Vinzenz von Tscharner

Intensity analysis in time-frequency space of surface myoelectric signals by wavelets of specified resolution.

Surface myoelectric signals often appear to carry more information than what is resolved in root mean square analysis of the progress curves or in its power spectrum. Time-frequency analysis of myoelectric signals has not yet led to satisfactory results in respect of separating simultaneous events in time and frequency. In this study a time-frequency analysis of the intensities in time series was developed. This intensity analysis uses a filter bank of non-linearly scaled wavelets with specified time-resolution to extract time-frequency aspects of the signal. Special procedures were developed to calculate intensity in such a way as to approximate the power of the signal in time. Applied to an EMG signal the intensity analysis was called a functional EMG analysis. The method resolves events within the EMG signal. The time when the events occur and their intensity and frequency distribution are well resolved in the intensity patterns extracted from the EMG signal. Averaging intensity patterns from multiple experiments resolve repeatable functional aspects of muscle activation. Various properties of the functional EMG analysis were shown and discussed using model EMG data and real EMG data.

Time-frequency and principal-component methods for the analysis of EMGs recorded during a mildly fatiguing exercise on a cycle ergometer

Abstract Electromyographic signals contain the information on muscle activity and have to be frequently averaged, compared, classified or details need to be extracted. A time–frequency analysis, based on wavelets, was previously presented. The analysis transformed an EMG signal into an EMG-intensity-pattern showing the intensities at any point in time for the frequencies filtered out by the wavelets. The purpose of the present study was: 1. to define and apply a new EMG-pattern-space for the analysis of EMG-intensity-patterns; and 2. to determine the variation of EMG-intensity-patterns while getting mildly fatigued by cycling on a cycle-ergometer. The coordinates spanning the pattern space were principal components of the measured EMG-intensity-patterns. A point in pattern-space thus represented an EMG-intensity-pattern. Fatigue resulted in points moving along a line in pattern space. The line was characterized by an intercept at time 0 and a slope. Thus mild fatigue caused a shift from an initial intensity-pattern representing the intercept to a final intensity-pattern adding gradually larger amounts of the pattern representing the slope. The intensity-pattern of the slope revealed the physiologically important individual strategies for coping with mild fatigue. Changes were observed at different times and at different frequencies during the cycling movement.

Changes in EMG signals for the muscle tibialis anterior while running barefoot or with shoes resolved by non-linearly scaled wavelets

The purpose of this project was to study the EMG pattern of the tibialis anterior muscle in heel-toe running. Specifically, EMG changes in time, intensity and frequency shortly before and after heel-strike were addressed using an EMG-specific non-linearly scaled wavelets analysis. This method allowed extracting the time, intensity and frequency information inherent in the EMG signal at any time. The EMG signals of 40 male subjects were recorded for running barefoot and with shoes. The results confirmed that the pre-heel-strike EMG activities were typically seen at higher EMG frequencies (60-270Hz) while the post-heel-strike EMG activities resulted in lower frequency signals (10-90Hz). The timing of the pre-heel-strike EMG activities was not influenced by the used shoe conditions. The timing of the post-heel-strike EMG activities was significantly delayed when wearing shoes. The intensity of the pre-heel-strike muscle activity increased compared to the post-heel-strike one when wearing shoes. One can conclude that the activity of the tibialis anterior adjusts specifically to exterior conditions. The frequency shift between pre- and post heel-strike muscle activity were discussed with respect to activation of different motor units.

Methodological aspects of EEG and body dynamics measurements during motion

EEG involves the recording, analysis, and interpretation of voltages recorded on the human scalp which originate from brain gray matter. EEG is one of the most popular methods of studying and understanding the processes that underlie behavior. This is so, because EEG is relatively cheap, easy to wear, light weight and has high temporal resolution. In terms of behavior, this encompasses actions, such as movements that are performed in response to the environment. However, there are methodological difficulties which can occur when recording EEG during movement such as movement artifacts. Thus, most studies about the human brain have examined activations during static conditions. This article attempts to compile and describe relevant methodological solutions that emerged in order to measure body and brain dynamics during motion. These descriptions cover suggestions on how to avoid and reduce motion artifacts, hardware, software and techniques for synchronously recording EEG, EMG, kinematics, kinetics, and eye movements during motion. Additionally, we present various recording systems, EEG electrodes, caps and methods for determinating real/custom electrode positions. In the end we will conclude that it is possible to record and analyze synchronized brain and body dynamics related to movement or exercise tasks.

