Principal component analysis of the power developed in the flexion/extension muscles of the hip in able-bodied gait

Design and analysis of a passive exoskeleton with its hip joint energy-storage

A novel passive hip exoskeleton has been designed and built with the aim of reducing metabolic consumption during walking by a passive way of storing the negative mechanical energy in the deceleration phase and releasing it in the acceleration phase. A ratchet spiral spring mechanism with a set of double stable switches is designed inside the exoskeleton for the above purpose. An analysis is conducted on the mechanism and the switching timing for the energy management to automatically store or release the energy according to the biomechanics of walking. In addition, a gravity-balance mechanism embedded inside the exoskeleton is designed as well to minimize the influence of the lower limb weight on muscle work. Human-exoskeleton interaction has been studied using the Opensim software, and simulation results demonstrated the effectiveness of the exoskeleton in reducing metabolic consumption during walking. An exoskeleton prototype has been built and tested with experiments measuring assistive torque and surface electromyography signal, confirming the effectiveness of the gravity-balance mechanism and energy-storage method, as well as the exoskeleton's actual assistive effect.

Systematic Review on the Applicability of Principal Component Analysis for the Study of Movement in the Older Adult Population

Principal component analysis (PCA) is a dimensionality reduction method that has identified significant differences in older adults' motion analysis previously not detected by the discrete exploration of biomechanical variables. This systematic review aims to synthesize the current evidence regarding PCA use in the study of movement in older adults (kinematics and kinetics), summarizing the tasks and biomechanical variables studied. From the search results, 1685 studies were retrieved, and 19 studies were included for review. Most of the included studies evaluated gait or quiet standing. The main variables considered included spatiotemporal parameters, range of motion, and ground reaction forces. A limited number of studies analyzed other tasks. Further research should focus on the PCA application in tasks other than gait to understand older adults' movement characteristics that have not been identified by discrete analysis.

Symmetry Analysis of Amputee Gait Based on Body Center of Mass Trajectory and Discrete Fourier Transform

The calculation of symmetry in amputee gait is a valuable tool to assess the functional aspects of lower limb prostheses and how it impacts the overall gait mechanics. This paper analyzes the vertical trajectory of the body center of mass (CoM) of a group formed by transfemoral amputees and non-amputees to quantitatively compare the symmetry level of this parameter for both cases. A decomposition of the vertical CoM into discrete Fourier series (DFS) components is performed for each subject’s CoM trajectory to identify the main components of each pattern. A DFS-based index is then calculated to quantify the CoM symmetry level. The obtained results show that the CoM displays different patterns along a gait cycle for each amputee, which differ from the sine-wave shape obtained in the non-amputee case. The CoM magnitude spectrum also reveals more coefficients for the amputee waveforms. The different CoM trajectories found in the studied subjects can be thought as the manifestation of developed compensatory mechanisms, which lead to gait asymmetries. The presence of odd components in the magnitude spectrum is related to the asymmetric behavior of the CoM trajectory, given the fact that this signal is an even function for a non-amputee gait. The DFS-based index reflects this fact due to the high value obtained for the non-amputee reference, in comparison to the low values for each amputee.

Altered Satellite Cell Responsiveness and Denervation Implicated in Progression of Rotator-Cuff Injury

Background

Rotator-cuff injury (RCI) is common and painful; even after surgery, joint stability and function may not recover. Relative contributions to atrophy from disuse, fibrosis, denervation, and satellite-cell responsiveness to activating stimuli are not known.

