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To cope with the high intra-subject variability of muscle activation intervals, a large amount of gait cycles is necessary to clearly document physiological or pathological muscle activity patterns during human locomotion. The Clustering for Identification of Muscle Activation Pattern (CIMAP) algorithm has been proposed to help clinicians obtaining a synthetic and clear description of normal and pathological muscle functions in human walking. Moreover, this algorithm allows the extraction of Principal Activations (PAs), defined as those muscle activations that are strictly necessary to perform a specific task and occur in every gait cycle. This contribution aims at assessing the impact of the number of gait cycles on the extraction of the PAs. Results demonstrated no statistically significant differences between PAs extracted considering different numbers of gait cycles, revealing, on average, similarity values higher than 0.88.Clinical Relevance-This contribution demonstrates the potential applicability of the CIMAP algorithm to the analysis of subjects affected by neurological disorders, for whom the assessment of motor functions may be of the uttermost importance and only a reduced number of gait cycles can be acquired.

The present study proposes a new personalized sleep spindle detection algorithm, suggesting the importance of an individualized approach. We identify an optimal set of features that characterize the spindle and exploit a support vector machine to distinguish between spindle and nonspindle patterns. The algorithm is assessed on the open source DREAMS database, that contains only selected part of the polysomnography, and on whole night polysomnography recordings from the SPASH database. We show that on the former database the personalization can boost sensitivity, from 84.2% to 89.8%, with a slight increase in specificity, from 97.6% to 98.1%. On a whole night polysomnography instead, the algorithm reaches a sensitivity of 98.6% and a specificity of 98.1%, thanks to the personalization approach. Future work will address the integration of the spindle detection algorithm within a sleep scoring automated procedure.

The muscle synergy theory was widely used in literature to assess the modular organization of the central nervous system (CNS) during human locomotion. The extraction of muscle synergies may be strongly influenced by the preprocessing techniques applied to surface electromyographic (sEMG) signals. The aim of this contribution is to assess the robustness improvement in muscle synergy extraction obtained using an innovative pre-processing technique with respect to the standard procedure. The new pre-processing technique that we propose is based on the extraction of principal muscle activation intervals (necessary to accomplish a specific biomechanical task during gait) from the original sEMG signals, discarding the secondary muscle activation intervals (activations that occur only in some strides with auxiliary functions). Results suggest that the extraction of the principal activation intervals from sEMG provide a more consistent and stable description of the modular organization of the CNS with respect to the standard pre-processing procedure.

During cyclic movements, the number of muscle activations and their timing are different from cycle to cycle. In a previous study, the CIMAP algorithm was proposed for grouping cycles showing similar EMG activation intervals, using dendrogram clustering. Even if the algorithm demonstrated good performances on a healthy population, the intra-cluster variability decreased when applied to datasets from pathological subjects. In this work we propose an optimized version of the CIMAP, comparing the performances of 8 different combinations of parameters used for the dendrogram construction. The cut-off point is also modified. The new and the original version of the algorithm are compared, in terms of intra-cluster variability, considering a population of 60 subjects, both healthy and pathological. The results show that the new CIMAP allows for obtaining clusters with lower variability with respect to the original version of the algorithm (p <; 0.001).

Joint position error (JPE) is frequently used to assess proprioception in rehabilitation and sport science. During position-reposition tests the subject is asked to replicate a specific target angle (e.g. 30° of knee flexion) for a specific number of times. The aim of this study is to find an effective method to estimate JPE from the joint kinematic signal. Forty healthy subjects were tested to assess knee joint position sense. Three different methods of JPE estimation are described and compared using a hierarchical clustering approach. Overall, the 3 methods showed a high degree of similarity, ranging from 88% to 100%. We concluded that it is preferable to use the more user-independent method, in which the operator does not have to manually place "critical" markers.

The extraction of muscle synergies in human locomotion may be biased by the kind of pre-processing applied to electromyographic (EMG) data. The aim of this contribution is to analyze the differences in the muscle synergies extracted using a standard pre-processing procedure and a new procedure. The new procedure is based on the selection of the muscle's principal activations (necessary actuations of the muscle to accomplish its specific biomechanical task during gait), discarding secondary activations (with an auxiliary function in motor control). EMG signals were recorded from 12 muscles of a healthy volunteer who was asked to walk, at self-selected pace, for 5 minutes. A dataset of 193 gait cycles was collected and divided into 19 epochs of 10 concatenated gait cycles. The application of the new pre-processing procedure provided 5 instead of 6 muscle synergies accurately reconstructing the original EMG data matrix, and clearer and more stable neural activation commands. The new preprocessing procedure may be easily extended to the extraction of muscle synergies in other cyclic movements, such as running, stair climbing, cyclo-ergometer exercising, and swimming.

Gait alterations are observed even years after hip replacement surgery. Such long-lasting alterations may arise from a global change of the motor control strategies. The aim of this work is to investigate the changes in gait motor control strategies of patients after hip replacement surgery by analyzing the frequency of occurrence (OF) of myoelectric activation patterns. We studied five lower limb muscles during gait, in hip prosthesis patients and controls. We found that patients adopt a motor control strategy that tends to prefer "simplified" myoelectric patterns, showing a smaller number of activations within the gait cycle. This altered motor control was present both on the prosthesis and the sound side, and did not improve during the 12-month follow-up. The reduced number of activations is even more evident in lateral hamstrings and gluteus medius, which are the muscles more affected by hip replacement surgery. Furthermore, this study demonstrated that the OF is a sensitive parameter able to discover subtle changes in motor control strategies. Its use can be extended to other studies involving motor coordination, motor learning and motor control adaptations.