Discrimination of gender-, speed-, and shoe-dependent movement patterns in runners using full-body kinematics.

Changes in gait kinematics have often been analyzed using pattern recognition methods such as principal component analysis (PCA). It is usually just the first few principal components that are analyzed, because they describe the main variability within a dataset and thus represent the main movement patterns. However, while subtle changes in gait pattern (for instance, due to different footwear) may not change main movement patterns, they may affect movements represented by higher principal components. This study was designed to test two hypotheses: (1) speed and gender differences can be observed in the first principal components, and (2) small interventions such as changing footwear change the gait characteristics of higher principal components. Kinematic changes due to different running conditions (speed - 3.1m/s and 4.9 m/s, gender, and footwear - control shoe and adidas MicroBounce shoe) were investigated by applying PCA and support vector machine (SVM) to a full-body reflective marker setup. Differences in speed changed the basic movement pattern, as was reflected by a change in the time-dependent coefficient derived from the first principal. Gender was differentiated by using the time-dependent coefficient derived from intermediate principal components. (Intermediate principal components are characterized by limb rotations of the thigh and shank.) Different shoe conditions were identified in higher principal components. This study showed that different interventions can be analyzed using a full-body kinematic approach. Within the well-defined vector space spanned by the data of all subjects, higher principal components should also be considered because these components show the differences that result from small interventions such as footwear changes.

Classification of multi muscle activation patterns of osteoarthritis patients during level walking

The study compares the timing and frequency changes of surface EMGs recorded from osteoarthritis patients with previous traumatic ankle injury and normal subjects during level walking. EMG intensity (power) was obtained by a wavelet analysis. There were intensity values for each frequency characterized by the wavelets for every time point. The intensities were compounded into Multi Muscle Patterns (MMP) simultaneously showing the time and spectral aspects of the lower leg muscle activity. The aim of the study was to test the hypothesis that the differences between the group of the MMPs from the affected leg (AFL) and the not affected leg (NAL) allow detecting whether a newly measured MMP results from an AFL or NAL. This hypothesis was tested by a spherical classification procedure yielding the correctly classified MMPs thus indicating the significance of the differences between the MMPs of the AFL and NAL. The hypothesis was supported (not falsified) by the results. Thus there were common features of muscle activity in the AFL of most osteoarthritis patients that allowed detecting whether the MMP of a new patient was of the kind seen in most other osteoarthritis patients. The spectral, timing and intensity factors in the MMP that allowed this classification were visualized in the mean MMPs of the patients and the control group. The comparison revealed where on average the relative timing and spectral differences of the muscle activation of osteoarthritis patients and control subjects occurred.

Subspace Identification and Classification of Healthy Human Gait

Purpose The classification between different gait patterns is a frequent task in gait assessment. The base vectors were usually found using principal component analysis (PCA) is replaced by an iterative application of the support vector machine (SVM). The aim was to use classifyability instead of variability to build a subspace (SVM space) that contains the information about classifiable aspects of a movement. The first discriminant of the SVM space will be compared to a discriminant found by an independent component analysis (ICA) in the SVM space. Methods Eleven runners ran using shoes with different midsoles. Kinematic data, representing the movements during stance phase when wearing the two shoes, was used as input to a PCA and SVM. The data space was decomposed by an iterative application of the SVM into orthogonal discriminants that were able to classify the two movements. The orthogonal discriminants spanned a subspace, the SVM space. It represents the part of the movement that allowed classifying the two conditions. The data in the SVM space was reconstructed for a visual assessment of the movement difference. An ICA was applied to the data in the SVM space to obtain a single discriminant. Cohens d effect size was used to rank the PCA vectors that could be used to classify the data, the first SVM discriminant or the ICA discriminant. Results The SVM base contains all the information that discriminates the movement of the two shod conditions. It was shown that the SVM base contains some redundancy and a single ICA discriminant was found by applying an ICA in the SVM space. Conclusions A combination of PCA, SVM and ICA is best suited to extract all parts of the gait pattern that discriminates between the two movements and to find a discriminant for the classification of dichotomous kinematic data.

