periods was greater in comparison to the walking periods (>40% across all paths). It was concluded that trunk speed extracted from inertial systems have fair accuracy during walking, but the accuracy was reduced in the transition periods.

Human Movement Detection and Gait Periodicity Analysis via Channel State Information

In recent years, movement detection and gait recognition methods using different techniques emerge in an endless stream. On the one hand, wearable sensors need be worn by the detecting target and the method based on camera requires line of sight. On the other hand, radio frequency signals are easy to be impaired. In this paper, we propose a novel multi-layer filter of channel state information (CSI) to capture moving individuals in dynamic environments and analyze his/her gait periodicity. We design and evaluate an efficient CSI subcarrier feature difference to the multi-layer filtering method leveraging principal component analysis (PCA) and discrete wavelet transform (DWT) to eliminate the noises. Furthermore, we propose the profile matching mechanism for movement detection and the gait periodicity analysis mechanism for human gait. Experimental results in different environments indicate that our approach performs identification with an average accuracy of 94%.

GSI: efficient spatio-temporal template for human gait recognition

Human gait recognition is a challenging task in computer vision community. In order to represent the gait, the most common feature is a gait template. Many efficient templates have been developed recently, however, the effectiveness of the proposed motion models is still under investigation. A novel template feature, named gait salient image (GSI) is introduced in this paper. The main contribution of the proposed GSI is encoding the motion energy of gait into a single template. This idea is being conceptualised by applying appropriate spatio-temporal filter for extracting motion features and averaging it over a gait period. To show how GSI-based feature is being efficient, the proposed template is classified using PCA+LDA. Extensive experiments on popular gait databases reveal an improvement over the available methods in terms of efficiency and accuracy. The value of recognition rate is 58.44% for Rank1 and 76.60% for Rank5 based on the USF database.

Spontaneous Symmetry-Breaking in a Network Model for Quadruped Locomotion

Spontaneous symmetry-breaking proves a mechanism for pattern generation in legged locomotion of animals. The basic timing patterns of animal gaits are produced by a network of spinal neurons known as a Central Pattern Generator (CPG). Animal gaits are primarily characterized by phase differences between leg movements in a periodic gait cycle. Many different gaits occur, often having spatial or spatiotemporal symmetries. A natural way to explain gait patterns is to assume that the CPG is symmetric, and to classify the possible symmetry-breaking periodic motions. Pinto and Golubitsky have discussed a four-node model CPG network for biped gaits with [Formula: see text] symmetry, classifying the possible periodic states that can arise. A more specific rate model with this structure has been analyzed in detail by Stewart. Here we extend these methods to quadruped gaits, using an eight-node network with [Formula: see text] symmetry proposed by Golubitsky and coworkers. We formulate a rate model and calculate how the first steady or Hopf bifurcation depends on its parameters, which represent four connection strengths. The calculations involve a distinction between “real” gaits with one or two phase shifts (pronk, bound, pace, trot) and “complex” gaits with four phase shifts (forward and reverse walk, forward and reverse buck). The former correspond to real eigenvalues of the connection matrix, the latter to complex conjugate pairs. The partition of parameter space according to the first bifurcation, ignoring complex gaits, is described explicitly. The complex gaits introduce further complications, not yet fully understood. All eight gaits can occur as the first bifurcation from a fully synchronous equilibrium, for suitable parameters, and numerical simulations indicate that they can be asymptotically stable.

Self-synchronization and self-stabilization of 3D bipedal walking gaits

Abstract This paper seeks insight into stabilization mechanisms for periodic walking gaits in 3D bipedal robots. Based on this insight, a control strategy based on virtual constraints, which imposes coordination between joints rather than a temporal evolution, will be proposed for achieving asymptotic convergence toward a periodic motion. For planar bipeds with one degree of underactuation, it is known that a vertical displacement of the center of mass – with downward velocity at the step transition – inducesstability of a walking gait. This paper concerns the qualitative extension of this type of property to 3D walking with two degrees of underactuation. It is shown that a condition on the position of the center of mass in the horizontal plane at the transition between steps induces synchronization between the motions in the sagittal and frontal planes. A combination of the conditions for self-synchronization and vertical oscillations leads to stable gaits. The algorithm for self-stabilization of 3D walking gaits is first developed for a simplified model of a walking robot (an inverted pendulum with variable length legs), and then it is extended to a complex model of the humanoid robot Romeo using the notion of Hybrid Zero Dynamics. Simulations of the model of the robot illustrate the efficacy of the method and its robustness.

