Reflections on clinical gait analysis

Clinical gait analysis allows the measurement and assessment of walking biomechanics, which facilitates the identification of abnormal characteristics and the recommendation of treatment alternatives. The predominant methods for this analysis currently include the tracking of external markers placed on the patient, the monitoring of patient/ground interaction (e.g. ground reaction forces), and the recording of muscle electromyographic (EMG) activity, all during gait. These data allow the computation of stride and temporal parameters, joint/segment kinematics, joint kinetics, and EMG plots that are used to gain a better understanding of a patient's walking difficulties. Gait interpretation involves a systemic evaluation of each of these types of data, noting both corroborating and conflicting information while identifying functionally significant deviations from the normal. Understanding the etiology of these abnormalities allows the formulation of a treatment plan that may involve physical therapy, bracing, and/or surgery. This process is challenging because of the complexity of the motion, neuromuscular involvement of the patient (e.g. dynamic spasticity), variability of treatment outcome, and on occasion, uncertainty about the quality of the gait data. The experience of the interpretation team with respect to gait biomechanics, a particular patient population, and the effectiveness of different treatment modalities is the principal determinant of the success of this approach. The clinical gait analysis process continues to evolve positively. It has become more comprehensive and meaningful because of an improved understanding of normal gait biomechanics and more rigorous data collection/reduction protocols that complement accumulated clinically relevant experience.     

Verification of accuracy and validity of gait phase detection system using motion sensors for applying walking assistive FES

In this study, we have analysed heel strike (HS) and toe off (TO) of normal individuals and hemiplegic patients, taking advantage of output curves acquired from various sensors, and verified the validity of sensor detection methods and their effectiveness when they were used for hemiplegic gaits. Gait phase detections using three different motion sensors were valid, since they all had reliabilities more than 95%, when compared with foot velocity algorithm. Results showed that the tilt sensor and the gyrosensor could detect gait phase more accurately in normal individuals. Vertical acceleration could detect HS most accurately in hemiplegic patient group A. The gyrosensor could detect HS and TO most accurately in hemiplegic patient groups A and B. The detection of TO from all sensor signals was valid in both the patient groups A and B. However, the vertical acceleration detected HS validly in patient group A and the gyrosensor detected HS validly in patient group B.     

Diurnal variation in gait characteristics and transition speed

The aim of this study was to investigate the effect of time-of-day on Preferred Transition Speed (PTS) and spatiotemporal organization of walking and running movements. Twelve active male subjects participated in the study (age: 27.2?±?4.9 years; height: 177.9?±?5.4?cm; body mass: 75.9?±?5.86?kg). First, PTS was determined at 08:00?h and 18:00?h. The mean of the two PTS recorded at the two times-of-day tested was used as a reference (PTS_m). Then, subjects were asked to walk and run on a treadmill at three imposed speeds (PTS_m, PTS_m?+?0.3?m.s^?1, and PTS_m???0.3?m.s^?1) at 08:00?h and 18:00?h. Mean stride length, temporal stride, spatial stride variability, and temporal stride variability were used for gait analysis. The PTS observed at 08:00?h (2.10?±?0.17?m.s^?1) tends to be lower (p?=?0.077) than that recorded at 18:00?h (2.14?±?0.19?m.s^?1). Stride lengths recorded while walking (p?=?0.038) and running (p?=?0.041) were shorter at 08:00?h than 18:00?h. No time-of-day effect was observed for stride frequency during walking and running trials. When walking, spatial stride variability (p?=?0.020) and temporal stride variability (p?=?0.028) were lower at 08:00?h than at 18:00?h. When running, no diurnal variation of spatial stride variability or temporal stride variability was detected.     

Intra-rater repeatability of gait parameters in healthy adults during self-paced treadmill-based virtual reality walking

Self-paced treadmill walking is becoming increasingly popular for the gait assessment and re-education, in both research and clinical settings. Its day-to-day repeatability is yet to be established. This study scrutinised the test-retest repeatability of key gait parameters, obtained from the Gait Real-time Analysis Interactive Lab (GRAIL) system. Twenty-three male able-bodied adults (age: 34.56 ± 5.12 years) completed two separate gait assessments on the GRAIL system, separated by 5 ± 3 days. Key gait kinematic, kinetic, and spatial-temporal parameters were analysed. The Intraclass-Correlation Coefficients (ICC), Standard Error Measurement (SEM), Minimum Detectable Change (MDC), and the 95% limits of agreements were calculated to evaluate the repeatability of these gait parameters. Day-to-day agreements were excellent (ICCs > 0.87) for spatial-temporal parameters with low MDC and SEM values, <0.153 and <0.055, respectively. The repeatability was higher for joint kinetic than kinematic parameters, as reflected in small values of SEM (<0.13 Nm/kg and <3.4°) and MDC (<0.335 Nm/kg and <9.44°). The obtained values of all parameters fell within the 95% limits of agreement. Our findings demonstrate the repeatability of the GRAIL system available in our laboratory. The SEM and MDC values can be used to assist researchers and clinicians to distinguish ‘real’ changes in gait performance over time.     

