Accuracy of EMG linear envelope in identifying the peak of muscular activity during walking.

BACKGROUND: The linear-envelope peak (LEP) of surface EMG signal is widely used in gait analysis to characterize muscular activity, especially in clinics. RESEARCH QUESTION: This study is designed to evaluate LEP accuracy in identifying muscular activation and assessing activation timing during walking. METHODS: Surface EMG signals from gastrocnemius lateralis (GL) and tibialis anterior (TA) were analyzed in 100 strides per subject (31 healthy subjects) during ground walking. Signals were full-wave rectified and low-pass filtered (cut-off frequency=5 Hz) to extract the linear envelope. LEP accuracy in identifying muscle activations and the associated error in peak detection were assessed by direct comparison with a reference method based on wavelet transform. LEP accuracy in identifying the timing of higher signalenergy levels was also assessed, increasing the reference-algorithm selectivity. RESULTS: The detection error (percentage number of times when LEP falls outside the correspondent reference activation interval) is close to zero. Detection error increases up to 70% for intervals including only signal energy higher than 90% of energy peak. Mean absolute error (MAE, the absolute value of the distance between LEP timing and the correspondent actual timing of the sEMG-signal peak computed by reference algorithm) is 54.1±20.0 ms. Detection error and MAE are significantly higher (p<0.05) in TA data compared to GL signals. Differences among MAE values detected adopting different values for LE cut-off frequency are not statistically significant. SIGNIFICANCE: LEP was found to be accurate in identifying the number of muscle activations during walking. However, the use of LEP to assess the timing of highest sEMG-signal energy (signal peak) should be considered carefully. Indeed, it could introduce a relevant inaccuracy in muscle-activation identification and peak-timing quantification. The type of muscle to analyze could also influence LEP performances, while the cut-off frequency chosen for envelope extraction appears to have a limited impact.

A Minimal and Multi-Source Recording Setup for Ankle Joint Kinematics Estimation During Walking Using Only Proximal Information From Lower Limb.

In this study, a minimal setup for the ankle joint kinematics estimation is proposed relying only on proximal information of the lower-limb, i.e. thigh muscles activity and joint kinematics. To this purpose, myoelectric activity of Rectus Femoris (RF), Biceps Femoris (BF), and Vastus Medialis (VM) were recorded by surface electromyography (sEMG) from six healthy subjects during unconstrained walking task. For each subject, the angular kinematics of hip and ankle joints were synchronously recorded with sEMG signal for a total of 288 gait cycles. Two feature sets were extracted from sEMG signals, i.e. time domain (TD) and wavelet (WT) and compared to have a compromise between the reliability and computational capacity, they were used for feeding three regression models, i.e. Artificial Neural Networks, Random Forest, and Least Squares - Support Vector Machine (LS-SVM). BF together with LS-SVM provided the best ankle angle estimation in both TD and WT domains (RMSE < 5.6 deg). The inclusion of Hip joint trajectory significantly enhanced the regression performances of the model (RMSE < 4.5 deg). Results showed the feasibility of estimating the ankle trajectory using only proximal and limited information from the lower limb which would maximize a potential transfemoral amputee user's comfortability while facing the challenge of having a small amount of information thus requiring robust data-driven models. These findings represent a significant step towards the development of a minimal setup useful for the control design of ankle active prosthetics and rehabilitative solutions.

Multiscale Fuzzy Entropy Analysis of Balance: Evidences of Scale-Dependent Dynamics on Diabetic Patients With and Without Neuropathy.

Postural control is usually assessed by examining the fluctuations of the center of pressure (COP). Balance maintenance is based on sensory feedback and neural interactions, deployed over multiple temporal scales and producing less complex outputs with aging and disease. This paper aims to investigate postural dynamics and complexity on diabetic patients, since diabetic neuropathy (DN) affects the somatosensory system and impairs postural steadiness. A multiscale fuzzy entropy (MSFEn) analysis, over a wide range of temporal scales, was performed on COP timeseries during unperturbed stance in a group of diabetic individuals without neuropathy and two groups of DN patients, with and without symptoms. A parameterization of the MSFEn curve is also proposed. A significant loss of complexity was recognized for the medial-lateral direction in DN groups with respect to non-neuropathic population. For the anterior-posterior direction, symptomatic DN group showed a lowered sway complexity for longer time scales with respect to non neuropathic and asymptomatic patients. The MSFEn approach and the related parameters highlighted that the loss of complexity might be attributed to different factors depending on sway direction, i.e. related to the presence of neuropathy along the medial-lateral axis and to a symptomatic state on the anterior-posterior direction. Results of this study support the use of the MSFEn for gaining insights into balance control mechanisms for diabetic patients, in particular when comparing non neuropathic with neuropathic asymptomatic patients, whose identification by posturographic analysis would be of great value.