Removal of the electrocardiogram signal from surface EMG recordings using non-linearly scaled wavelets

The surface electromyographic (EMG) signal (EMG signal) recorded on some areas of the body, especially from the trunk, is often contaminated with heart muscle electrical activity (ECG) caused by the proximity of the collection sites to the heart. It is therefore necessary to suppress or separate the ECG signal from the EMG signal during the analysis. However, the suppression should not eliminate low frequency components of the EMG signal. The purpose of this study was to develop a method to remove the ECG from contaminated EMG signals by combining the wavelet transform with an independent component analysis using the wavelet spectra. In contrast to other methods, this method uses the spectral differences of the EMG and ECG signals for the discrimination. Hence, no separately measured reference ECG signal is required. The method removes ECG contaminations of various shapes. It is superior to filtering with a Butterworth filter because it does not eliminate the low frequency EMG signals in the range between 10 and 50 Hz. It is known that the information contained in different frequency bands of the EMG is not identical. It is therefore important to retain the EMG signal from high and low frequencies which is possible by applying the presented cleaning procedure.

Quantification and reliability of center of pressure movement during balance tasks of varying difficulty

Postural control is often assessed by quantifying the magnitude of the center of pressure (COP) movement. However, these measures usually focus on the gross amount of movement and ignore the temporal structure of the COP signal. A novel non-linear analysis technique was recently developed to characterize the temporal structure of the COP signal with an output termed the entropic half-life [E(1/2)]. The E(1/2) reflects how much of the previous postural position is used to determine the current postural control strategy (memory effect). The purpose of this study was to quantify the E(1/2) and four COP movement magnitude measurements (medio-lateral and anterior-posterior excursion, path length, 95% ellipse area) for balance tasks increasing in sensory difficulty, as well as the test-retest reliability of each measure. Twenty-seven healthy young adults completed single limb stance tasks varying in sensory difficulty (rigid surface eyes open, rigid surface eyes closed, foam surface eyes open) on two separate occasions. Relative reliability was assessed using an intraclass correlation coefficient (ICC3,3). Absolute reliability was assessed using the standard error of the measurement (SEM) and the sensitivity of the measurement to true changes was assessed using the minimal detectable change (MDC95). The E(1/2) was found to have excellent reliability for all tasks tested (ICC range 0.82-0.91, SEM range 3.5-14.1 mm, MCD95 range 9.7-39.2 mm). The high reliability of the E(1/2) was comparable to that of movement magnitude measurements. This may be used in order to better understand the underlying motor control system.

Unstable shoes: functional concepts and scientific evidence

The purpose of this study was to discuss (a) the conceptual idea behind unstable footwear and (b) the validity and scientific support of some selected claims made with respect to unstable shoes. The concept is that unstable shoes are built to provide a training device that uses instability as a strategy to train and strengthen muscles in the human locomotor system. Specific claims are: (1) evidence shows that unstable shoes currently on the market produce a substantial and significant increase in instability. The effects are most evident during standing but are also apparent in walking. (2) Unstable shoes increase the activity in certain muscles in about 80% of the population. The affected muscles change between different subjects. The highest relative increases were found in the small muscles crossing the ankle joint complex. (3) ‘Muscle toning’ is not defined and experimental data associating ‘muscle toning’ with unstable shoes are not available. (4) There is evidence that unstable shoes improve the static balance performance of users whose balance skills are low. (5) There is indirect evidence that unstable shoes reduce forces in the joints of the lower extremities. (6) There is evidence that unstable shoes can reduce the level of perceived pain. This has been confirmed in subjects suffering from pain in the knee joint and for subjects with low back pain. Based on these results, it seems that unstable shoes are associated with several possible benefits. However, the effects are not consistent between different subjects. In our experience, positive effects can be shown for about 80% of the test subjects.