Methods and Findings

Potential contributions of denervation and disrupted satellite cell responses to growth signals were examined in supraspinatus (SS) and control (ipsilateral deltoid) muscles biopsied from participants with RCI (N = 27). Biopsies were prepared for explant culture (to study satellite cell activity), immunostained to localize Pax7, BrdU, and Semaphorin 3A in satellite cells, sectioning to study blood vessel density, and western blotting to measure the fetal (γ) subunit of acetylcholine receptor (γ-AchR). Principal component analysis (PCA) for 35 parameters extracted components identified variables that contributed most to variability in the dataset. γ-AchR was higher in SS than control, indicating denervation. Satellite cells in SS had a low baseline level of activity (Pax7+ cells labelled in S-phase) versus control; only satellite cells in SS showed increased proliferative activity after nitric oxide-donor treatment. Interestingly, satellite cell localization of Semaphorin 3A, a neuro-chemorepellent, was greater in SS (consistent with fiber denervation) than control muscle at baseline. PCAs extracted components including fiber atrophy, satellite cell activity, fibrosis, atrogin-1, smoking status, vascular density, γAchR, and the time between symptoms and surgery. Use of deltoid as a control for SS was supported by PCA findings since “muscle” was not extracted as a variable in the first two principal components. SS muscle in RCI is therefore atrophic, denervated, and fibrotic, and has satellite cells that respond to activating stimuli.

Conclusions

Since SS satellite cells can be activated in culture, a NO-donor drug combined with stretching could promote muscle growth and improve functional outcome after RCI. PCAs suggest indices including satellite cell responsiveness, atrogin-1, atrophy, and innervation may predict surgical outcome.

A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation

Autonomous poststroke rehabilitation systems which can be deployed outside hospital with no or reduced supervision have attracted increasing amount of research attentions due to the high expenditure associated with the current inpatient stroke rehabilitation systems. To realize an autonomous systems, a reliable patient monitoring technique which can automatically record and classify patient's motion during training sessions is essential. In order to minimize the cost and operational complexity, the combination of nonvisual-based inertia sensing devices and pattern recognition algorithms are often considered more suitable in such applications. However, the high motion irregularity due to stroke patients' body function impairment has significantly increased the classification difficulty. A novel fuzzy kernel motion classifier specifically designed for stroke patient's rehabilitation training motion classification is presented in this paper. The proposed classifier utilizes geometrically unconstrained fuzzy membership functions to address the motion class overlapping issue, and thus, it can achieve highly accurate motion classification even with poorly performed motion samples. In order to validate the performance of the classifier, experiments have been conducted using real motion data sampled from stroke patients with a wide range of impairment level and the results have demonstrated that the proposed classifier is superior in terms of error rate compared to other popular algorithms.

Gait training with real-time augmented toe-ground clearance information decreases tripping risk in older adults and a person with chronic stroke

Falls risk increases with ageing but is substantially higher in people with stroke. Tripping-related balance loss is the primary cause of falls, and Minimum Toe Clearance (MTC) during walking is closely linked to tripping risk. The aim of this study was to determine whether real-time augmented information of toe-ground clearance at MTC can increase toe clearance, and reduce tripping risk. Nine healthy older adults (76 ± 9 years) and one 71 year old female stroke patient participated. Vertical toe displacement was displayed in real-time such that participants could adjust their toe clearance during treadmill walking. Participants undertook a session of unconstrained walking (no-feedback baseline) and, in a subsequent Feedback condition, were asked to modify their swing phase trajectory to match a “target” increased MTC. Tripping probability (PT) pre- and post-training was calculated by modeling MTC distributions. Older adults showed significantly higher mean MTC for the post-training retention session (27.7 ± 3.79 mm) compared to the normal walking trial (14.1 ± 8.3 mm). The PT on a 1 cm obstacle for the older adults reduced from 1 in 578 strides to 1 in 105,988 strides. With gait training the stroke patient increased MTC and reduced variability (baseline 16 ± 12 mm, post-training 24 ± 8 mm) which reduced obstacle contact probability from 1 in 3 strides in baseline to 1 in 161 strides post-training. The findings confirm that concurrent visual feedback of a lower limb kinematic gait parameter is effective in changing foot trajectory control and reducing tripping probability in older adults. There is potential for further investigation of augmented feedback training across a range of gait-impaired populations, such as stroke.

Joint-specific changes in locomotor complexity in the absence of muscle atrophy following incomplete spinal cord injury

Background

Following incomplete spinal cord injury (iSCI), descending drive is impaired, possibly leading to a decrease in the complexity of gait. To test the hypothesis that iSCI impairs gait coordination and decreases locomotor complexity, we collected 3D joint angle kinematics and muscle parameters of rats with a sham or an incomplete spinal cord injury.