Computational stability of human knee joint at early stance in Gait: Effects of muscle coactivity and anterior cruciate ligament deficiency.

As one of the most complex and vulnerable structures of body, the human knee joint should maintain dynamic equilibrium and stability in occupational and recreational activities. The evaluation of its stability and factors affecting it is vital in performance evaluation/enhancement, injury prevention and treatment managements. Knee stability often manifests itself by pain, hypermobility and giving-way sensations and is usually assessed by the passive joint laxity tests. Mechanical stability of both the human knee joint and the lower extremity at early stance periods of gait (0% and 5%) were quantified here for the first time using a hybrid musculoskeletal model of the lower extremity. The roles of muscle coactivity, simulated by setting minimum muscle activation at 0-10% levels and ACL deficiency, simulated by reducing ACL resistance by up to 85%, on the stability margin as well as joint biomechanics (contact/muscle/ligament forces) were investigated. Dynamic stability was analyzed using both linear buckling and perturbation approaches at the final deformed configurations in gait. The knee joint was much more stable at 0% stance than at 5% due to smaller ground reaction and contact forces. Muscle coactivity, when at lower intensities (<3% of its maximum active force), increased dynamic stability margin. Greater minimum activation levels, however, acted asan ineffective strategy to enhance stability. Coactivation also substantially increased muscle forces, joint loads and ACL force and hence the risk of further injury and degeneration. A deficiency in ACL decreases total ACL force (by 31% at 85% reduced stiffness) and the stability margin of the knee joint at the heel strike. It also markedly diminishes forces in lateral hamstrings (by up to 39%) and contact forces on the lateral plateau (by up to 17%). Current work emphasizes the need for quantification of the lower extremity stability margin in gait.

Treadmill vs. overground walking: different response to physical interaction.

Rehabilitation of human motor function is an issue of growing significance, and human-interactive robots offer promising potential to meet the need. For the lower extremity, however, robot-aided therapy has proven challenging. To inform effective approaches to robotic gait therapy, it is important to better understand unimpaired locomotor control: its sensitivity to different mechanical contexts and its response to perturbations. The present study evaluated the behavior of 14 healthy subjects who walked on a motorized treadmill and overground while wearing an exoskeletal ankle robot. Their response to a periodic series of ankle plantar flexion torque pulses, delivered at periods different from, but sufficiently close to, their preferred stride cadence, was assessed to determine whether gait entrainment occurred, how it differed across conditions, and if the adapted motor behavior persisted after perturbation. Certain aspects of locomotor control were exquisitely sensitive to walking context, while others were not. Gaits entrained more often and more rapidly during overground walking, yet, in all cases, entrained gaits synchronized the torque pulses with ankle push-off, where they provided assistance with propulsion. Furthermore, subjects entrained to perturbation periods that required an adaption toward slower cadence, even though the pulses acted to accelerate gait, indicating a neural adaptation of locomotor control. Lastly, during 15 post-perturbation strides, the entrained gait period was observed to persist more frequently during overground walking. This persistence was correlated with the number of strides walked at the entrained gait period (i.e., longer exposure), which also indicated a neural adaptation.NEW & NOTEWORTHY We show that the response of human locomotion to physical interaction differs between treadmill and overground walking. Subjects entrained to a periodic series of ankle plantar flexion torque pulses that shifted their gait cadence, synchronizing ankle push-off with the pulses (so that they assisted propulsion) even when gait cadence slowed. Entrainment was faster overground and, on removal of torque pulses, the entrained gait period persisted more prominently overground, indicating a neural adaptation of locomotor control.

