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Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human–computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios. [ABSTRACT FROM AUTHOR]

Featured Application: Results of this work could be useful in the field of hand gesture recognition based on myoelectric signal processing, which has widespread usage for robotic arms and upper limb prosthesis control. The surface electromyography signal (sEMG) is widely used for gesture characterization; its reliability is strongly connected to the features extracted from sEMG recordings. This study aimed to investigate the use of two complexity measures, i.e., fuzzy entropy (FEn) and permutation entropy (PEn) for hand gesture characterization. Fourteen upper limb movements, sorted into three sets, were collected on ten subjects and the performances of FEn and PEn for gesture descriptions were analyzed for different computational parameters. FEn and PEn were able to properly cluster the expected numbers of gestures, but computational parameters were crucial for ensuring clusters' separability and proper gesture characterization. FEn and PEn were also compared with other eighteen classical time and frequency domain features through the minimum redundancy maximum relevance algorithm and showed the best predictive importance scores in two gesture sets; they also had scores within the subset of the best five features in the remaining one. Further, the classification accuracies of four different feature sets presented remarkable increases when FEn and PEn are included as additional features. Outcomes support the use of FEn and PEn for hand gesture description when computational parameters are properly selected, and they could be useful in supporting the development of robotic arms and prostheses myoelectric control. [ABSTRACT FROM AUTHOR]

In this article, a self-contained procedure to estimate the vertical, medial–lateral, and anterior–posterior displacement of a single sacrum-worn inertial measurement unit (IMU) is presented, which can be related to the human body center of mass (CoM) displacement during treadmill walking through an adaptation of the sacral marker method. Furthermore, a magnetometer-free custom sensor-fusion algorithm for orientation estimation is proposed alongside a practical alignment procedure to refer relative IMU orientation estimation to a ground-fixed reference frame. Twelve healthy subjects performed two trials of treadmill walking at 3, 4, and 5 km/h for 150 s, with a sacrum-worn IMU. Orientation and displacement estimations were then compared with those obtained from an optoelectronic measurement system. Roll, pitch, and yaw angles showed root mean square error (RMSE) lower than 2° for walking trials at 3, 4, and 5 km/h, with Pearson’s correlation coefficient higher than 0.90 for each angle. Displacement accuracy was evaluated in terms of peak-to-trough distances and RMSE. Mean errors resulted lower than 1 mm for each axis of interest and for each gait speed, with RMSE not higher than 2.5 mm. The proposed off-line algorithm can be used in low-budget and infrastructure-free environments, to achieve reliable CoM displacement estimation during cyclic activities such as treadmill walking. [ABSTRACT FROM AUTHOR]

This paper proposes a free dataset, available at the following link, named KIMORE, regarding different rehabilitation exercises collected by a RGB-D sensor. Three data inputs including RGB, depth videos, and skeleton joint positions were recorded during five physical exercises, specific for low back pain and accurately selected by physicians. For each exercise, the dataset also provides a set of features, specifically defined by the physicians, and relevant to describe its scope. These features, validated with respect to a stereophotogrammetric system, can be analyzed to compute a score for the subject’s performance. The dataset also contains an evaluation of the same performance provided by the clinicians, through a clinical questionnaire. The impact of KIMORE has been analyzed by comparing the output obtained by an example of rule and template-based approaches and the clinical score. The dataset presented is intended to be used as a benchmark for human movement assessment in a rehabilitation scenario in order to test the effectiveness and the reliability of different computational approaches. Unlike other existing datasets, the KIMORE merges a large heterogeneous population of 78 subjects, divided into 2 groups with 44 healthy subjects and 34 with motor dysfunctions. It provides the most clinically-relevant features and the clinical score for each exercise. 1 https://univpm-my.sharepoint.com/:f:/g/personal/p008099_staff_univpm_it/EiwbKIzk6N9NoJQx4J8aubIBx0o7tIa1XwclWp1NmRkA-w?e=F3jtBk [ABSTRACT FROM AUTHOR]

The present work proposes an interactive software tool for surface electromyography signal processing and analysis, focused on the assessment of muscular activations during walking and easy-to-use also for users without signal treatment knowledge. The tool is based on two logical steps: first, an automatic detection of muscle activations is provided; then, the user can tune the value of each processing parameter, with a real-time visual feedback. A stride-to-stride evaluation of muscular recruitment and a co-contraction recognition are available, together with the computation of an average myoelectric activation pattern. The tunable signal processing tailors the analysis to any considered application, allowing to extract information from low-quality signals. A muscle activity characterisation from the single stride to the whole walking trial is also provided. Beyond gait analysis and muscles activation timing identification, this tool could be valuable also in an educational scenario, considering the interactive characteristics of the graphical user interface. [ABSTRACT FROM AUTHOR]