Identification of Neurodegenerative Diseases From Gait Rhythm Through Time Domain and Time-Dependent Spectral Descriptors.

The analysis of gait rhythm by pattern recognition can support the state-of-the-art clinical methods for the identification of neurodegenerative diseases (NDD). In this study, we investigated the use of time domain (TD) and time-dependent spectral features (PSDTD) for detecting NDD sub-types. Also, we proposed two classification pathways for supporting NDD diagnosis, the first one made by a two-step learning phase, whereas the second one encompasses a single learning model. We considered stride-to-stride fluctuation data of healthy controls (CN), patients affected by Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (AS). TD feature set provided good results to distinguish between CN and NDDs, while performances lowered for specific NDD identification. PSDTD features boosted the accuracy of each binary identification task. With k-nearest neighbor classifier, the first diagnosis pathway reached 98.76% accuracy to distinguish between CN and NDD and 94.56% accuracy for NDDs sub-types, whereas the second pathway offered an overall accuracy of 94.84% for a 4-class classification task. Outcomes of this study indicate that the use of TD and PSDTD features, simple to extract and with a low computational load, provides reliable results in terms of NDD identification, being also useful for the development of gait rhythm computer-aided NDD detection systems.

Muscle Co-Contraction Detection in the Time-Frequency Domain.

BACKGROUND: Muscle co-contraction plays a significant role in motion control. Available detection methods typically only provide information in the time domain. The current investigation proposed a novel approach for muscle co-contraction detection in the time-frequency domain, based on continuous wavelet transform (CWT). METHODS: In the current study, the CWT-based cross-energy localization of two surface electromyographic (sEMG) signals in the time-frequency domain, i.e., the CWT coscalogram, was adopted for the first time to characterize muscular co-contraction activity. A CWT-based denoising procedure was applied for removing noise from the sEMG signals. Algorithm performances were checked on synthetic and real sEMG signals, stratified for signal-to-noise ratio (SNR), and then validated against an approach based on the acknowledged double-threshold statistical algorithm (DT). RESULTS: The CWT approach provided an accurate prediction of co-contraction timing in simulated and real datasets, minimally affected by SNR variability. The novel contribution consisted of providing the frequency values of each muscle co-contraction detected in the time domain, allowing us to reveal a wide variability in the frequency content between subjects and within stride. CONCLUSIONS: The CWT approach represents a relevant improvement over state-of-the-art approaches that provide only a numerical co-contraction index or, at best, dynamic information in the time domain. The robustness of the methodology and the physiological reliability of the experimental results support the suitability of this approach for clinical applications.

Machine Learning for Detection of Muscular Activity from Surface EMG Signals.

BACKGROUND: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-based approach (DEMANN) in detecting the onset/offset timing of muscle activation from sEMG signals. METHODS: A dataset of 2880 simulated sEMG signals, stratified for signal-to-noise ratio (SNR) and time support, was generated to train a hidden single-layer fully-connected neural network. DEMANN's performance was evaluated on simulated sEMG signals and two different datasets of real sEMG signals. DEMANN was validated against different reference algorithms, including the acknowledged double-threshold statistical algorithm (DT). RESULTS: DEMANN provided a reliable prediction of muscle onset/offset in simulated and real sEMG signals, being minimally affected by SNR variability. When directly compared with state-of-the-art algorithms, DEMANN introduced relevant improvements in prediction performances. CONCLUSIONS: These outcomes support DEMANN's reliability in assessing onset/offset events in different motor tasks and the condition of signal quality (different SNR), improving reference-algorithm performances. Unlike other works, DEMANN's adopts a machine learning approach where a neural network is trained by only simulated sEMG signals, avoiding the possible complications and costs associated with a typical experimental procedure, making this approach suitable to clinical practice.

Center of pressure plausibility for the double-link human stance model under the intermittent control paradigm.