Nonholonomic virtual constraints and gait optimization for robust walking control

A key challenge in robotic bipedal locomotion is the design of feedback controllers that function well in the presence of uncertainty, in both the robot and its environment. This paper addresses the design of feedback controllers and periodic gaits that function well in the presence of modest terrain variation, without over-reliance on perception and a priori knowledge of the environment. Model-based design methods are introduced and subsequently validated in simulation and experiment on MARLO, an underactuated three-dimensional bipedal robot that is of roughly human size and is equipped with an inertial measurement unit and joint encoders. Innovations include an optimization method that accounts for multiple types of disturbances and a feedback control design that enables continuous velocity-based posture regulation via nonholonomic virtual constraints. Using a single continuously defined controller taken directly from optimization, MARLO traverses sloped sidewalks and parking lots, terrain covered with randomly thrown boards, and grass fields, all while maintaining average walking speeds between 0.9 and 0.98 m/s and setting a new precedent for walking efficiency in realistic environments.

Adjacent regenerative peripheral nerve interfaces produce phase-antagonist signals during voluntary walking in rats

BackgroundRegenerative Peripheral Nerve Interfaces (RPNIs) are neurotized muscle grafts intended to produce electromyographic signals suitable for motorized prosthesis control. Two RPNIs producing independent agonist/antagonist signals are required for each control axis; however, it is unknown whether signals from adjacent RPNIs are independent. The purpose of this work was to determine signaling characteristics from two adjacent RPNIs, the first neurotized by a foot dorsi-flexor nerve and the second neurotized by a foot plantar-flexor nerve in a rodent model.MethodsTwo Control group rats had electrodes implanted onto the soleus (tibial nerve) and extensor digitorum longus (peroneal nerve) muscles in the left hind limb. Two Dual-RPNI group rats had two separate muscles grafted to the left thigh and each implanted with electrodes: the extensor digitorum longus was neurotized with a transected fascicle from the tibial nerve, and the tibialis anterior was implanted with a transected peroneal nerve. Four months post-surgery, rats walked on a treadmill, were videographed, and electromyographic signals were recorded. Amplitude and periodicity of all signals relative to gait period were quantified. To facilitate comparisons across groups, electromyographic signals were expressed as a percent of total stepping cycle activity for each stance and swing gait phase. Independence between peroneal and tibial nerve activations were assessed by statistical comparisons between groups during stance and swing.ResultsElectromyographic activity for Control and Dual-RPNI rats displayed alternating activation patterns coinciding with stance and swing. Significant signal amplitude differences between the peroneal and tibial nerves were found in both the Control and Dual-RPNI groups. Non-inferiority tests performed on Dual-RPNI group signal confidence intervals showed that activation was equivalent to the Control group in all but the peroneal RPNI construct during stance. The similar electromyographic activity obtained for Control and RPNI suggests the latter constructs activate independently during both stance and swing, and contain minimal crosstalk.ConclusionsIn-vivo myoelectric RPNI activity encodes neural activation patterns associated with gait. Adjacent RPNIs neurotized with agonist/antagonist nerves display activity amplitudes similar to Control during voluntary walking. The distinct and expected activation patterns indicate the RPNI may provide independent signaling in humans, suitable for motorized prosthesis control.

3LP: A linear 3D-walking model including torso and swing dynamics

In this paper, we present a new mechanical model for biped locomotion, composed of three linear pendulums (one per leg and one for the whole upper body) to describe stance, swing and torso dynamics. In addition to a double support phase, this model has different actuation possibilities in the swing hip and stance ankle which produce a broad range of walking gaits. Without the need for numerical time-integration, closed form solutions help to find periodic gaits which could simply scale in certain dimensions to modulate the motion online. Thanks to linearity properties, the proposed model can potentially provide a computationally fast platform for model predictive controllers to predict the future and consider meaningful inequality constraints to ensure feasibility of the motion. Such a property comes from describing dynamics with joint torques directly and therefore, reflecting hardware limitations more precisely, even in the very abstract template space. The proposed model produces human-like torque and ground reaction force profiles, and thus, compared to point-mass models, it is more promising for the generation of dynamic walking trajectories. Despite being linear and lacking many features of human walking like center of mass excursion, knee flexion and ground clearance, we show that the proposed model can explain one of the main optimality trends in human walking, i.e. the nonlinear speed-frequency relationship. In this paper, we mainly focus on describing the model and its capabilities, comparing it with human data and calculating optimal human gait variables. Setting up control problems and advanced biomechanical analysis remains for future works.

The Biomechanical and Energetic Advantages of a Mediolaterally Wide Pelvis in Women.