The functional reach test (FRT) is widely used for assessing dynamic balance stability in elderly and pathological subjects. Force platforms (FPs) represent a fundamental part of the instrumented FRT experimental setup due to the central role of center-of-pressure (COP) displacement in FRT analysis. Recently, the nintendo wii balance board (NBB) has been suggested as a low-cost and reliable device for ground reaction force and COP measurement in poorly dynamic motor tasks. Therefore, this paper aimed to compare NBB-COP data with those obtained from a laboratory-grade platform during FRT. Data from 48 healthy subjects were simultaneously acquired from both devices. FP-COP and NBB-COP trajectories showed a remarkable correlation in both directions ( ${r}>\textsf {0.990}$ ) and low root-mean-square error values (1.14 ± 0.88 mm and 0.55 ± 0.28 mm for anterior–posterior and medial–lateral direction). Fixed biases between COP-based parameters did not exceed 2% of the FP outcomes with high consistency throughout the present measurement range (ICC consistency always >0.950). Only the COP mean velocity exhibited a tendency toward proportional errors, which can be adjusted by a calibration of NBB data. Findings of this paper confirmed the NBB validity for COP measurement in a widely used motor task as the functional reach, supporting the feasibility of NBB in research scenarios. [ABSTRACT FROM AUTHOR]

Here, the authors present a low cost telerehabilitation system made up of a commercial red-green-blue depth (RGBD) camera and a web-based platform. The authors goal is to monitor and assess subject movement providing acceptable and usable at-home remote rehabilitation services without the presence of a clinician. Clinical goals, defined by physiotherapists, are firstly translated into motion analysis features. A Takagi Sugeno fuzzy inference system (FIS) is then proposed to evaluate and combine these features into scores. In this stage, the 'collaborative design' paradigm is used in depth and complete manner: the contribution of the clinician is not limited only to the rules definition but enters in the core of the evaluation algorithm through the definition of the fuzzy rules. A case study on low back pain rehabilitation involving 40 subjects, 5 exercises, and 4 physiotherapists is then presented to the effectiveness of the proposed system. Results of the validation of the system aimed at the assessment of the reliability of the proposed approach show high correlations between clinician evaluation and FIS scores. In this scenario, due to the high correlation, each FIS could represent a virtual alter-ego of the physiotherapist which enable a real time and free second opinion. [ABSTRACT FROM AUTHOR]

Background: Heel reconstruction represents a challenge for all plastic surgeons due to the anatomical and functional features of this weight-bearing area. In the last decade a combined use of acellular dermal matrices and skin grafts has been proposed as a reliable and less invasive alternative for complex wound management; nevertheless only a few cases have been reported in the literature. Methods: We describe the long-term outcome of 2 cases of severe degloving trauma of the plantar region with massive soft tissue defects of the foot, that underwent surgical reconstruction with artificial dermis and skin grafts. At the fifth year of follow-up, both patients underwent a clinical and a computerized gait analysis to study their functional outcomes and the kinematics of their gait. Results: Both patients recovered functional ambulation and returned to their own work and vocational activities, showing a symmetric gait and parameters of upright posture fully comparable to normality. Conclusions: Despite the initial concerns about the use of acellular dermal matrices and skin grafts for this kind of injury, they seem to be a simple and safe alternative for weight-bearing reconstruction of the degloved foot. The authors believe that the current study yields useful information and reassurance about their long-term reliability. [ABSTRACT FROM AUTHOR]

Background and Objectives: The use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well.Methods: This work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland-Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects.Results: High correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count.Conclusion: This study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications. [ABSTRACT FROM AUTHOR]

Background: In this paper, squat named by Authors unconstrained because performed without constrains related to feet position, speed, knee maximum angle to be reached, was tested as motor task revealing differences in functional performance after knee arthroplasty. It involves large joints ranges of motion, does not compromise joint safety and requires accurate control strategies to maintain balance. Methods: Motion capture techniques were used to study squat on a healthy control group (CTR) and on three groups, each characterised by a specific knee arthroplasty design: a Total Knee Arthroplasty (TKA), a Mobile Bearing and a Fixed Bearing Unicompartmental Knee Arthroplasty (respectively MBUA and FBUA). Squat was analysed during descent, maintenance and ascent phase and described by speed, angular kinematics of lower and upper body, the Center of Pressure (CoP) trajectory and muscle activation timing of quadriceps and biceps femoris. Results: Compared to CTR, for TKA and MBUA knee maximum flexion was lower, vertical speed during descent and ascent reduced and the duration of whole movement was longer. CoP mean distance was higher for all arthroplasty groups during descent as higher was, CoP mean velocity for MBUA and TKA during ascent and descent. Conclusions: Unconstrained squat is able to reveal differences in the functional performance among control and arthroplasty groups and between different arthroplasty designs. Considering the similarity index calculated for the variables showing statistically significance, FBUA performance appears to be closest to that of the CTR group. Level of evidence: III a. [ABSTRACT FROM AUTHOR]

