Vladimir Medved

Measurement of Human Locomotion

The importance of measurements to properly assess human locomotion is increasingly recognized. Already well established as an experimental scientific research tool, human locomotion measurements are frequently a routine clinical application. Fields of application encompass both healthy and pathological locomotion encountered in rehabilitation medicine, orthopedics, kinesiology, sports science, and other related fields. This volume provides comprehensive description of instrument systems for measurement of kinematics of human movement, kinetic quantities experienced by the human body in contact with the ground, and myoelectric changes associated with locomotor activity. Physical principles governing the operation of several measurement devices and relevant mathematics and engineering are presented, as well as signal processing issues that must be addressed in order to obtain and use quantitative measurement variables in the biomechanical context. Measurement data acquisiton, processing, and presentation to the user in a computer-based laboratory environment are explained. The ultimate goal is to contribute to the diagnostics and treatment of specific locomotion patterns. References to major historical landmarks in the development of measurement methodology are provided as well. Selected experimental data are shown and interpreted to illustrate the methods, some originating from the authors own research. Consequently, the reader will gain insight into the working principles, typical uses, and comparative advantages of a number of instruments, such as simple electrogoniometers, sophisticated stereometric instruments to capture human body kinematics, imbedded force plates, distributed pressure measurement systems, wire and telemetry electromyographs, etc. Systems oriented and interdisciplinary in character, this volume addresses biomedical engineers, active in industry or the clinical environment, physicians, kinesiologists, physical therapists, and students and researchers of human movement in clinics and academia. By focusing on locomotion measurements, the volume attempts to complement classical biomechanics, neurophysiology, and motor control-oriented texts.

Measurement and analysis of surface myoelectric signals during fatigued cyclic dynamic contractions

Abstract A method of surface myoelectric (ME) signal measurement and analysis, with the aim of evaluating muscle fatigue during cyclic dynamic contractions of quadriceps muscle (lower leg extension and flexion exercise on ‘leg-extension’ training device), was developed. As an indicator of muscle fatigue a change in the power spectrum median frequency (MF), calculated from the spectrogram, has been used. Our method considers the maximum median frequency values during each contraction. A slope of the regression line (Hz/min) that fits the maximum values of MF in a least-square sense was used as a fatigue index. Dependencies of the muscle fatigue index, as well as median frequencies at the beginning of the exercise on spectrogram analysis parameters were considered, and analysis parameters for reliable results were chosen. Values of the slope of the regression line and MF at the beginning of an exercise are in agreement with the notion that rectus femoris has higher muscle fibre conduction velocity than both vasti muscles.

The application of Hilbert–Huang transform in the analysis of muscle fatigue during cyclic dynamic contractions

Surface electromyography (sEMG) is a common technique used in the assessment of local muscle fatigue. As opposed to static contraction situations, sEMG recordings during dynamic contractions are particularly characterised by non-stationary (and non-linear) features. Standard signal processing methods using Fourier and wavelet based procedures demonstrate well known restrictions on time–frequency resolution and the ability to process non-stationary and/or non-linear time-series, thus aggravating the spectral parameters estimation. The Hilbert–Huang transform (HHT), comprising of the empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), provides a new approach to overcome these issues. The time-dependent median frequency estimate is used as muscle fatigue indicator, and linear regression parameters are derived as fatigue quantifiers. The HHT method is utilised for the analysis of the sEMG signals recorded over quadriceps muscles during cyclic dynamic contractions. The results are compared with those obtained by the Fourier and wavelet based methods. It is shown that HHT procedure provides the most consistent and reliable assessment of spectral and derived linear regression parameters, given the time epoch width and sampling interval in the time domain. The suggested procedure successfully deals with non-stationary and non-linear properties of biomedical signals.

A marker-free method to estimate joint centre of rotation by video image processing.

A marker-free video measurement and image processing method that provides numerical estimation of the 2-D centre of rotation of one rigid segment is tested. The algorithm is based on binary region moment features. A comparison is made between this method and a marker-based one, where the location of the markers has been calculated in two ways. The algorithm is also extended to handle two rigid segments. The method is to be applied in human locomotion analysis in order to calculate the centre of rotation of the hip joint. Its accuracy has been tested by a comparison with in vivo radiological measurements on humans.

Locomotion diagnostics: Some neuromuscular and robotic aspects

Some aspects of neuromuscular function evaluation in fast sports locomotion are presented. The procedure of locomotion diagnostics and the underlying principles are discussed, and experimental data for their support are given. These data concern the evaluation of locomotor skill and of muscle fatigue. The approach processes some features similar to robotic systems study and is potentially applicable to other (ergonomic, medical) areas as well. Among the major limitations of the methodology are a relatively crude representation of the neuromuscular system, with only a few major muscles (and only in the lower extremities) monitored, and the fact that the EMG gives an indication of an actively generated muscle force only, and not of significant elastic force components.<<ETX>>

Method to evaluate the skill level in fast locomotion through myoelectric and kinetic signal analysis

The paper describes a method aimed at providing objective diagnostic testing of skilled locomotor stereotypes. Bioelectric muscle activity indices and ground reaction force data are used to represent a movement structure, in a schematised way, using discrete states in time. Athletes were asked to perform one specific movement structure: a backward somersault from the standing position. Mathematical analyses of measured signals reveal the significance, for the skill level evaluation, of parameters reflecting the impulsive take-off force waveform and the symmetry in EMG activity of ankle extensor muscles, which therefore might be used as diagnostic criteria. Within technical limitations, this approach may also be applied to other locomotor patterns and possibly to monitor the progress in motorics in pathological locomotion. EMG telemetry could significantly enhance the methods scope.