Here, we argue that two key shifts in thinking are required to more clearly understand the selection pressures shaping pelvis evolution in female hominins: (1) the primary locomotor mode of female hominins was loaded walking in the company of others, and (2) the periodic gait of human walking is most effectively explained as a biomechanically controlled process related to heel-strike collisions that is tuned for economy and stability by properly-timed motor inputs (a model called dynamic walking). In the light of these two frameworks, the evidence supports differences between female and male upper-pelvic morphology being the result of the unique reproductive role of female hominins, which involved moderately paced, loaded walking in groups. Anat Rec, 300:764-775, 2017. © 2017 Wiley Periodicals, Inc.

Toward Unified Control of a Powered Prosthetic Leg: A Simulation Study.

This brief presents a novel control strategy for a powered knee-ankle prosthesis that unifies the entire gait cycle, eliminating the need to switch between controllers during different periods of gait. A reduced-order Discrete Fourier Transformation (DFT) is used to define virtual constraints that continuously parameterize periodic joint patterns as functions of a mechanical phasing variable. In order to leverage the provable stability properties of Hybrid Zero Dynamics (HZD), hybrid-invariant Bézier polynomials are converted into unified DFT virtual constraints for various walking speeds. Simulations of an amputee biped model show that the unified prosthesis controller approximates the behavior of the original HZD design under ideal scenarios and has advantages over the HZD design when hybrid invariance is violated by mismatches with the human controller. Two implementations of the unified virtual constraints, a feedback linearizing controller and a more practical joint impedance controller, produce similar results in simulation.

Stable, Robust Hybrid Zero Dynamics Control of Powered Lower-Limb Prostheses.

To improve the quality of life for lower-limb amputees, powered prostheses are being developed. Advanced control schemes from the field of bipedal robots, such as hybrid zero dynamics (HZD), may provide great performance. HZD-based control specifies the motion of the actuated joints using output functions to be zeroed, and the required torques are calculated using input-output linearization. For one-step periodic gaits, there is an analytic metric of stability. To apply HZD-based control on a powered prosthesis, several modifications must be made. Because the prosthesis and amputee are only connected via the socket, the prosthesis controller does not have access to the full state of the biped, which decentralizes the form of the input-output linearization. The differences between the amputated and contralateral sides result in a two-step periodic gait, which requires the orbital stability metric to be extended. In addition, because human gait is variable, the prosthesis controller must be robust to continuous moderate perturbations. This robustness is proved using local input-to-state stability and demonstrated with simulations of an above-knee amputee model.

On the periodic gait stability of a multi-actuated spring-mass hopper model via partial feedback linearization

Spring-loaded inverted pendulum (SLIP) template (and its various derivatives) could be considered as the mostly used and widely accepted models for describing legged locomotion. Despite their simple nature, as being a simple spring-mass model in dynamics perspective, the SLIP model and its derivatives are formulated as restricted three-body problem, whose non-integrability has been proved long before. Thus, researchers proceed with approximate analytical solutions or use partial feedback linearization when numerical integration is not preferred in their analysis. The key contributions of this paper can be divided into two parts. First, we propose a dissipative SLIP model, which we call as multi-actuated dissipative SLIP (MD-SLIP), with two extended actuators: one linear actuator attached serially to the leg spring and one rotary actuator attached to hip. The second contribution of this paper is a partial feedback linearization strategy by which we can cancel some nonlinear dynamics of the proposed model and obtain exact analytical solution for the equations of motion. This allows us to investigate stability characteristics of the hopping gait obtained from the MD-SLIP model. We illustrate the applicability of our solutions with open-loop and closed-loop hopping performances on rough terrain simulations.

First Steps Towards Translating HZD Control of Bipedal Robots to Decentralized Control of Exoskeletons

This paper presents preliminary results toward translating gait and control design for bipedal robots to decentralized control of an exoskeleton aimed at restoring mobility to patients with lower limb paralysis, without the need for crutches. A mathematical hybrid dynamical model of the human-exoskeleton system is developed and a library of dynamically feasible periodic walking gaits for different walking speeds is found through nonlinear constrained optimization using the full-order dynamical system. These walking gaits are stabilized using a centralized (i.e., full-state information) hybrid zero dynamics-based controller, which is then decentralized (i.e., control actions use partial state information) so as to be implementable on the exoskeleton subsystem. A control architecture is then developed so as to allow the user to actively control the exoskeleton speed through his/her upper body posture. Numerical simulations are carried out to compare the two controllers. It is found that the proposed decentralized controller not only preserves the periodic walking gaits but also inherits the robustness to perturbations present in the centralized controller. Moreover, the proposed velocity regulation scheme is able to reach a steady state and track desired walking speeds under both, centralized, and decentralized schemes.