Despite human balance maintenance in quiet conditions could seem a trivial motor task, it is not. Recently, the human stance was described through a double link inverted pendulum (DIP) actively controlled at the ankle with an intermittent proportional (P) and derivative (D) control actions based on the sway of a virtual inverted pendulum (VIP) that links the ankle joint with the DIP center of mass. Such description, encompassing both the mechanical model and the intermittent control policy, was referred as the DIP/VIP human stance model, and it showed physiologically plausible kinematic patterns. In this study a mathematical formalization of the Center of pressure (COP) for a DIP structure was developed. Then, it was used in conjunction with an intermittently controlled DIP/VIP model to assess its kinetic plausibility. Three descriptors commonly employed in posturography were selected among six based on their capability to discriminate between young (Y) and elderly (O) adults groups. Then, they were applied to assess whether variations of the P-D parameters affect the synthetic COP. The results showed that DIP/VIP model can reproduce COP trajectories, showing characteristics similar to the Y and O groups. Moreover, it was observed that both P and D parameters increased passing from Y to O, indicating that the COP obtained from the DIP/VIP model is able to highlight differences in balance control between groups. The study hence promote the use of DIP/VIP in posturography, where inferential techniques can be applied to characterize neural control.

Machine-Learning-Based Prediction of Gait Events From EMG in Cerebral Palsy Children.

Machine-learning techniques are suitably employed for gait-event prediction from only surface electromyographic (sEMG) signals in control subjects during walking. Nevertheless, a reference approach is not available in cerebral-palsy hemiplegic children, likely due to the large variability of foot-floor contacts. This study is designed to investigate a machine-learning-based approach, specifically developed to binary classify gait events and to predict heel-strike (HS) and toe-off (TO) timing from sEMG signals in hemiplegic-child walking. To this objective, sEMG signals are acquired from five hemiplegic-leg muscles in nearly 2500 strides from 20 hemiplegic children, acknowledged as Winters' group 1 and 2. sEMG signals, segmented in overlapping windows of 600 samples (pace = 5 samples), are used to train a multi-layer perceptron model. Intra-subject and inter-subject experimental settings are tested. The best-performing intra-subject approach is able to provide in the hemiplegic population a mean classification accuracy (±SD) of 0.97±0.01 and a suitable prediction of HS and TO events, in terms of average mean absolute error (MAE, 14.8±3.2 ms for HS and 17.6±4.2 ms for TO) and F1-score (0.95±0.03 for HS and 0.92±0.07 for TO). These results outperform previous sEMG-based attempts in cerebral-palsy populations and are comparable with outcomes achieved by reference approaches in control populations. In conclusion, the findings of the study prove the feasibility of neural networks in predicting the two main gait events using surface EMG signals, also in condition of high variability of the signal to predict as in hemiplegic cerebral palsy.

Neuromuscular Control Modelling of Human Perturbed Posture Through Piecewise Affine Autoregressive With Exogenous Input Models.

In this study, the neuromuscular control modeling of the perturbed human upright stance is assessed through piecewise affine autoregressive with exogenous input (PWARX) models. Ten healthy subjects underwent an experimental protocol where visual deprivation and cognitive load are applied to evaluate whether PWARX can be used for modeling the role of the central nervous system (CNS) in balance maintenance in different conditions. Balance maintenance is modeled as a single-link inverted pendulum; and kinematic, dynamic, and electromyography (EMG) data are used to fit the PWARX models of the CNS activity. Models are trained on 70% and tested on the 30% of unseen data belonging to the remaining dataset. The models are able to capture which factors the CNS is subjected to, showing a fitting accuracy higher than 90% for each experimental condition. The models present a switch between two different control dynamics, coherent with the physiological response to a sudden balance perturbation and mirrored by the data-driven lag selection for data time series. The outcomes of this study indicate that hybrid postural control policies, yet investigated for unperturbed stance, could be an appropriate motor control paradigm when balance maintenance undergoes external disruption.

Functional evaluation of triceps surae during heel rise test: from EMG frequency analysis to machine learning approach.