Methods

12 adult, female, Long-Evans rats, 6 sham and 6 mild-moderate T8 iSCI, were tested 4 weeks following injury. The Basso Beattie Bresnahan locomotor score was used to verify injury severity. Animals had reflective markers placed on the bony prominences of their limb joints and were filmed in 3D while walking on a treadmill. Joint angles and segment motion were analyzed quantitatively, and complexity of joint angle trajectory and overall gait were calculated using permutation entropy and principal component analysis, respectively. Following treadmill testing, the animals were euthanized and hindlimb muscles removed. Excised muscles were tested for mass, density, fiber length, pennation angle, and relaxed sarcomere length.

Results

Muscle parameters were similar between groups with no evidence of muscle atrophy. The animals showed overextension of the ankle, which was compensated for by a decreased range of motion at the knee. Left-right coordination was altered, leading to left and right knee movements that are entirely out of phase, with one joint moving while the other is stationary. Movement patterns remained symmetric. Permutation entropy measures indicated changes in complexity on a joint specific basis, with the largest changes at the ankle. No significant difference was seen using principal component analysis. Rats were able to achieve stable weight bearing locomotion at reasonable speeds on the treadmill despite these deficiencies.

Conclusions

Decrease in supraspinal control following iSCI causes a loss of complexity of ankle kinematics. This loss can be entirely due to loss of supraspinal control in the absence of muscle atrophy and may be quantified using permutation entropy. Joint-specific differences in kinematic complexity may be attributed to different sources of motor control. This work indicates the importance of the ankle for rehabilitation interventions following spinal cord injury.

Influence of ankle injury on muscle activation and postural control during ballet grand plié

Ballet deep squat with legs rotated externally (grand plié) is a fundamental movement for dancers. However, performing this task is a challenge to ankle control, particularly for those with ankle injury. Thus, the purpose of this study was to investigate how ankle sprains affect the ability of postural and muscular control during grand plié in ballet dancers. Thirteen injured dancers and 20 uninjured dancers performed a 15 second grand plié consisting of lowering, squatting, and rising phases. The lower extremity motion patterns and muscle activities, pelvic orientation, and center of pressure (COP) excursion were measured. In addition, a principal component analysis was applied to analyze waveforms of muscle activity in bilateral medial gastrocnemius, peroneus longus, and tibialis anterior. Our findings showed that the injured dancers had smaller pelvic motions and COP excursions, greater maximum angles of knee flexion and ankle dorsiflexion as well as different temporal activation patterns of the medial gastrocnemius and tibialis anterior. These findings suggested that the injured dancers coped with postural challenges by changing lower extremity motions and temporal muscle activation patterns.

Using principal component analysis to aid bayesian network development for prediction of critical care patient outcomes

BACKGROUND

Predicting an intensive care unit patient's outcome is highly desirable. An end goal is for computational techniques to provide updated, accurate predictions about changing patient condition using a manageable number of physiologic parameters.

METHODS

Principal component analysis was used to select input parameters for critical care patient outcome models. Vital signs and laboratory values from each patient's hospital stay along with outcomes ("Discharged" vs. "Deceased") were collected retrospectively at a Level I Trauma-Military Medical Center in the southwest; intensive care unit patients were included if they had been admitted for burn, infection, or hypovolemia during a 5-year period ending October 2007. Principal component analysis was used to determine which of the 24 parameters would serve as inputs in a bayesian network developed for outcome prediction.

RESULTS

Data for 581 patients were collected. Pulse pressure, heart rate, temperature, respiratory rate, sodium, and chloride were found to have statistically significant differences between Discharged and Deceased groups for "Hypovolemia" patients. For "Burn" patients, pulse pressure, hemoglobin, hematocrit, and potassium were found to have statistically significant differences. For a "Combined" group, heart rate, temperature, respiratory rate, sodium, and chloride had statistically significant differences. A bayesian network based on these results, developed for the Combined group, achieved an accuracy of 75% when predicting patient outcome.

CONCLUSIONS

Outcome prediction for critical care patients is possible. Future work should explore model development using additional temporal data and should include prospective validation. Such technology could serve as the basis of real-time intelligent monitoring systems for critical patients.

