Determination of typical patterns from strongly varying signals

Forces measured in human joints vary considerably when an activity such as walking is carried out by different subjects or when it is repeated. ‘Typical’ standardised force–time patterns are needed to test and improve joint implants. Mechanically most important for their endurance are the magnitudes and times of force maxima and minima. They should equal the arithmetic means from the single measurements. Similar problems exist when evaluating other strongly varying signals, as in gait analysis. The new method to calculate typical signals (TSs) enhances existing dynamic time warping (DTW) procedures. It allows us to combine any number of signals. The sequence of input signals – used for calculating the TS – has only a minor influence. The accuracy of the method was tested numerically on signals for which the typical patterns could be defined exactly, and also on real joint forces that varied to different extents.     

An equilibrium-point model of electromyographic patterns during single-joint movements based on experimentally reconstructed control signals

The purpose of this work has been to develop a model of electromyographic (EMG) patterns during single-joint movements based on a version of the equilibrium-point hypothesis, a method for experimental reconstruction of the joint compliant characteristics, the dual-strategy hypothesis, and a kinematic model of movement trajectory. EMG patterns are considered emergent properties of hypothetical control patterns that are equally affected by the control signals and peripheral feedback reflecting actual movement trajectory. A computer model generated the EMG patterns based on simulated movement kinematics and hypothetical control signals derived from the reconstructed joint compliant characteristics. The model predictions have been compared to published recordings of movement kinematics and EMG patterns in a variety of movement conditions, including movements over different distances, at different speeds, against different-known inertial loads, and in conditions of possible unexpected decrease in the inertial load. Changes in task parameters within the model led to simulated EMG patterns qualitatively similar to the experimentally recorded EMG patterns. The model's predictive power compares it favourably to the existing models of the EMG patterns.     

Averaging of strongly varying signals.

Forces acting at the hip joint during a given activity often vary much between trials and subjects. Large variations are also encountered in many other biomechanical signals. Arithmetic mean curves then lead to falsified results, especially if extreme values occur at very different times. A method was developed for calculating a typical curve from such varying, time dependent signals. All cycle times are first averaged and the signals are then more and more smoothed using Fourier series with decreasing numbers of harmonics. The remaining extrema are analysed to decide whether they are typical for all curves or not. This is done by systematically cutting off a varying number of extrema at the beginning or end of all curves. After this an equal number of extrema remains in all curves. These extrema are then shifted to average positions in time, i.e. the times between consecutive extrema are compressed or expanded, and the standard deviation of all curves is calculated. The combination of cut off extrema which results in the smallest standard deviation is then used further on. The same time distortions are applied to the original curves and their arithmetic mean finally results in the typical signal. This procedure is well suited for averaging hip contact forces and other varying signals as long as their complexity and variation is not extremely large.     

Glenohumeral joint cartilage contact in the healthy adult during scapular plane elevation depression with external humeral rotation

The shoulder (glenohumeral) joint has the greatest range of motion of all human joints; as a result, it is particularly vulnerable to dislocation and injury. The ability to non-invasively quantify in-vivo articular cartilage contact patterns of joints has been and remains a difficult biomechanics problem. As a result, little is known about normal in-vivo glenohumeral joint contact patterns or the consequences that surgery has on altering them. In addition, the effect of quantifying glenohumeral joint contact patterns by means of proximity mapping, both with and without cartilage data, is unknown. Therefore, the objectives of this study are to (1) describe a technique for quantifying in-vivo glenohumeral joint contact patterns during dynamic shoulder motion, (2) quantify normal glenohumeral joint contact patterns in the young healthy adult during scapular plane elevation depression with external humeral rotation, and (3) compare glenohumeral joint contact patterns determined both with and without articular cartilage data. Our results show that the inclusion of articular cartilage data when quantifying in-vivo glenohumeral joint contact patterns has significant effects on the anterior–posterior contact centroid location, the superior–inferior contact centroid range of travel, and the total contact path length. As a result, our technique offers an advantage over glenohumeral joint contact pattern measurement techniques that neglect articular cartilage data. Likewise, this technique may be more sensitive than traditional 6-Degree-of-Freedom (6-DOF) joint kinematics for the assessment of overall glenohumeral joint health. Lastly, for the shoulder motion tested, we found that glenohumeral joint contact was located on the anterior–inferior glenoid surface.     

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.     