The purpose of the present study was the quantification of the co-activation patterns of the knee extensor and flexor muscles during walking at self-selected speed and cadence. To this aim, the Statistical Gait Analysis, a recent methodology providing a statistical characterization of gait, was performed on surface EMG signals from Vastus Lateralis (VL) and Medial Hamstrings (MH) in 14 healthy young adult subjects. Muscular co-contraction was assessed as the overlapping period between activation intervals of agonist and antagonist muscles. Superimpositions between VL and MH activity were detected from terminal swing to the following loading response in 100% of the considered strides. This superimposition could be intended as an actual co-contraction of VL and MH, working across the same joint, the knee. It occurs in this gait phase likely in order to assist knee extension, developing muscle tension for weight acceptance during loading response, and to control knee flexion. A further less frequent (28.9±13.6% of the strides, P<;0.001) superimposition was detected in terminal stance; this superimposition, however, should not be considered a real co-contraction, because VL and MH work on different joints. These findings have the merit to provide a novel data on the variability of the reciprocal role of VL and MH during walking, allowing a deeper insight in the physiological mechanisms that regulate the knee flexion/extension.

Many studies have recently addressed the quantification of the natural variability of myoelectric activity during walking, considering hundreds of strides. The availability of so many strides allows assessing a parameter seldom considered in classic surface EMG (sEMG) studies: the occurrence frequency, defined as the frequency each muscle activation occurs with, quantified by the number of strides in which a muscle is recruited with that specific activation modality. Aim of this study is to point out the occurrence frequency as a suitable parameter for the evaluation of the variability of the myoelectric activity during walking. This goal was pursued by means of the statistical gait analysis of sEMG signal acquired from Gastrocnemius Lateralis (GL) in six healthy subjects, with different characteristics. Results show that among these six subjects relevant differences were not detected in the temporal parameters, i.e., activation onset/offset instant and activation duration. In the same subjects, the values of the occurrence frequency ranged from 3% to 74% in the different activation modalities, indicating a large variability of this parameter. These findings show that occurrence frequency is able to provide further and different information with respect to classical temporal parameters. Thus, the occurrence frequency is proposed as a suitable parameter to support the classic temporal parameters in the evaluation of variability of myoelectric activity during walking.

In clinical gait analysis is fundamental to have access to normative data, to be used as a reference in the interpretation of pathological walking. In a paediatric population this may be complicated by the dependence of gait parameters on child growth. The aim of this work is to provide the correlations of spatial-temporal gait parameters with children's height. We obtained the regression lines of cadence, double support, and gait phases, with respect to height, from a sample of 85 normally typically developing children aged 6 to 11. Our analysis of gait phases was not limited to the traditional analysis of stance and swing, but rather focused on the sub-phases of stance - heel contact, flat foot contact, push off - which proved to be an innovative approach to gait analysis. Heel contact decreased, flat foot contact increased and push off remained essentially unchanged with respect to children's height. These results may be useful in the interpretation of gait data in developing children, and the regression lines obtained may be used to normalize their gait parameters.

Time-frequency plots are widely applied to the non-stationary analysis of signals. These plots may be difficult to interpret, particularly when large data sets have to be considered. The aim of this work is to propose an automatic procedure of feature selection and clustering to be applied to time-frequency plots. We focus on the application of this procedure to plots obtained from a non-stationary analysis of the center-of-pressure signals acquired in upright bipedal stance. From a data set of 168 time-frequency plots we obtained 5 different clusters, each characterized by a few distinctive features. We were able to interpret the results of the clustering relating them to the physiological mechanisms underlying postural sway.

Static stabilometry is a technique aimed at quantifying postural sway during quiet standing in the upright position. Many different models and many different techniques to analyze the trajectories of the Centre of Pressure (CoP) have been proposed. Most of the parameters calculated according to these different approaches are affected by a relevant intra- and inter-subject variability or do not have a clear physiological interpretation. In this study we hypothesize that CoP trajectories have rotational characteristics, therefore we decompose them in clockwise and counter-clockwise components, using the rotary spectra analysis. Rotary spectra obtained studying a population of healthy subjects are described through the group average of spectral parameters, i.e., 95% spectral bandwidth, mean frequency, median frequency, and skewness. Results are reported for the clockwise and the counter-clockwise components and refer to the upright position maintained with eyes open or closed. This study demonstrates that the approach is feasible and that some of the spectral parameters are statistically different between the open and closed eyes conditions. More research is needed to demonstrate the clinical applicability of this approach, but results so far obtained are promising.

Dynamic infrared imaging is a promising technique to be applied to early breast cancer diagnosis. It is based on the acquisition of hundreds of consecutive thermal images with a frame rate ranging from 50 to 200 frames/s, followed by the spectral analysis of temperature time series at each image pixel. To improve the time series signal-to-noise ratio, it is useful to realign the thermal images of the acquisition sequence. Our previous studies demonstrated that a registration algorithm based on fiducial points is suitable to both clinical applications and research, when associated with a proper set of skin markers. In this paper, we evaluate the performance of different marker sets by means of a model that allows estimating the signal-to-noise ratio increment due to registration, and we conclude that a 9-marker set is a good compromise between motion artifact reduction and the time required to prepare the patient.