Effect of registration on cyclical kinematic data

Given growing interest in functional data analysis (FDA) as a useful method for analyzing human movement data, it is critical to understand the effects of standard FDA procedures, including registration, on biomechanical analyses. Registration is used to reduce phase variability between curves while preserving the individual curve's shape and amplitude. The application of three methods available to assess registration could benefit those in the biomechanics community using FDA techniques: comparison of mean curves, comparison of average RMS values, and assessment of time-warping functions. Therefore, the present study has two purposes. First, the necessity of registration applied to cyclical data after time normalization is assessed. Second, we illustrate the three methods for evaluating registration effects. Masticatory jaw movements of 22 healthy adults (2 males, 21 females) were tracked while subjects chewed a gum-based pellet for 20s. Motion data were captured at 60 Hz with two gen-locked video cameras. Individual chewing cycles were time normalized and then transformed into functional observations. Registration did not affect mean curves and warping functions were linear. Although registration decreased the RMS, indicating a decrease in inter-subject variability, the difference was not statistically significant. Together these results indicate that registration may not always be necessary for cyclical chewing data. An important contribution of this paper is the illustration of three methods for evaluating registration that are easy to apply and useful for judging whether the extra data manipulation is necessary.

Neuro-rehabilitation robot-assisted assessments of synergy patterns of forearm, elbow and shoulder joints in chronic stroke patients

BACKGROUND

Abnormal synergy is one of the major motor deficits in stroke patients. Abnormal muscle synergies, in conjunction with weakness and spasticity, interfere with voluntary movements and restrict the range of motion. This study aimed to quantify abnormal synergies in the affected upper limbs of chronic stroke patients by using a neuro-rehabilitation robot.

METHODS

Twelve chronic stroke patients and eight age-matched control subjects were recruited to perform rectilinear tracking movements in four horizontal directions (back-forth, two oblique directions at 45 degrees , and right-left). Kinematic, kinetic and electromyogram data were recorded and used to develop two biomechanical indices and one electromyogram assessment index based on principal component analysis.

FINDINGS

Significant differences between upper limbs of control subjects and the affected side of stroke patients were observed in all three assessment indices. Higher correlation between the elbow joint angle and the forearm pronation/supination torque, higher variation of the forearm torque, and abnormal co-contraction of the elbow and shoulder muscles were observed in the affected limbs of stroke patients. The difference was more prominent in the right-left direction and the oblique direction contra-proximal to ipsi-distal.

INTERPRETATION

The proposed assessment indices could be employed to quantify the abnormal synergies in stroke patients. Rectilinear tracking along the right-left direction and the oblique direction of contra-proximal to ipsi-distal is more suitable for assessing abnormal synergies.

An application of principal component analysis for lower body kinematics between loaded and unloaded walking

Load carriage is a very common daily activity at home and in the workplace. Generally, the load is in the form of an external load carried by an individual, it could also be the excessive body mass carried by an overweight individual. To quantify the effects of carrying extra weight, whether in the form of an external load or excess body mass, motion capture data were generated for a diverse subject set. This consisted of twenty-three subjects generating one hundred fifteen trials for each loading condition. This study applied principal component analysis (PCA) to motion capture data in order to analyze the lower body gait patterns for four loading conditions: normal weight unloaded, normal weight loaded, overweight unloaded and overweight loaded. PCA has been shown to be a powerful tool for analyzing complex gait data. In this analysis, it is shown that in order to quantify the effects of external loads and/or for both normal weight and overweight subjects, the first principal component (PC1) is needed. For the work in this paper, PCs were generated from lower body joint angle data. The PC1 of the hip angle and PC1 of the ankle angle are shown to be an indicator of external load and BMI effects on temporal gait data.