Cross-correlation as a method for comparing dynamic electromyography signals during gait

Current clinical interpretation of dynamic electromyography (EMG) data is usually based on qualitative assessments of muscle timing. Cross-correlation may provide a method for objectively comparing the timing and shape of EMG signals. This study used cross-correlation to compare EMG signals from different walking trials, different test sessions, and different individuals in able-bodied adults. Cross-correlation results (R-values) for different walking trials within a single test session were high, averaging > or = 0.90 for all muscles tested (R = 1.0 indicates exact agreement). Cross-correlation values were also high among trials from different test sessions conducted by the same and different examiners (average R > or = 0.78 for all muscles). R-values were much more variable when comparing different subjects (average 0.40-0.81, range 0.00-0.91). R-values were lower for the medial hamstrings and rectus femoris compared with the other muscles tested. These results suggest that cross-correlation may be useful for evaluating changes in an individual patient's muscle activation patterns, such as before and after surgery, but not for comparing EMG patterns among different individuals, such as between patients and normative data. This is especially true for biarticular muscles such as the hamstrings and rectus femoris, which may have variable activation patterns and/or increased sensitivity to electrode placement. Cross-correlation may also be useful for identifying appropriate muscles for transfer, identifying "outlier" trials within a test session, and selecting representative EMG curves for a given patient. The advantages of cross-correlation are that it considers shape of the EMG signal in addition to timing and that the assessments it provides are objective, rather than subjective.     

An open source lower limb model: Hip joint validation

Musculoskeletal lower limb models have been shown to be able to predict hip contact forces (HCFs) that are comparable to in vivo measurements obtained from instrumented prostheses. However, the muscle recruitment predicted by these models does not necessarily compare well to measured electromyographic (EMG) signals. In order to verify if it is possible to accurately estimate HCFs from muscle force patterns consistent with EMG measurements, a lower limb model based on a published anatomical dataset (Klein Horsman et al., 2007. Clinical Biomechanics. 22, 239-247) has been implemented in the open source software OpenSim. A cycle-to-cycle hip joint validation was conducted against HCFs recorded during gait and stair climbing trials of four arthroplasty patients (Bergmann et al., 2001. Journal of Biomechanics. 34, 859-871). Hip joint muscle tensions were estimated by minimizing a polynomial function of the muscle forces. The resulting muscle activation patterns obtained by assessing multiple powers of the objective function were compared against EMG profiles from the literature. Calculated HCFs denoted a tendency to monotonically increase their magnitude when raising the power of the objective function; the best estimation obtained from muscle forces consistent with experimental EMG profiles was found when a quadratic objective function was minimized (average overestimation at experimental peak frame: 10.1% for walking, 7.8% for stair climbing). The lower limb model can produce appropriate balanced sets of muscle forces and joint contact forces that can be used in a range of applications requiring accurate quantification of both. The developed model is available at the website https://simtk.org/home/low_limb_london.     

Drumming signals within the family Taeniopterygidae (Plecoptera)

Drumming signals of the 11 species of Palaearctic Taeniopterygidae are described for the first time based on the study of populations from nine different European countries from Spain to Russia. In this way, a contribution is made to our knowledge of the constancy respectively the divergence of signals typical for the species distributed over a very wide area. Communication patterns of the family under varying temperatures were analysed. The dependence on temperature indicates that the adults of this family are cold stenothermic insects. Within the genus Brachyptera (Newport, 1851), the call signal of males is significantly different from their response signal (duration and drumming frequency). The marked modulation of the drumming frequency, e.g., of B. risi (Morton, 1896) and B. seticornis (Klapálek, 1902) is reached by a modulation of the amplitude of abdominal movements. Based on the study of the drumming signals, Taeniopteryx auberti (Kis and Sowa, 1964) is proposed as a subspecies of T. hubaulti (Aubert, 1946). Actual intensity of the drumming signals of T. auberti was measured. Taeniopteryx hubaulti is recorded for the first time outside the Alps.     

A reductionist approach to the analysis of learning in brain–computer interfaces

The complexity and scale of brain–computer interface (BCI) studies limit our ability to investigate how humans learn to use BCI systems. It also limits our capacity to develop adaptive algorithms needed to assist users with their control. Adaptive algorithm development is forced offline and typically uses static data sets. But this is a poor substitute for the online, dynamic environment where algorithms are ultimately deployed and interact with an adapting user. This work evaluates a paradigm that simulates the control problem faced by human subjects when controlling a BCI, but which avoids the many complications associated with full-scale BCI studies. Biological learners can be studied in a reductionist way as they solve BCI-like control problems, and machine learning algorithms can be developed and tested in closed loop with the subjects before being translated to full BCIs. The method is to map 19 joint angles of the hand (representing neural signals) to the position of a 2D cursor which must be piloted to displayed targets (a typical BCI task). An investigation is presented on how closely the joint angle method emulates BCI systems; a novel learning algorithm is evaluated, and a performance difference between genders is discussed.     