Computational evaluation of load carriage effects on gait balance stability

Evaluating the effects of load carriage on gait balance stability is important in various applications. However, their quantification has not been rigorously addressed in the current literature, partially due to the lack of relevant computational indices. The novel Dynamic Gait Measure (DGM) characterizes gait balance stability by quantifying the relative effects of inertia in terms of zero-moment point, ground projection of center of mass, and time-varying foot support region. In this study, the DGM is formulated in terms of the gait parameters that explicitly reflect the gait strategy of a given walking pattern and is used for computational evaluation of the distinct balance stability of loaded walking. The observed gait adaptations caused by load carriage (decreased single support duration, inertia effects, and step length) result in decreased DGM values (p < 0.0001), which indicate that loaded walking motions are more statically stable compared with the unloaded normal walking. Comparison of the DGM with other common gait stability indices (the maximum Floquet multiplier and the margin of stability) validates the unique characterization capability of the DGM, which is consistently informative of the presence of the added load.     

Quantitative evaluation of the major determinants of human gait

Accurate knowledge of the isolated contributions of joint movements to the three-dimensional displacement of the center of mass (COM) is fundamental for understanding the kinematics of normal walking and for improving the treatment of gait disabilities. Saunders et al. (1953) identified six kinematic mechanisms to explain the efficient progression of the whole-body COM in the sagittal, transverse, and coronal planes. These mechanisms, referred to as the major determinants of gait, were pelvic rotation, pelvic list, stance knee flexion, foot and knee mechanisms, and hip adduction. The aim of the present study was to quantitatively assess the contribution of each major gait determinant to the anteroposterior, vertical, and mediolateral displacements of the COM over one gait cycle. The contribution of each gait determinant was found by applying the concept of an ‘influence coefficient’, wherein the partial derivative of the COM displacement with respect to a prescribed determinant was calculated. The analysis was based on three-dimensional measurements of joint angular displacements obtained from 23 healthy young adults walking at slow, normal and fast speeds. We found that hip flexion, stance knee flexion, and ankle-foot interaction (comprised of ankle plantarflexion, toe flexion and the displacement of the center of pressure) are the major determinants of the displacements of the COM in the sagittal plane, while hip adduction and pelvic list contribute most significantly to the mediolateral displacement of the COM in the coronal plane. Pelvic rotation and pelvic list contribute little to the vertical displacement of the COM at all walking speeds. Pelvic tilt, hip rotation, subtalar inversion, and back extension, abduction and rotation make negligible contributions to the displacements of the COM in all three anatomical planes.     

Computed tomography-based joint locations affect calculation of joint moments during gait when compared to scaling approaches

Hip joint moments are an important parameter in the biomechanical evaluation of orthopaedic surgery. Joint moments are generally calculated using scaled generic musculoskeletal models. However, due to anatomical variability or pathology, such models may differ from the patient's anatomy, calling into question the accuracy of the resulting joint moments. This study aimed to quantify the potential joint moment errors caused by geometrical inaccuracies in scaled models, during gait, for eight test subjects. For comparison, a semi-automatic computed tomography (CT)-based workflow was introduced to create models with subject-specific joint locations and inertial parameters. 3D surface models of the femora and hemipelves were created by segmentation and the hip joint centres and knee axes were located in these models. The scaled models systematically located the hip joint centre (HJC) up to 33.6 mm too inferiorly. As a consequence, significant and substantial peak hip extension and abduction moment differences were recorded, with, respectively, up to 23.1% and 15.8% higher values in the image-based models. These findings reaffirm the importance of accurate HJC estimation, which may be achieved using CT- or radiography-based subject-specific modelling. However, obesity-related gait analysis marker placement errors may have influenced these results and more research is needed to overcome these artefacts.     