The identification of age-related differences in kinetic gait parameters using principal component analysis

Background. The age of onset of adult-like kinetic gait patterns is controversial. A potential cause of discrepant results between studies is the statistical analyses used to test for differences in kinetic parameters between age groups. Therefore, the purpose of this study was to identify age-related differences in kinetic gait parameters across children aged 3-13 years using principal component analysis. Methods. Principal component analysis was applied to seven kinetic waveform variables (N=7) from each of four age groups (3-4 years (n=13); 5-6 years (n=10); 7-8 years (n=12); and 9-13 years (n=12)). The principal component scores for each kinetic variable were used to test for group differences using one-way ANOVA and Kruskal-Wallis tests. Findings. Significant group differences (P<0.05) were found for five of the principal component scores. Plantarflexion moments increased with age and the oldest group of children (9-13 years old) demonstrated significantly larger plantarflexor moment patterns compared to all other age groups. The 9-13 years old showed significantly larger knee flexor and extensor moments for the first half of the cycle and a later reversal to extensor moments in terminal stance compared to 3-6 years old. The older group also showed decreased hip extensor moments for the first third of the cycle and increased flexor moments in the second third of the cycle compared to the 3-4 and 7-8 years old. Larger stance phase hip abduction moments were observed in the older group compared to all other groups. This was followed by a more complex pattern of alternating moments. Hip power also showed a complex series of differences between age-groups. Interpretation. Compared to parameterization techniques, principal component analysis identified different characteristics in kinetic gait data to discriminate between paediatric age groups. This is the first study to identify age-related differences in gait kinetics using waveform analysis.

Reduction, classification and ranking of motion analysis data: an application to osteoarthritic and normal knee function data

There are certain major obstacles to using motion analysis as an aid to clinical decision making. These include: the difficulty in comprehending large amounts of both corroborating and conflicting information; the subjectivity of data interpretation; the need for visualization; and the quantitative comparison of temporal waveform data. This paper seeks to overcome these obstacles by applying a hybrid approach to the analysis of motion analysis data using principal component analysis (PCA), the Dempster-Shafer (DS) theory of evidence and simplex plots. Specifically, the approach is used to characterise the differences between osteoarthritic (OA) and normal (NL) knee function data and to produce a hierarchy of those variables that are most discriminatory in the classification process. Comparisons of the results obtained with the hybrid approach are made with results from artificial neural network analyses.

An extended index to quantify normality of gait in children

Clinical gait analysis aims to quantify and assess the mechanics of walking and identify deviations from 'normal' movement patterns. To facilitate the use of clinical equipment, protocols are required to process data and produce a few meaningful summary measurements which can, in turn, be used to flag gait abnormalities. Earlier work produced a one-dimensional index of gait, calculated from sagittal hip, knee and ankle rotation angle patterns. The objective of this study was to extend the original index, incorporating kinematic and kinetic data from multiple planes, while allowing for correlations between component measures. A one-dimensional index of normal gait was developed, based on normative gait data (N=45 children, aged 3-13 years). The new one-dimensional index was calculated using correlation patterns between seven component indices, each of which has diagnostic interpretation. The effectiveness of the new index was tested using immature normative data (N=14) and hypotonic data (N=10). Approximately 85% of immature normative children and 100% of hypotonic children were classified as either unusual or extreme by the one-dimensional index. These data reduction protocols improve objective gait analyses in the clinical setting.

Inter-segmental coordination: Motor pattern in humans stepping over an obstacle with mechanical ankle joint friction

This study examined the influence of a mechanical perturbation of the ankle joint on obstacle avoidance pattern. A decoupled control between the distal joint and the combined (hip-knee) proximal joints was observed according to the task requirement. In this context, a greater mechanical friction at the ankle should be compensated at this joint (local compensation) or alternatively, by regulating more combined proximal joints (knee and/or hip). The leading limb inter-segmental coordination was evaluated in both no constraint and constraint conditions in calculating ranges of motion (ROM), moments of force and powers (from heel-off to obstacle) at the ankle, knee and hip joints. Electromyographic activities were also analyzed. With the constraint, the dorsiflexor moment and the tibialis anterior activity remained unchanged while both ROM and power bursts (absorbed and generated) decreased. The hip and knee ROM remain invariant. At heel-off the absorption by hip extensors decreased and the forthcoming generation by knee flexors increased in the constraint condition. To quantify the inter-joint coordination, principal component analysis was used and indicated a high level of inter-joint coupling (synergy) that decreased with the constraint (i.e. less inter-joint coupling). At the ankle joint, the results suggest that the central command was the same in both conditions thus, not be adapted. At both the hip and knee joints, a combined joints modulation occurred to overcome additional friction.