Effects of EMG processing on biomechanical models of muscle joint systems: sensitivity of trunk muscle moments, spinal forces, and stability

Biomechanical models are in use to estimate parameters such as contact forces and stability at various joints. In one class of these models, surface electromyography (EMG) is used to address the problem of mechanical indeterminacy such that individual muscle activation patterns are accounted for. Unfortunately, because of the stochastical properties of EMG signals, EMG based estimates of muscle force suffer from substantial estimation errors. Recent studies have shown that improvements in muscle force estimation can be achieved through adequate EMG processing, specifically whitening and high-pass (HP) filtering of the signals. The aim of this paper is to determine the effect of such processing on outcomes of a biomechanical model of the lumbosacral joint and surrounding musculature. Goodness of fit of estimated muscle moments to net moments and also estimated joint stability significantly increased with increasing cut-off frequencies in HP filtering, whereas no effect on joint contact forces was found. Whitening resulted in moment estimations comparable to those obtained from optimal HP filtering with cut-off frequencies over 250 Hz. Moreover, compared to HP filtering, whitening led to a further increase in estimated joint-stability. Based on theoretical models and on our experimental results, we hypothesize that the processing leads to an increase in pick-up area. This then would explain the improvements from a better balance between deep and superficial motor unit contributions to the signal.     

Foot joint coupling variability differences between habitual rearfoot and forefoot runners prior to and following an exhaustive run

As joint coupling variability has been associated with running-related lower extremity injury, the purpose of this study was to identify how variability within the foot may be different between forefoot (FFS) and rearfoot strike (RFS) runners. Identifying typical variability in uninjured runners may contribute to understanding of ideal coordination associated with running foot strike patterns.Fifteen FFS and 15 RFS runners performed a maximal-effort 5 km treadmill run. A 7-segment foot model identified 6 functional articulations (rearfoot, medial and lateral midfoot and forefoot, and 1st metatarsophalangeal) for analysis. Beginning and end of the run motion capture data were analyzed. Vector coding was used to calculate 6 joint couples. Standard deviations of the coupling angles were used to identify variability within subphases of stance (loading, mid-stance, terminal, and pre-swing). Mixed between-within subjects ANOVAs compared differences between the foot strikes, pre and post run.Increased variability was identified within medial foot coupling for FFS and within lateral foot coupling for RFS during loading and mid-stance. The exhaustive run increased variability during mid-stance for both groups.Interpretation. Joint coupling variability profiles for FFS and RFS runners suggest different foot regions have varying coordination needs which should be considered when comparing the strike patterns.     

Multimodal signalling in estrildid finches: song,dance and colour are associated with different ecological and life‐history traits

Sexual traits (e.g. visual ornaments, acoustic signals, courtship behaviour) are often displayed together as multimodal signals. Some hypotheses predict joint evolution of different sexual signals (e.g. to increase the efficiency of communication) or that different signals trade off with each other (e.g. due to limited resources). Alternatively, multiple signals may evolve independently for different functions, or to communicate different information (multiple message hypothesis). We evaluated these hypotheses with a comparative study in the family Estrildidae, one of the largest songbird radiations, and one that includes many model species for research in sexual selection and communication. We found little evidence for either joint evolution or trade‐offs between song and colour ornamentation. Some negative correlations between dance repertoire and song traits may suggest a functional compromise, but generally courtship dance also evolved independently from other signals. Instead of correlated evolution, we found that song, dance and colour are each related to different socio‐ecological traits. Song complexity evolved together with ecological generalism, song performance with investment in reproduction, dance with commonness and habitat type, whereas colour ornamentation was shown previously to correlate mostly with gregariousness. We conclude that multimodal signals evolve in response to various socio‐ecological traits, suggesting the accumulation of distinct signalling functions.     