Soleus muscle flap as coverage tissue is a possible surgical solution adopted to cover the wounds due to open fractures. Despite this procedure presents many clinical advantages, relatively poor information is available about the loss of functionality of triceps surae of the treated leg. In this study, a group of patients who underwent a soleus muscle flap surgical procedure has been analyzed through the heel rise test (HRT), in order to explore the triceps surae residual functionalities. A frequency band analysis was performed in order to assess whether the residual heads of triceps surae exhibit different characteristics with respect to both the non-treated lower limb and an age-matched control group. Then, an in-depth analysis based on a machine learning approach was proposed for discriminating between groups by generalizing across new unseen subjects. Experimental results showed the reliability of the proposed analyses for discriminating between-group at a specific time epoch and the high interpretability of the proposed machine learning algorithm allowed the temporal localization of the most discriminative frequency bands. Findings of this study highlighted that significant differences can be recognized in the myoelectric spectral characteristics between the treated and contralateral leg in patients who underwent soleus flap surgery. These experimental results may support the clinical decision-making for assessing triceps surae performance and for supporting the choice of treatment in plastic and reconstructive surgery. Graphical Abstract The Graphical abstract presents the scope of the proposed analysis of myoelectric signals of soleus and gastrocnemius muscles of patiens groups during Hell Rise Test, highlighting the applied methods and the obtained results.

Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals.

BACKGROUND: Machine learning models were satisfactorily implemented for estimating gait events from surface electromyographic (sEMG) signals during walking. Most of them are based on inter-subject approaches for data preparation. Aim of the study is to propose an intra-subject approach for binary classifying gait phases and predicting gait events based on neural network interpretation of sEMG signals and to test the hypothesis that the intra-subject approach is able to achieve better performances compared to an inter-subject one. To this aim, sEMG signals were acquired from 10 leg muscles in about 10.000 strides from 23 healthy adults, during ground walking, and a multi-layer perceptron (MLP) architecture was implemented. RESULTS: Classification/prediction accuracy was tested vs. the ground truth, represented by the foot-floor-contact signal provided by three foot-switches, through samples not used during training phase. Average classification accuracy of 96.1 ± 1.9% and mean absolute value (MAE) of 14.4 ± 4.7 ms and 23.7 ± 11.3 ms in predicting heel-strike (HS) and toe-off (TO) timing were provided. Performances of the proposed approach were tested by a direct comparison with performances provided by the inter-subject approach in the same population. Comparison results showed 1.4% improvement of mean classification accuracy and a significant (p < 0.05) decrease of MAE in predicting HS and TO timing (23% and 33% reduction, respectively). CONCLUSIONS: The study developed an accurate methodology for classification and prediction of gait events, based on neural network interpretation of intra-subject sEMG data, able to outperform more typical inter-subject approaches. The clinically useful contribution consists in predicting gait events from only EMG signals from a single subject, contributing to remove the need of further sensors for the direct measurement of temporal data.

Kinetic data simultaneously acquired from dynamometric force plate and Nintendo Wii Balance Board during human static posture trials.

Data provided with this article are relative to kinetic measures from standing posture trials in eye open and eye closed conditions of 15 healthy subjects, acquired from a dynamometric force plate and a Nintendo Wii Balance Board (NBB). Data have been originally collected for a research project aimed at evaluating the reliability of low-cost devices in clinical scenarios. Raw data from the force plate include three ground reaction force components, center of pressure trajectories and torque around the vertical axis. Raw data from the NBB consist of vertical component of the ground reaction force measured by each of the four device sensors. Processed data consist of synchronized center of pressure time-series from both devices, referred to the force plate reference frame. Data were acquired simultaneously from the devices, allowing a direct comparison between the kinetic measures provided by the gold-standard for posture analysis (dynamometric force plate) and a low-cost device (NBB). Utility of present data can be twofold: first they can be used to assess the overall quality of the NBB signals for posturographic analysis by a direct comparison with the same signals acquired from the gold-standard device for kinetic measurement. Secondly, data from the dynamometric force plate can be used per se to evaluate different kind of parameters useful to assess balance capabilities, also by comparing data from different sensorial conditions (eye open versus eye closed).

Wavelet filtering of fetal phonocardiography: A comparative analysis.