Quantification of human motion: gait analysis-benefits and limitations to its application to clinical problems

The technology supporting the analysis of human motion has advanced dramatically. Past decades of locomotion research have provided us with significant knowledge about the accuracy of tests performed, the understanding of the process of human locomotion, and how clinical testing can be used to evaluate medical disorders and affect their treatment. Gait analysis is now recognized as clinically useful and financially reimbursable for some medical conditions. Yet, the routine clinical use of gait analysis has seen very limited growth. The issue of its clinical value is related to many factors, including the applicability of existing technology to addressing clinical problems; the limited use of such tests to address a wide variety of medical disorders; the manner in which gait laboratories are organized, tests are performed, and reports generated; and the clinical understanding and expectations of laboratory results. Clinical use is most hampered by the length of time and costs required for performing a study and interpreting it. A "gait" report is lengthy, its data are not well understood, and it includes a clinical interpretation, all of which do not occur with other clinical tests. Current biotechnology research is seeking to address these problems by creating techniques to capture data rapidly, accurately, and efficiently, and to interpret such data by an assortment of modeling, statistical, wave interpretation, and artificial intelligence methodologies. The success of such efforts rests on both our technical abilities and communication between engineers and clinicians.

PCA in studying coordination and variability: a tutorial

OBJECTIVE

To explain and underscore the use of principal component analysis in clinical biomechanics as an expedient, unbiased means for reducing high-dimensional data sets to a small number of modes or structures, as well as for teasing apart structural (invariant) and variable components in such data sets.

DESIGN

The method is explained formally and then applied to both simulated and real (kinematic and electromyographic) data for didactical purposes, thus illustrating possible applications (and pitfalls) in the study of coordinated movement.

BACKGROUND

In the sciences at large, principal component analysis is a well-known method to remove redundant information in multidimensional data sets by means of mode reduction. At present, principal component analysis is starting to penetrate the fundamental and clinical study of human movement, which amplifies the need for an accessible explanation of the method and its possibilities and limitations. Besides mode reduction, we discuss principal component analysis in its capacity as a data-driven filter, allowing for a separation of invariant and variant properties of coordination, which, arguably, is essential in studies of motor variability.

METHODS

Principal component analysis is applied to kinematic and electromyographic time series obtained during treadmill walking by healthy humans.

RESULTS

Common signal structures or modes are identified in the time series that turn out to be readily interpretable. In addition, the identified coherent modes are eliminated from the data, leaving a filtered, residual pattern from which useful information may be gleaned regarding motor variability.

CONCLUSIONS

Principal component analysis allows for the detection of modes (information reduction) in both kinematic and electromyographic data sets, as well as for the separation of invariant structure and variance in those data sets.

RELEVANCE

Principal component analysis can be successfully applied to movement data, both as feature extractor and as data-driven filter. Its potential for the (clinical) study of human movement sciences (e.g., diagnostics and evaluation of interventions) is evident but still largely untapped.

Local or global asymmetry in gait of people without impairments

Using two consecutive gait cycles, simultaneous and bilateral kinetic gait data, the main objectives of this study were (a) to identify the main functional roles of ankle, knee and hip extensors/flexors, and (b) to determine whether the action taken by these muscle groups appears to be symmetric or not. Gait of our able-bodied subjects appears to be asymmetric with significant differences noted between each two corresponding peak muscle moment values. Using principal component analysis (PCA) as a curve structure detection method, task discrepancies were recognized when comparisons were made between each two corresponding representative moment curves at each joint (local asymmetry). Muscle moment behaved symmetrically when the right limb representative curve was compared to its corresponding principal component (PC) at the contralateral limb. Gait of able-bodied subjects appears to be symmetric, while control and propulsion were recognized as two major roles of the extensors and flexors (global gait asymmetry). Symmetrical behavior of the lower limbs should be considered a consequence of local asymmetry which indicates different levels of within and between muscle activities developed at each joint during gait cycles.