Plurisegmental neurones of the cricket Gryllus campestris L. that discharge in the rhythm of its own song

Male crickets (Gryllus campestris L.) mounted so that their wings and abdomen could move freely were induced to stridulate by brain lesion. During the song the activity of single neurones was recorded extracellularly in a cervical connective. Nine distinct spike patterns were observed. Patterns I and II tend to copy the chirp as a whole rather than the onset of the syllables (the recorded potentials of the wing-opener muscle M99 marked the syllable onset). The other patterns reflect the syllabic structure. Each, in its own way, marks the various syllables with different numbers of spikes. The delay of the spike response is different for each pattern. Some patterns, but not others, also reflect the beginning or end of the song, or the abdominal expiratory activity. One neurone also responds in correlation with muscle discharges typical of the courtship song. In some of the patterns it is evident that there is a stronger correlation with the closer muscle (M90) discharge than with the opener muscle discharge. Activation by auditory self-stimulation by way of the tympanal organs can be ruled out for all patterns. It is possible that patterns I–V are induced by afferent activity coupled to the wing movement. Patterns VI–VIII are probably copies of motor signals ascending from the thoracic song-pattern generators to the head ganglia. It is evident that the head ganglia have detailed information as to the motor output for stridulation and abdominal expiration.     

A model of control of the movement of the multiarticular extremity

A model for coordinated execution of multijoint goal-directed limb movements is suggested from the following principles. (1) Central control signals for a single limb joint are individually formed, proceeding from its ability to bring the limb nearer to the target and leaving control signals directed simultaneously to other joint out of account. The joints thereby behave as a set of Tsetlin's abstract automata [11], each functioning independently and guided by a common, collective effect. (2) Neither levels of muscle activation, nor force and kinematic variables are directly specified by the command signals. They only modify the system's parameters that affect equilibrium joint positions, and thus make the limb to move to the goal. A concrete model based on the above principles is described and its behavior is compared with actual goal-directed movements in man and spinal frogs. Various control strategies for multiarticular movements in living organisms are discussed.     

Compensatory effect of the minor Streptomyces lividans type I signal peptidases on the SipY major signal peptidase deficiency as determined by extracellular proteome analysis

The developmentally complex bacterium Streptomyces lividans has the ability to produce and secrete a significant amount of protein and possesses four different type I signal peptidase genes (sipW, sipX, sipY and sipZ) that are unusually clustered in its chromosome. 2-DE and subsequent MS of extracellular proteins showed that proteins with typical export signals for type I and type II signal peptidases are the main components of the S. lividans secretome. Secretion of extracellular proteins is severely reduced in a strain deficient in the major type I signal peptidase (SipY). This deficiency was efficiently compensated by complementation with any of the other three signal peptidases as deduced from a comparison of the corresponding 2-D PAGE patterns with that of the wild-type strain.     

Vibration signals from the FT joint can induce phase transitions in both directions in motoneuron pools of the stick insect walking system

The influence of vibratory signals from the femoral chordotonal organ fCO on the activities of muscles and motoneurons in the three main leg joints of the stick insect leg, i.e., the thoraco-coxal (TC) joint, the coxa-trochanteral (CT) joint, and the femur-tibia (FT) joint, was investigated when the animal was in the active behavioral state. Vibration stimuli induced a switch in motor activity (phase transition), for example, in the FT joint motor activity switched from flexor tibiae to extensor tibiae or vice versa. Similarly, fCO vibration induced phase transitions in both directions between the motoneuron pools of the TC joint and the CT joint. There was no correlation between the directions of phase transition in different joints. Vibration stimuli presented during simultaneous fCO elongation terminated the reflex reversal motor pattern in the FT joint prematurely by activating extensor and inactivating flexor tibiae motoneurons. In legs with freely moving tibia, fCO vibration promoted phase transitions in tibial movement. Furthermore, ground vibration promoted stance-swing transitions as long as the leg was not close to its anterior extreme position during stepping. Our results provide evidence that, in the active behavioral state of the stick insect, vibration signals can access the rhythm generating or bistable networks of the three main leg joints and can promote phase transitions in motor activity in both directions. The results substantiate earlier findings on the modular structure of the single-leg walking pattern generator and indicate a new mechanism of how sensory influence can contribute to the synchronization of phase transitions in adjacent leg joints independent of the walking direction.     

Activation of human arm muscles during flexion/extension and supination/pronation tasks: A theory on muscle coordination

Generally the number of muscles acting across a joint exceeds the number of degrees of freedom available to the joint. This redundancy raises a problem regarding the ratio in which these muscles are activated during a particular motor task. In this paper we present a theory to explain the activation patterns of muscles used during voluntary and reflex induced contractions. The basic assumptions underlying the theory are that 1) coordination of muscles is based on synergistic muscle activities, 2) the synergisms involved satisfy certain transformations of muscle spindle signals to muscle activation signals and 3) muscle spindle output is proportional to the ratio of muscle stretch and muscle length in lengthening muscles, and is zero in shortening muscles. The theory is used to predict the recruitment threshold of motor units in six arm muscles during voluntary isometric contractions. All theoretical predictions are in reasonable agreement with the experimentally observed behavior of a large population of motor units within each muscle. However, within a single muscle sometimes motor-unit populations have been found to have different types of recruitment behavior. This deviating behavior is discussed in the light of the theory presented here.     