Fetal heart rate (FHR) monitoring can serve as a benchmark to identify high-risk fetuses. Fetal phonocardiogram (FPCG) is the recording of the fetal heart sounds (FHS) by means of a small acoustic sensor placed on maternal abdomen. Being heavily contaminated by noise, FPCG processing implies mandatory filtering to make FPCG clinically usable. Aim of the present study was to perform a comparative analysis of filters based on Wavelet transform (WT) characterized by different combinations of mothers Wavelet and thresholding settings. By combining three mothers Wavelet (4th-order Coiflet, 4th-order Daubechies and 8th-order Symlet), two thresholding rules (Soft and Hard) and three thresholding algorithms (Universal, Rigorous and Minimax), 18 different WT-based filters were obtained and applied to 37 simulated and 119 experimental FPCG data (PhysioNet/PhysioBank). Filters performance was evaluated in terms of reliability in FHR estimation from filtered FPCG and noise reduction quantified by the signal-to-noise ratio (SNR). The filter obtained by combining the 4th-order Coiflet mother Wavelet with the Soft thresholding rule and the Universal thresholding algorithm was found to be optimal in both simulated and experimental FPCG data, since able to maintain FHR with respect to reference (138.7[137.7; 140.8] bpm vs. 140.2[139.7; 140.7] bpm, P > 0.05, in simulated FPCG data; 139.6[113.4; 144.2] bpm vs. 140.5[135.2; 146.3] bpm, P > 0.05, in experimental FPCG data) while strongly incrementing SNR (25.9[20.4; 31.3] dB vs. 0.7[-0.2; 2.9] dB, P < 10-14 , in simulated FPCG data; 22.9[20.1; 25.7] dB vs. 15.6[13.8; 16.7] dB, P < 10-37, in experimental FPCG data). In conclusion, the WT-based filter obtained combining the 4th-order Coiflet mother Wavelet with the thresholding settings constituted by the Soft rule and the Universal algorithm provides the optimal WT-based filter for FPCG filtering according to evaluation criteria based on both noise and clinical features.

EMG-Based Characterization of Walking Asymmetry in Children with Mild Hemiplegic Cerebral Palsy.

Hemiplegia is a neurological disorder that is often detected in children with cerebral palsy. Although many studies have investigated muscular activity in hemiplegic legs, few EMG-based findings focused on unaffected limb. This study aimed to quantify the asymmetric behavior of lower-limb-muscle recruitment during walking in mild-hemiplegic children from surface-EMG and foot-floor contact features. sEMG signals from tibialis anterior (TA) and gastrocnemius lateralis and foot-floor contact data during walking were analyzed in 16 hemiplegic children classified as W1 according to Winter' scale, and in 100 control children. Statistical gait analysis, a methodology achieving a statistical characterization of gait by averaging surface-EMG-based features, was performed. Results, achieved in hundreds of strides for each child, indicated that in the hemiplegic side with respect to the non-hemiplegic side, W1 children showed a statistically significant: decreased number of strides with normal foot-floor contact; decreased stance-phase length and initial-contact sub-phase; curtailed, less frequent TA activity in terminal swing and a lack of TA activity at heel-strike. The acknowledged impairment of anti-phase eccentric control of dorsiflexors was confirmed in the hemiplegic side, but not in the contralateral side. However, a modified foot-floor contact pattern is evinced also in the contralateral side, probably to make up for balance requirements.

Glucose effectiveness and its components in relation to body mass index.

BACKGROUND: Obesity is known to induce a deterioration of insulin sensitivity (SI ), one of the insulin-dependent components of glucose tolerance. However, few studies investigated whether obesity affects also the insulin-independent component, that is glucose effectiveness (SG ). This cross-sectional study aimed to analyse SG and its components in different body mass index (BMI) categories. MATERIALS AND METHODS: Three groups of subjects spanning different BMI (kg m-2 ) categories underwent a 3-h frequently sampled intravenous glucose tolerance test: Lean (LE; 18.5 ≤ BMI < 25, n = 73), Overweight (OW; 25 ≤ BMI < 30, n = 90), and Obese (OB; BMI ≥ 30, n = 41). OB has been further divided into two subgroups, namely Obese I (OB-I; 30 ≤ BMI < 35, n = 27) and Morbidly Obese (OB-M; BMI ≥ 35, n = 14). Minimal model analysis provided SG and its components at zero (GEZI) and at basal (BIE) insulin. RESULTS: Values for SG were 1.98 ± 1.30 × 10-2 ·min-1 in all subjects grouped and 2.38 ± 1.23, 1.84 ± 0.82, 1.59 ± 0.61 10-2 ·min-1 in LE, OW and OB, respectively. In all subjects grouped, a significant inverse linear correlation was found between the log-transformed values of SG and BMI (r = -0.3, P < 0.0001). SG was significantly reduced in OW and OB with respect to LE (P < 0.001) but no significant difference was detected between OB and OW (P = 0.35) and between OB-I and OB-M (P = 0.25). Similar results were found for GEZI. BIE was not significantly different among NW, OW and OB (P = 0.11) and between OB-I and OB-M (P ≥ 0.07). CONCLUSIONS: SG and its major component GEZI deteriorate in overweight individuals compared to those in the normal BMI range, without further deterioration when BMI increases above 30 kg m-2 .

Fractal Analysis of Motor Control in Knee Arthroplasty Patients.

Knee osteoarthritis is commonly treated through total knee arthroplasty (TKA) or unicompartmental knee arthroplasty (UKA) and therefore the assessment of postoperative differences in functional capabilities between TKA and UKA patients appears of primary importance. Throughout the years, fractal analysis has been applied to several biological time-series, revealing to be particularly useful for assessing human balance and motor control by quantifying complexity and repeatability of dynamic measures. In this study, fractal dimension (FD) has been computed on ground reaction force and momentum acquired during squatting movement in two groups of TKA and UKA patients and a control group of healthy subjects (CTRL). FD resulted able to discriminate between TKA and both CTRL and UKA group, showing significant differences in all the considered measures. Outcomes of this study could help to gain further information about functional recovery after different knee arthroplasty procedures, in order to improve the choice of rehabilitative treatment.

Complexity Measures of Postural Control in Type-2 Diabetic Subjects.

Balance maintenance is commonly analyzed by evaluating the center of pressure (COP) displacement, which presents an acknowledged non-stationary behavior. The latter led to an evaluation of COP regularity through complexity measures such as the approximate (AppEn) and sample entropy (SampEn). These indexes quantify the regularity of time-series in terms of inner pattern recurrence; however, they are highly dependent on the input parameters used for their computation. Thus, this study aimed to evaluate the use of the AppEn, SampEn and a recently proposed entropy measure, the fuzzy entropy (FuzzyEn) for the analysis of COP time-series in type-2 diabetic subjects with and without neuropathy during quiet standing trials in eyes open condition. Results highlighted consistency of entropy measures for different values of input parameters, showing significant differences between the two populations in terms of COP regularity for both anterior-posterior and medial-lateral directions. Findings of this study outline low complexity in postural control of neuropathic subjects, also in the medial-lateral direction, which could indicate a limited capacity of producing adaptable responses, relying on fixed balance control patterns. Further, they support the use of complexity measures for the analysis of patients with diabetic neurological impairment.

UKF Magnetometer-Free Sensor Fusion for Pelvis Pose Estimation During Treadmill Walking.

Inertial measurement units are an efficient tool to estimate the orientation of a rigid body with respect to a global or navigation frame. Thanks to their relatively small scale, these devices are often employed in clinical environments in form of wearable devices. A direct consequence of this large use of inertial sensors has been the development of many sensor fusion techniques for pose estimation in many practical applications. In this paper we study the feasibility of a nonlinear "Unscented" variant of the well-known Kalman Filter for gyroscope/accelerometer sensor fusion in pelvis pose estimation during treadmill walking. In addition, orientation estimation has been obtained without IMU magnetometer data, in order to propose a method suitable also for environments where magnetic disturbances could arise. Pelvis heading (yaw), bank (roll) and attitude (pitch) angles have been evaluated both using the proposed filter and a gold standard optometric system. The root mean square errors obtained using the proposed sensor fusion with respect to the gold standard are below 1 degree for each axis, showing also a significant high correlation (> 0.90). Findings of this study highlight the suitability of a magnetometer-free UKF approach for pose estimation of pelvis during human walking on treadmill, providing information useful also for further estimation of center of mass displacement in the same experimental conditions.

Description of Postural Strategies through a Variable Structure Control.

Human postural strategies in balance maintenance are the results of the complex control action played by the Central Nervous System (CNS). Literature underlined that such strategies become more evident when external perturbations challenge the stance. In this study, a new model of balance maintenance under support base movement perturbation is formulated. A sliding mode approach is employed to simulate the aforementioned strategies in stabilizing a double inverted pendulum, used to describe the mechanics of the bipedal human stance. Control parameters are then optimized in order to reproduce the measured center of mass (COM) displacement in the anterior-posterior direction. Such parameters seem to be useful to distinguish different postural strategies employed by different subjects. Moreover, electromyographic data are employed to effectively support the goodness of the model.