行走模式对人体下肢三维步态、肌电信号的影响

目的:揭示人体在主动和被动两种行走模式下的步态特征与下肢主要肌群的肌电信号变化规律。方法:选取12名在校男大学生,通过Greenjog履带式自发力跑台和h/p/cosmos电动跑台建立主动式和被动式行走模型,先后在两种模式下以3种递增速度即慢速(2 km/h)、常速(4 km/h)、和快速(6 km/h)进行一次性步行运动,运用三维运动捕捉分析系统和表面肌电测试仪,分别对每种速度采集2 min的连续三维步态和肌电信号数据。结果:慢速时,被动式行走的支撑相占比显著高于主动式行走(P<0.05),而摆动相占比和膝关节角度变化值则显著低于主动式行走(P<0.05);常速时,被动式行走的膝关节角度变化值和股二头肌积分肌电值显著低于主动式行走(P<0.05);快速时,被动式行走的膝关节、踝关节角度变化值和股二头肌积分肌电值显著低于主动式行走(P<0.01);此外,随着步行速度的增加,被动式行走的胫骨前肌积分肌电值呈现显著增高的趋势(P<0.05)。结论:随着步行速度的增加,人体主动步行时的膝关节活动范围更充分,对主动肌的锻炼作用更明显。     

A computational method for reliable gait event detection and abnormality detection for feedback in rehabilitation

In this paper, a gait event detection algorithm is presented that uses computer intelligence (fuzzy logic) to identify seven gait phases in walking gait. Two inertial measurement units and four force-sensitive resistors were used to obtain knee angle and foot pressure patterns, respectively. Fuzzy logic is used to address the complexity in distinguishing gait phases based on discrete events. A novel application of the seven-dimensional vector analysis method to estimate the amount of abnormalities detected was also investigated based on the two gait parameters. Experiments were carried out to validate the application of the two proposed algorithms to provide accurate feedback in rehabilitation. The algorithm responses were tested for two cases, normal and abnormal gait. The large amount of data required for reliable gait-phase detection necessitate the utilisation of computer methods to store and manage the data. Therefore, a database management system and an interactive graphical user interface were developed for the utilisation of the overall system in a clinical environment.     

Neuromusculoskeletal computer modeling and simulation of upright, straight-legged, bipedal locomotion of Australopithecus afarensis (A.L. 288-1)

The skeleton of Australopithecus afarensis (A.L. 288-1, better known as "Lucy") is by far the most complete record of locomotor morphology of early hominids currently available. Even though researchers agree that the postcranial skeleton of Lucy shows morphological features indicative of bipedality, only a few studies have investigated Lucy's bipedal locomotion itself. Lucy's energy expenditure during locomotion has been the topic of much speculation, but has not been investigated, except for several estimates derived from experimental data collected on other animals. To gain further insights into how Lucy may have walked, we generated a full three-dimensional (3D) reconstruction and forward-dynamic simulation of upright bipedal locomotion of this ancient human ancestor. Laser-scanned 3D bone geometries were combined with state-of-the-art neuromusculoskeletal modeling and simulation techniques from computational biomechanics. A detailed full 3D neuromusculoskeletal model was developed that encompassed all major bones, joints (10), and muscles (52) of the lower extremity. A model of muscle force and heat production was used to actuate the musculoskeletal system, and to estimate total energy expenditure during locomotion. Neural activation profiles for each of the 52 muscles that produced a single step of locomotion, while at the same time minimizing the energy consumed per meter traveled, were searched through numerical optimization. The numerical optimization resulted in smooth locomotor kinematics, and the predicted energy expenditure was appropriate for upright bipedal walking in an individual of Lucy's body size.     

Estimation of temporal gait parameters using Bayesian models on acceleration signals

The purpose of this study is to develop a system capable of performing calculation of temporal gait parameters using two low-cost wireless accelerometers and artificial intelligence-based techniques as part of a larger research project for conducting human gait analysis. Ten healthy subjects of different ages participated in this study and performed controlled walking tests. Two wireless accelerometers were placed on their ankles. Raw acceleration signals were processed in order to obtain gait patterns from characteristic peaks related to steps. A Bayesian model was implemented to classify the characteristic peaks into steps or nonsteps. The acceleration signals were segmented based on gait events, such as heel strike and toe-off, of actual steps. Temporal gait parameters, such as cadence, ambulation time, step time, gait cycle time, stance and swing phase time, simple and double support time, were estimated from segmented acceleration signals. Gait data-sets were divided into two groups of ages to test Bayesian models in order to classify the characteristic peaks. The mean error obtained from calculating the temporal gait parameters was 4.6%. Bayesian models are useful techniques that can be applied to classification of gait data of subjects at different ages with promising results     

Comparison of several techniques for the detection of apoptotic astrocytes in vitro