Continuous curve registration as an intertrial gait variability reduction technique

Timing in peak values shifts slightly between gait trials. When gait data are averaged, part of the standard deviation could be associated with this intertrial variability unless normalization is carried out beforehand. The objective of this study was to determine how continuous curve registration, an alignment technique, can reduce intersubject variability in gait data without altering the original curve characteristics. Gait data were obtained by means of a four-camera high-speed video system synchronized to a force plate. The data for 60 gait trials were collected from 20 young, healthy subjects. Curve registration was applied to hip angular displacement, net moment, and power curves generated in the sagittal plane. Following registration, the peak values increased by an average of 1.2% (0.11 +/- 0.26 degrees) for angular displacement, and by 11.2% (0.11 +/- 0.09 W/kg) for power, while there were no changes for moments. First and second derivatives of the unregistered and registered curves did not display significant differences, and the harmonics were barely affected. Continuous curve registration would thus be an appropriate technique for application prior to any statistical analysis using able-bodied gait patterns.

An index to quantify normality of gait in young children

Gait patterns are often described by recording the changes in angular rotation of such joints as the hip, knee and ankle, during a complete cycle. Each joint exhibits distinctive behavior throughout the gait cycle, and abnormal gait can be described by measuring departure from a typical (mean) joint rotation curve. Standard techniques for observation of gait patterns produce large sets of data. Data reduction is achieved in this work by locating primary directions of variation from mean behavior. Variation from the mean can then be summarized with a one-dimensional statistic, thought of as a squared distance from the population mean. Percentiles of this one-dimensional index can be calculated, enabling classification of a child as normal, unusual or abnormal. A key feature of this analysis is that it is applied across multiple joint angle curves and their derivatives, thus providing a measure that takes account of the interactions between the curves as well as their individual characteristics. A data base of 348 gait cycles, collected from normal children, aged 3-7, were analyzed. Data on each child were stored in a 36-dimensional vector. Most information on patterns of variation among normal children can be stored in a smaller 11-dimensional vector, which can be used for diagnostic purposes. Performance of the one-dimensional index of gait is demonstrated on data from very young children, and on children, up to age 7, who were born prematurely.

Knee flexors/extensors in gait of elderly and young able-bodied men (II)

In this study the fundamental tasks of muscle activity at the knee during gait in elderly and young able-bodied subjects were identified using principal component analysis (PCA). Role discrepancies between the older and younger subjects for the actions executed by the knee flexors and extensors during the gait cycle were also investigated. The t-test for independent groups was applied to determine significant differences between spatio-temporal and peak muscle moment parameters (P<0.05). PCA as a multivariate classification and curve structure detection method was applied to the sagittal knee muscle moment curves of twenty elderly (72 +/-5.5 years) and twenty young (25 +/- 8.1 years) subjects. The first three principal components (PCs) which accounted for 80% (older) and 93% (younger) of the information were retained for further analysis. Providing stable locomotion was recognised as a major task of the knee in the older subjects, while for the younger subjects the knee contributed to both balance and propulsion. Supporting the body during single limb support should be considered the only common task at the knee level in elderly and young subjects' gait. The lack of muscle power for propulsion might be the reason for not identifying the knee extensor muscle roles in the first three major tasks during elderly gait. Functional asymmetry can be considered the result of a different ordering of the functional roles of the muscles acting at the knee level in elderly and young subjects.

Main functional roles of knee flexors/extensors in able-bodied gait using principal component analysis (I)

This study was undertaken to demonstrate how principal component analysis (PCA) can be used to detect the main functional structure of actions taken by knee flexors/extensors during able-bodied gait. PCA was applied as a classification and curve structure detection method for knee sagittal muscle moment developed during walking of 20 young, healthy male subjects. Over 90% of the information provided by the first three principal components (PCs) was chosen for further biomechanical interpretation. PCA was able to identify the three main functional contributions of knee sagittal muscle moment during able-bodied gait, namely control balance, foot clearance/limb preparation and shock absorption.