Development of monitoring algorithm for controlling a biped robot: norm bounded uncertainties system-based

It is well known that the biped walking gait is represented as a steady periodic gait. Investigation of such passive natural motion leads to different strategies of control; these strategies require a sharp mathematical model of the walking dynamics. One of the most used models is the Kajita's one. In this paper, we present an algorithm of controller's design used for the stabilisation of biped robot's gait. Using the Kajita's model as a reference, we include a Norm bounded uncertainties to ensure a more realistic numerical model. The modified model allows us to include constraints on both inputs, outputs and states. The synthesis of the dynamic controller relies on the use of predictive control theory (MPC) and the resolution of a convex optimisation problem with linear matrix inequalities (LMIs) at every sampling period. The generated control law allows a real-time walking robot even in rough ground or unknown environment.

Development of monitoring algorithm for controlling a biped robot: norm bounded uncertainties system-based

It is well known that the biped walking gait is represented as a steady periodic gait. Investigation of such passive natural motion leads to different strategies of control; these strategies require a sharp mathematical model of the walking dynamics. One of the most used models is the Kajita's one. In this paper, we present an algorithm of controller's design used for the stabilisation of biped robot's gait. Using the Kajita's model as a reference, we include a Norm bounded uncertainties to ensure a more realistic numerical model. The modified model allows us to include constraints on both inputs, outputs and states. The synthesis of the dynamic controller relies on the use of predictive control theory (MPC) and the resolution of a convex optimisation problem with linear matrix inequalities (LMIs) at every sampling period. The generated control law allows a real-time walking robot even in rough ground or unknown environment.

Dynamic characteristics and stability criterion of rotary galloping gait with an articulated passive spine joint

Based on the simplified planar quadruped model with a passive spine joint, this paper focuses on studying the effect of the spine joint on the dynamic performance and the stability of the rotary galloping gait. The stable, periodic, and symmetric rotary galloping gait is achieved by the particle swarm optimization method without any parameter symmetry for leg motion and without any limitation for spine motion. By analyzing the symmetric pattern of the rotary galloping gait, the stability criterion is derived: the closer to zero is the pitching angle at peak time of the center of mass, the better is the stability of the galloping gait. Then, we study the effect of rear leg touchdown angle and spine stiffness on the stability. Moreover, the unstable rotary galloping gait can be stabilized by adjusting one or more of the touchdown angle, spine stiffness, and leg stiffness. Furthermore, the effect of the spine stiffness on the key kinematic and kinetic properties is also studied for different initial values and mechanical parameters. The presented stability criterion and the achieved dynamic performance analysis results will be instructive for the design and control of the quadruped robot.

Design of adaptive biometric gait recognition algorithm with free walking directions

Gait is one of well-identified biometrics that has been broadly applied for human identification at a distance based on their motion style. However, the current gait recognition might have difficulties due to changing the viewing angles anduncertainty associated with gait signature extraction. This study deals with the design of an intelligent gait recognition system that tackles the problems mentioned above. This system is based on spatial-domain energy deviation image as a gait signature by adopting clustering technique to estimate the gait period in the gait sequence with arbitrary walking directions. To further improve the performance of the proposed system, interval type-2 fuzzy K-nearest neighbor classifier is used to diminish the effect of uncertainty formed by variations in gait signature extraction. Interval type-2 fuzzy set is involved in extending themembership values of each gait signature by using several initial K in order to handle and manage uncertainty that exists in choosing the initial value K. The proposed method realises the reduction in the dimensions of the gait feature and over-fitting. The comprehensive analyses reveal that the proposed algorithm can significantly enhance the multiple view gait recognition performance when being matched to the similar methods in the literature.

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject’s self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot–ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject’s walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject’s walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject’s walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations.