Human Gait

The study of human gait is one of the interesting fields among several disciplines. This arena has attracted researchers and professionals not only from human physiology and neuro biology but has also fascinated scientists from socio-psychology and socio-informatics. The locomotion of humans is associated with lots of physical and biological aspect that might provide critical information related to stress, intentions, emotions, personality, and even neurological disorder. This abundant source of data might be used in the complex system to create information in response to generate evidences for identifying the particular person, some of which was also investigated in our previous work [1]. This paper is the extension of human gait recognition that presents the analysis of trade-off between evolution of Genetic Programs (GPs) and their performance, considering different training cases, provided that the computational effort and some other parameter are kept constant. Furthermore, our previous work [2] had left an important question unanswered about how the extension of number of fitness cases and the use of experts in collaborative filtering affects the evolution of GPs and recognition of Gait. This study is an attempt to explore the same unexplained question.

A two-dimensional, seven link, nine degrees of freedom biped model was developed to investigate the dynamic characteristics of normal and transfemoral amputee locomotion during the entire gait cycle. The equations of motion were derived using the Lagrange method and the stance foot-ground contact was simulated using a five-point penetration model. The joint driving torques were obtained using forward dynamic optimization of the normal human gait and applied to the intact joints of the amputee. Three types of motion controllers; frictional, elastic and hydraulic were considered for the prosthetic joints of the amputee and their design parameters were optimized to achieve the closest kinematics to that of the normal gait. It was found that, if optimally designed, all three passive controllers could reasonably reproduce a normal kinematical pattern in the swing phase. However, the stance phase kinematics could only be replicated by the hydraulic and elastic controllers; the performance of the latter was highly sensitive to the design parameters. It was concluded that an appropriately designed hydraulic motion controller can provide reasonably normal kinematics and reliable stability for stance knee flexion prostheses. © 2013 Shandiz et al.; licensee InTech.

cdn.intechopen.com/pdfs/43698/InTech-A_robotic_model_of_transfemoral_amputee_locomotion_for_design_optimization_of_knee_controllers.pdf

Today neuromuscular simulations are used in several fields, such as diagnostics and planing of surgery, to get a deeper understanding of the musculoskeletal system. During the last year, new models and datasets have been presented which can provide us with more in-depth simulations and results. The same kind of development has occurred in the field of studying the human knee joint using complex three dimensional finite element models and simulations. In the field of musculoskeletal simulations, no such knee joints can be used. Instead the most common knee joint description is an idealized knee joint with limited accuracy or a planar knee joint which only describes the knee motion in a plane. In this paper, a new knee joint based on both equations and geometry is introduced and compared to a common clinical planar knee joint. The two kinematical models are analyzed using a gait motion, and are evaluated using the muscle activation and joint reaction forces which are compared to invivo measured forces. We show that we are able to predict the lateral, anterior and longitudinal moments, and that we are able to predict better knee and hip joint reaction forces.

The availability of large data sources has been an important support for decision making; however, inmanycases, this is complicated due to the volume and complexity with which data is presented and manipulated. In medicine, this situation becomes more evident because decisions must be made quickly; therefore, advanced computational techniques have been incorporated to support decisions in various areas of knowledge and particularly, in medicine. This paper presents the results obtained by integrating data from the Gait Laboratory of the Children's Orthopedic Hospital in a single database. This database contains multimedia information from complementary medical tests (signals and x-rays), physical examinations and patients' gait videos. Also, to show the usefulness of this database, the relevance of using fuzzy queries about this data as a powerful tool for decision making was studied. These queries permit incorporating the benefits of fuzzy logic to express requirements that...

Most women like wearing high-heeled shoes for the benefit of sensuous attractiveness and selfesteem while musculoskeletal problems and gait patterns changes are often associated. The present study aimed to identify changes during the gait stance and swing phases in some lower limb muscles. In addition, abdominal muscle was included due to its importance in dynamic trunk stability, and lack of studies on the subject. Here, we found that the use of high-heeled shoes elicited not only the increasing of the electrical activity from the muscles involved in the gait cycle, but also altered the temporal sequence of their recruitment. As practical applications, these changes may be strategies to maintain stability and minimize risks of falling, but they are often associated to diseases. Women that use high-heeled shoes for prolonged time must apply specific muscle exercises to minimize its long-term effects.

Understanding the human gait is an important objective towards improving elderly mobility. In turn, gait analyses largely depend on kinematic and dynamic measurements. While the majority of current markerless vision systems focus on estimating 2D and 3D walking motion in the sagittal plane, we wish to estimate the 3D pose of rollator users' lower limbs from observing image sequences in the coronal (frontal) plane. Our apparatus poses a unique set of challenges: a single monocular view of only the lower limbs and a frontal perspective of the rollator user. Since motion in the coronal plane is relatively subtle, we explore multiple cues within a Bayesian probabilistic framework to formulate a posterior estimate for a given subject's leg limbs. This paper describes four cues based on three features to formulate a pose estimate: image gradients, colour and anthropometric symmetry. Our appearance model is applied within a non-parametric (particle) filtering system to track the lower limbs. Our tracking system does not rely on any detection for automatic initialization. Preliminary experiments are promising, showing that the algorithm may provide an indication of relative depth for each lower limb.

Research in the perception of depth in augmented and virtual reality has reported a consistent underestimation of egocentric depth with regard to stationary objects located along the ground plane. However, there has been a rather large disparity in the degree of underestimation reported from study to study with some studies reporting as much as a 68% underestimation of egocentric depth while others report as little as 6% [Jones et al. 2008]. The current study investigates the judgment of egocentric distance in real, augmented, and virtual environments and finds that subjects' judgments improve in accuracy as exposure continues in the absence of explicit feedback.

Today neuromuscular simulations are used in several fields, such as diagnostics and planing of surgery, to get a deeper understanding of the musculoskeletal system. During the last year, new models and datasets have been presented which can provide us with more in-depth simulations and results. The same kind of development has occurred in the field of studying the human knee joint using complex three dimensional finite element models and simulations. In the field of musculoskeletal simulations, no such knee joints can be used. Instead the most common knee joint description is an idealized knee joint with limited accuracy or a planar knee joint which only describes the knee motion in a plane. In this paper, a new knee joint based on both equations and geometry is introduced and compared to a common clinical planar knee joint. The two kinematical models are analyzed using a gait motion, and are evaluated using the muscle activation and joint reaction forces which are compared to invivo measured forces. We show that we are able to predict the lateral, anterior and longitudinal moments, and that we are able to predict better knee and hip joint reaction forces.

In this paper, we systematically analyze different components of human gait, for the purpose of human identification. We investigate dynamic features such as the swing of the hands/legs, the sway of the upper body and static features like height, in both frontal and side views. Both probabilistic and non-probabilistic techniques are used for matching the features. Various combination strategies may be used depending upon the gait features being combined. We discuss three simple rules: the Sum, Product and MIN rules that are relevant to our feature sets. Experiments using four different datasets demonstrate that fusion can be used as an effective strategy in recognition.

This paper presents an experimental characterization of the biomechanics of human gait by means of Cassino Tracking System (CaTraSys). CaTraSys is a passive cablebased parallel manipulator that is used to determine pose and exerted force of mechanical systems along large trajectories. It is a low-cost easy-operable system that has been utilized in the present work for a clinical application as an assessing device for diagnosis and rehabilitation procedures. A methodology for experimental tests and data analysis is outlined, which includes mean extrapolation of acquired trajectory data. Basic characteristics of two-dimensional (2D) and three-dimensional (3D) resulting gait patterns are discussed. Using spatiotemporal parameters, different test conditions and anthropometric characteristics are evaluated in order to assess their influence on the human walking from the kinematic point of view.

The measurement of a particular limb in the human body is an important task in many applications. Human behavior understanding typically involves human body posture analysis or estimation, as well as the generated corresponding gestures. This behavioral characterization makes it possible to analyze, interpret and animate human actions and, therefore, enables us to use some experimental methodologies. The present aims to contribute to this field by presenting the development of an experimental measurement system for human gait using accelerometry (an accelerometer network distributed on the body). When the subject walks, the system captures and processes the events and determines the angles of the limb. For results assessment, a virtual body model is presented in real-time motion, with data being stored in a database for future evaluations and analyses. The system proposed in this paper showed an average error of 3.1°, has low cost, high accuracy and is suitable for a wide range of applications.

Motor control can be achieved in the absence of awareness, even when performed intentionally. The aim of this study was to understand the mechanisms of the sudden awareness of our own movement. This was studied in locomotion because it is an automatic behavior which can be intentionally modulated. Subjects walked continuously with the instruction to maintain either a constant walking

From what we know at present with respect to the neural control of walking, it can be concluded that an optimal biologically inspired robot could have the following features. The limbs should include several joints in which position changes can be obtained by actuators across the joints. The control of mono-and biarticular actuators should occur at least at three levels: one at direct control of the actuators (equivalent to motoneuron level), the second at indirect control acting at a level which controls whole limb movement (flexion or extension) and the third at a still higher level controlling the interlimb coordination. The limb level circuits should be able to produce alternating flexion and extension movements in the limb by means of coupled oscillator flexor and extensor parts which are mutually inhibitory. The interlimb control level should be able to command the various limb control centers. All three control levels should have some basic feedback circuits but the most essential one is needed at the limb control level and concerns the decision to either flex or extend a given limb. The decision to activate the extensor part of the limb oscillator has to be based on feedback signalling the onset of loading of the limb involved. This should be signalled by means of load sensors in the limb. The decision to activate the flexor part of the limb oscillator has to depend on various types of feedback. The most important requirement is that flexion should only occur when the limb concerned is no longer loaded above a given threshold. The rule for the initiation of limb flexion can be made more robust by adding the requirement that position at the base of the limb (''hip'') should be within a normal end of stance phase range. Hence, human locomotion is thought to use a number of principles which simplify control, just as in other species such as the cat. It is suggested that cat and human locomotion are good models to learn from when designing efficient walking robots.

We present a strategy based on human gait to achieve efficient tracking, recovery of egomotion and 3-D reconstruction from an image sequence acquired by a single camera attached to a pedestrian. In the first phase, the parameters of the human gait are established by a classical frame-by-frame analysis, using an generalized least squares (GLS) technique. The gait model is non-linear, represented by a truncated Fourier series. In the second phase, this gait model is employed within a ''predict-correct'' framework using a maximum a posteriori, expectation-maximization (MAP-EM) strategy to obtain robust estimates of the ego-motion and scene structure, while continuously refining the gait model. Experiments on synthetic and real image sequences show that the use of the gait model results in more efficient tracking. This is demonstrated by improved matching and retention of features, and a reduction in execution time, when processing video sequences.

Understanding the human gait is an important objective towards improving elderly mobility. In turn, gait analyses largely depend on kinematic and dynamic measurements. While the majority of current markerless vision systems focus on estimating 2D and 3D walking motion in the sagittal plane, we wish to estimate the 3D pose of rollator users' lower limbs from observing image sequences in the coronal (frontal) plane. Our apparatus poses a unique set of challenges: a single monocular view of only the lower limbs and a frontal perspective of the rollator user. Since motion in the coronal plane is relatively subtle, we explore multiple cues within a Bayesian probabilistic framework to formulate a posterior estimate for a given subject's leg limbs. This paper describes four cues based on three features to formulate a pose estimate: image gradients, colour and anthropometric symmetry. Our appearance model is applied within a non-parametric (particle) filtering system to track the lower limbs. Our tracking system does not rely on any detection for automatic initialization. Preliminary experiments are promising, showing that the algorithm may provide an indication of relative depth for each lower limb.

The aim of this study was to determine the relative involvement of the corticospinal (CS) pathway in voluntarily controlled walking compared to unconstrained walking. In the voluntarily controlled walking condition, subjects had to walk at the same speed as in unconstrained walking with a mechanical constraint, which is known to affect speci®cally the upper-leg muscles. The motor cortex was activated transcranially using a focal magnetic stimulation coil in order to elicit motor evoked potentials (MEPs) in the rectus femoris (RF) and the biceps femoris (BF). The magnetic stimulation was delivered at the end of the swing (at 90% of the cycle duration), when the EMG backgrounds were similar in the two experimental conditions. For each subject in each condition, MEPs were measured for several stimulus intensities in order to establish the input/output (I/O) curve (MEPs amplitude plotted against stimulus strength). The results showed a signi®cant increase in the MEPs amplitude of both the RF and BF in voluntarily controlled walking compared to unconstrained walking, which is the ®rst evidence of cofacilitation of MEPs in antagonist upperleg muscles during human gait. In conclusion, although a lot of studies have emphasized a privileged input of the corticospinal pathway to the distal lower-leg muscles, this study shows that, if a locomotory task requires ®ne control of the proximal upper-leg muscles, a selective facilitation of MEPs is observed in these muscles.

Human gait is a spatio-temporal phenomenon and typifies the motion characteristics of an individual. The gait of a person is easily recognizable when extracted from a sideview of the person. Accordingly, gait-recognition algorithms work best when presented with images where the person walks parallel to the camera (i.e. the image plane). However, it is not realistic to expect that this assumption will be valid in most real-life scenarios. Hence it is important to develop methods whereby the side-view can be generated from any other arbitrary view in a simple, yet accurate, manner. That is the main theme of this paper. We show that if the person is far enough from the camera, it is possible to synthesize a side view (referred to as canonical view) from any other arbitrary view using a single camera. Two methods are proposed for doing this: i) by using the perspective projection model, and ii) by using the optical flow based structure from motion equations. A simple camera calibration scheme for this method is also proposed. Examples of synthesized views are presented. Preliminary testing with gait recognition algorithms gives encouraging results. A by-product of this method is a simple algorithm for synthesizing novel views of a planar scene.

Analysis of the gait stability of a man moving along an even surface with a constant velocity is presented. The stability criteria applied to biped robots, namely: the Zero Moment Point (ZMP) and the Ground projection of the Center Of Mass (GCOM) have been employed in the investigations. The analysis has been carried out on the basis of measurement data obtained from the human gait recorded with a digital camera.

Cerebral palsy is a neuro-musculoskeletal condition which affects human gait and balance. Intelligent diagnosis of this condition can have many advantages in clinical decision making. In this paper a novel classification algorithm is proposed in order to distinguish walking patterns in children with cerebral palsy based on kinetic and acceleration data measurements. This algorithm addresses the significance of wavelet network classifier model in pathologic pattern classification. The proposed algorithm consists of three main steps .In the first step feature extraction is implemented using discrete wavelet transform. The second step is feature selection in order to decrease the input data space and omit redundant features. For this purpose Shannon entropy is recruited in order to select the most relevant features which have the most effects on walking patterns. The final step is developing a wavelet network classifier model in order to classify walking patterns in to four groups: healthy, severe cp condition, moderate cp condition and minimal cp condition. Subject pools consist of 51 healthy children and 48 children with cerebral palsy which are selected to obtain walking patterns in the frame work of force, moment and acceleration components.

We compare the complexity of human gait time series from healthy subjects under different conditions. Using the recently developed multiscale entropy algorithm, which provides a way to measure complexity over a range of scales, we observe that normal spontaneous walking has the highest complexity when compared to slow and fast walking and also to walking paced by a metronome. These findings have implications for modeling locomotor control and for quantifying gait dynamics in physiologic and pathologic states.

We compare the complexity of human gait time series from healthy subjects under di erent conditions. Using the recently developed multiscale entropy algorithm, which provides a way to measure complexity over a range of scales, we observe that normal spontaneous walking has the highest complexity when compared to slow and fast walking and also to walking paced by a metronome. These ÿndings have implications for modeling locomotor control and for quantifying gait dynamics in physiologic and pathologic states.

The use of a wearable triaxial accelerometer for unsupervised monitoring of human movement has become a major research focus in recent years. In this paper, the relationship between accelerometry signals and human gait is analysed using a linear prediction (LP) model. We explore the use of the LP model for analysing five gait patterns and show that the LP cepstrum can be used for gait pattern classification with high accuracy. This is then compared to a filterbank based approach to estimate the cepstral coefficients. Fifty subjects participated in collection of gait pattern data involving walking on level surfaces, and walking up and down stairs and ramps. The results show that an overall accuracy of 93% can be achieved using features derived from the cepstral coefficients for the five different walking patterns.