Dynamics stability in landing with ACL deficient knee joint

The purpose of this research was to determine the effect of the controlled landing tests in dynamics stabilities of ACL deficient knee pre- and post reconstruction. The test protocol included experimental and control groups. The experimental group included examines with ACL deficient knee while control group consisted of healthy examines. The protocol consisted of following testing phases: 1. testing of the knee joint muscle power (execution of the maximum voluntary contraction during which the myoelectric signals were collected) and 2. testing of the knee joint dynamic stability (execution of the test with one-legged (landings) from the 40 cm and 20 cm high bench, during which the kinematic and kinetic parameters were collected). Following variables were studied: kinematic (valgus and varus, inner and outer rotation and angle of the flexion and extension in the knee joint), ground reaction force and EMG signals of the four main muscle of lower extremity have been measured during execution of the maximum static contraction lasting for 10 seconds. The protocol consisted of two testing period time pre- and post reconstruction of ACL. Statistically significant difference between groups in the first measurement as well as between injured and healthy knee was observed. That was the case especially in kinematical parameters (in inner and outer rotation angle as well as valgus-varus movement). Generally, a change in landing dynamics of the injured knee was observed. The differences are reduced following the reconstruction, and that is especially the case in examinees that underwent proprioceptive treatment during rehabilitation. The test could be applicable as an indicator of readiness of athletes to sustain training workload as well as a predictor of injury occurrence in healthy people.

Micro-Computer Based Measurement and Processing Method for Human Locomotor System’s Take-Off Properties Evaluation

Viewed as a mechanical system human body possesses interesting properties. Study of movement and locomotion of the body, as a whole, has revealed many features that can adequately be modeled, on the macroscopic level, by mechanical analogs. Biomechanical functioning of the body cannot eventually be separated from the physiological one - these two approaches being essentially integrated (1,2). In spite of this, the study of mechanical (kinematic and kinetic) variables of movement only has nevertheless led to many important findings regarding the interplay between active (muscle generated), passive and inertial forces and moments of forces the body is constrantly being subjected to in the course of various movement patterns.

Human Gait Data Mining by Symbol Based Descriptive Features

Most medical applications which include large dataset require search by example capability. Such similarity- based retrieval has attracted a great deal of attention in recent years. Although several different approaches have appeared, most are not specialized for problems of time series signals, typically found in gait analysis or ECG. This paper proposes an approach for efficient processing of human gait by using symbolization of different features by means of feature description. We evaluated this approach against database that holds limited set of kinematics, kinetic and EMG data, describing simple step (a subset of standard gait variables). The measurement was performed in the Biomechanics Laboratory at the Faculty of Kinesiology, University of Zagreb, using 2002-ELITE system with Kistler platform (40 cm by 60 cm).

Towards a High Performance Expert System for Gait Analysis

In a number of decision support systems searching through large data warehouse based on sample sequence is desirable. Gait analysis systems store demographic data (categorical, numerical data) and time sequence data (human gait variables) where query by example is needed for matching signal patterns. Although several different approaches have appeared, most of them are not specialized for the problem which combines simple facts and time series data or data warehousing principles for such data. We propose an end-to-end approach including data warehouse partitioning, symbolic-based algorithms, clustering-based algorithms for purpose of fast and intelligent retrieval and classification of stored facts. This paper gives the modified version of symbolic algorithm that allows dimensionality reduction and indexing with a lower-bounding distance measure. Paper shows how it can be used together with clustering based algorithms for efficient processing of time-series data stored in large fact tables of a data warehouse. Each symbolic word serves as input to hierarchical clustering algorithm and forms a node in the dendrogram representation (graph which displays nodes arranged into hierarchy). In a data warehouse this design improves performance in addition to denormalization, by adding derived information and summary tables. Input signal is divided into segments to reduce the length of the symbolic words. Since each word is represented as a simple fact in a database, hierarchical clustering algorithms can be applied. We have tested this approach on human locomotion data combining categorical, numerical and time series variables. Input dataset included ground reaction force during a simple step, representing a subset of standard gait variables, demographic and anthropometric parameters. The measurements were performed in the Biomechanics Laboratory at the Faculty of Kinesiology, University of Zagreb, using 2002-ELITE system with Kistler platform (40 cm by 60 cm).