Propagation of the hip joint centre location error to the estimate of femur vs pelvis orientation using a constrained or an unconstrained approach

To estimate hip joint angles during selected motor tasks using stereophotogrammetric data, it is necessary to determine the hip joint centre position. The question is whether the errors affecting that determination propagate less to the angles estimates when a three degrees of freedom (DOFs) constraint (spherical hinge) is used between femur and pelvis, rather than when the two bones are assumed to be unconstrained (six DOFs). An analytical relationship between the hip joint centre location error and the joint angle error was obtained limited to the planar case. In the 3-D case, a similar relationship was obtained using a simulation approach based on experimental data. The joint angle patterns resulted in a larger distortion using a constrained approach, especially when wider rotations occur. The range of motion of the hip flexion-extension, obtained simulating different location errors and without taking into account soft tissue artefacts, varied approximately 7 deg using a constrained approach and up to 1 deg when calculated with an unconstrained approach. Thus, the unconstrained approach should be preferred even though its estimated three linear DOFs most unlikely carry meaningful information.     

Statistical power when testing for genetic differentiation

A variety of statistical procedures are commonly employed when testing for genetic differentiation. In a typical situation two or more samples of individuals have been genotyped at several gene loci by molecular or biochemical means, and in a first step a statistical test for allele frequency homogeneity is performed at each locus separately, using, e.g. the contingency chi-square test, Fisher's exact test, or some modification thereof. In a second step the results from the separate tests are combined for evaluation of the joint null hypothesis that there is no allele frequency difference at any locus, corresponding to the important case where the samples would be regarded as drawn from the same statistical and, hence, biological population. Presently, there are two conceptually different strategies in use for testing the joint null hypothesis of no difference at any locus. One approach is based on the summation of chi-square statistics over loci. Another method is employed by investigators applying the Bonferroni technique (adjusting the P-value required for rejection to account for the elevated alpha errors when performing multiple tests simultaneously) to test if the heterogeneity observed at any particular locus can be regarded significant when considered separately. Under this approach the joint null hypothesis is rejected if one or more of the component single locus tests is considered significant under the Bonferroni criterion. We used computer simulations to evaluate the statistical power and realized alpha errors of these strategies when evaluating the joint hypothesis after scoring multiple loci. We find that the 'extended' Bonferroni approach generally is associated with low statistical power and should not be applied in the current setting. Further, and contrary to what might be expected, we find that 'exact' tests typically behave poorly when combined in existing procedures for joint hypothesis testing. Thus, while exact tests are generally to be preferred over approximate ones when testing each particular locus, approximate tests such as the traditional chi-square seem preferable when addressing the joint hypothesis.     

Fatigue compensation during FES using surface EMG

Muscle fatigue limits the effectiveness of FES when applied to regain functional movements in spinal cord injured (SCI) individuals. The stimulation intensity must be manually increased to provide more force output to compensate for the decreasing muscle force due to fatigue. An artificial neural network (ANN) system was designed to compensate for muscle fatigue during functional electrical stimulation (FES) by maintaining a constant joint angle. Surface electromyography signals (EMG) from electrically stimulated muscles were used to determine when to increase the stimulation intensity when the muscle’s output started to drop.

In two separate experiments on able-bodied subjects seated in hard back chairs, electrical stimulation was continuously applied to fatigue either the biceps (during elbow flexion) or the quadriceps muscle (during leg extension) while recording the surface EMG. An ANN system was created using processed surface EMG as the input, and a discrete fatigue compensation control signal, indicating when to increase the stimulation current, as the output. In order to provide training examples and test the systems’ performance, the stimulation current amplitude was manually increased to maintain constant joint angles. Manual stimulation amplitude increases were required upon observing a significant decrease in the joint angle. The goal of the ANN system was to generate fatigue compensation control signals in an attempt to maintain a constant joint angle.

On average, the systems could correctly predict 78.5% of the instances at which a stimulation increase was required to maintain the joint angle. The performance of these ANN systems demonstrates the feasibility of using surface EMG feedback in an FES control system.