Implication of apoptosis in numerous physiological and pathological processes has resulted in the development of numerous methods to detect apoptosis, but none of them is adapted to all cell types. In this study, we induced apoptosis on murine immortalized astrocytes with urine from multiple sclerosis (MS) patients. Among techniques allowing the detection of apoptotic cells, only a few are adapted to adherent cells such as astrocytes. We compared several techniques (propidium iodide labelling and flow cytometry analysis, TUNEL and annexin V labelling in immunofluorescence, DNA ladder, ELISA tests to detect nucleosomes) in order to choose the method best adapted to our adherent cellular model and to discuss their practicability for the detection of apoptosis on adherent cells.
For technical course, propidium iodide labelling followed by flow cytometry analysis as a quantitative technique, and TUNEL in IF (easier and quicker than propidium iodide) as a semiquantitative test were both retained as best adapted to our case.
Moreover, in our model, we have observed that phosphatydilserine externalization and DNA fragmentation were concomittant after induction of apoptosis.
Techniques studied in this article would allow an enlarged study of the apoptotic mechanism in several pathologies by culture of adherent cells sensitive to apoptosis in vitro .     

Automatic detection of gait events: a case study using inductive learning techniques

One of the problems which occurs in the development of a control system for functional electrical stimulation of the lower limbs is to detect accurately specific events within the gait cycle. We present a method for the classification of phases of the gait cycle using the artificial intelligence technique of inductive learning. Both the terminology of inductive learning and the algorithm used for the analyses are fully explained. Given a set of examples of sensor data from the gait events that are to be delected, the inductive learning algorithm is able to produce a decision tree (or set of rules) which classify the data using a minimum number of sensors. The nature of the redundancy of the sensor set is examined by progressively removing combinations of sensors and noting the effect on both the size of the decision trees produced and their classification accuracy on ‘unseen’ testing data. Since the algorithm is able to calculate which sensors are more important (informative), comparisons with the intuitive appreciation of sensor importance of five researchers in the fields were made, revealing that those sensors which appear intuitively most informative may, in fact, provide the least information. Comparison results with the standard statistical classification technique of linear discriminant analysis are also presented, showing the relative simplicity of the inductively derived rules together with their good classification accuracy. In addition to the control of FES, such techniques are also applicable to automatic gait analysis and the construction of expert systems for diagnosis of gait pathologies.     

The effects of muscle vibration on gait control: a review

Abstract

Background: The purpose of the review is to summarize the literature surrounding the use of muscle vibration as it relates to modifying human gait.

Methods: After a brief introduction concerning historical uses and early research identifying the effect of vibration on muscle activation, we reviewed 32 articles that used muscle vibration during walking. The review is structured to address the literature within four broad categories: the effect of vibration to ‘trigger’ gait-like lower limb motions, the effect of vibration on gait control of healthy individuals and individuals with clinical conditions in which gait disorders are a prominent feature, and the effect of vibration training protocols on gait.

Results: The acute effects of vibration during gait involving healthy participants is varied. Some authors reported differences in segmental kinematic and spatiotemporal measures while other authors reported no differences in these outcome measures. The literature involving participants with clinical conditions revealed that vibration consistently had a significant impact on gait, suggesting vibration may be an effective rehabilitation tool. All of the studies that used vibration therapy over time reported significant improvement in gait performance.

Conclusions: This review highlights the difficulties in drawing definitive conclusions as to the impact of vibration on gait control, partly because of differences in walking protocols, site of vibration application, and outcome measures used across different investigative teams. It is suggested that the development of common investigative methodologies and outcome measures would accelerate the identification of techniques that may provide optimal rehabilitation protocols for individuals experiencing disordered gait control.     

Comparison of PERV genomic locations between Asian and European pigs

Xenotransplantation from pigs provides a possible solution to the shortage of human organs for allotransplantation. Porcine endogenous retroviruses (PERVs) are a possible obstacle to using porcine organs in addition to the immunological barriers. Three main types of PERVs (A, B and C) have been previously investigated in diverse pig breeds. To examine the copy numbers of PERVs and their genomic locations in the Korean native pig genome, we screened a BAC (Bacterial Artificial Chromosome) library with PERV-specific protease primers for initial recognition of PERV-positive clones and three sets of envelope-specific primers for the identification of PERV types. A total of 45 PERV-positive clones, nine PERV-A and 36 PERV-B, have been identified from the library screening and the BAC contigs were constructed using the primers designed from BAC end sequences (BESs). These primers were also used for SCH (Somatic Cell Hybrid) and RH (Radiation Hybrid) mapping of the PERV-positive clones. The results indicate that 45 PERV-positive BAC clones belong to nine contigs and a singleton. SCH and IMpRH (INRA-Minnesota Porcine Radiation Hybrid) mapping results indicated that there are at least eight separate PERV genomic locations, consisting of three PERV-A and five PERV-B. One contig could not be mapped, and two contigs are closely located on SSC7. Southern blotting indicates there may be up to 15 additional sites. Further investigation of these clones will contribute to a general strategy to generate PERV-free lines of pigs suitable for xenotransplantation.