The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: The freezing of gait (FOG) is a common and highly distressing motor symptom in patients with Parkinson's Disease (PD). Effective management of FOG is difficult given its episodic nature, heterogeneous manifestation and limited responsiveness to drug treatment.Methods: In order to verify the acceptance of a smartphone-based architecture and its reliability at detecting FOG in real-time, we studied 20 patients suffering from PD-related FOG. They were asked to perform video-recorded Timed Up and Go (TUG) test with and without dual-tasks while wearing the smartphone. Video and accelerometer recordings were synchronized in order to assess the reliability of the FOG detection system as compared to the judgement of the clinicians assessing the videos. The architecture uses two different algorithms, one applying the Freezing and Energy Index (Moore-Bächlin Algorithm), and the other adding information about step cadence, to algorithm 1.Results: A total 98 FOG events were recognized by clinicians based on video recordings, while only 7 FOG events were missed by the application. Sensitivity and specificity were 70.1% and 84.1%, respectively, for the Moore-Bächlin Algorithm, rising to 87.57% and 94.97%, respectively, for algorithm 2 (McNemar value=28.42; p=0.0073).Conclusion: Results confirm previous data on the reliability of Moore-Bächlin Algorithm, while indicating that the evolution of this architecture can identify FOG episodes with higher sensitivity and specificity. An acceptable, reliable and easy-to-implement FOG detection system can support a better quantification of the phenomenon and hence provide data useful to ascertain the efficacy of therapeutic approaches. [ABSTRACT FROM AUTHOR]

Gait analysis is recognized as a useful assessment tool in the field of human movement research. However, doubts remain on its real effectiveness as a clinical tool, i.e. on its capability to change the diagnostic-therapeutic practice. In particular, the conditions in which evidence of a favorable cost-benefit ratio is found and the methodology for properly conducting and interpreting the exam are not identified clearly. To provide guidelines for the use of Gait Analysis in the context of rehabilitation medicine, SIAMOC (the Italian Society of Clinical Movement Analysis) promoted a National Consensus Conference which was held in Bologna on September 14th, 2013. The resulting recommendations were the result of a three-stage process entailing i) the preparation of working documents on specific open issues, ii) the holding of the consensus meeting, and iii) the drafting of consensus statements by an external Jury. The statements were formulated based on scientific evidence or experts' opinion, when the quality/quantity of the relevant literature was deemed insufficient. The aim of this work is to disseminate the consensus statements. These are divided into 13 questions grouped in three areas of interest: 1) General requirements and management, 2) Methodological and instrumental issues, and 3) Scientific evidence and clinical appropriateness. SIAMOC hopes that this document will contribute to improve clinical practice and help promoting further research in the field. [ABSTRACT FROM AUTHOR]

The early diagnosis of diabetic neuropathy (DN) is fundamental in order to enact timely therapeutic strategies for limiting disease progression. In this work, we explored the suitability of standing balance task for identifying the presence of DN. Further, we proposed two diagnosis pathways in order to succeed in distinguishing between different stages of the disease. We considered a cohort of non-neuropathic (NN), asymptomatic neuropathic (AN), and symptomatic neuropathic (SN) diabetic patients. From the center of pressure (COP), a series of features belonging to different description domains were extracted. In order to exploit the whole information retrievable from COP, a majority voting ensemble was applied to the output of classifiers trained separately on different COP components. The ensemble of kNN classifiers provided over 86% accuracy for the first diagnosis pathway, made by a 3-class classification task for distinguishing between NN, AN, and SN patients. The second pathway offered higher performances, with over 97% accuracy in identifying patients with symptomatic and asymptomatic neuropathy. Notably, in the last case, no asymptomatic patient went undetected. This work showed that properly leveraging all the information that can be mined from COP trajectory recorded during standing balance is effective for achieving reliable DN identification. This work is a step toward a clinical tool for neuropathy diagnosis, also in the early stages of the disease.

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.

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.

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.

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)., (© 2019 The Author(s).)

Background: The gastrocnemius and soleus muscle flaps are the most commonly used transfer flaps to treat lower limb severe lesions. The use of the muscle as coverage tissuemay incur in functionality loss and motor control deficits. The aim of this study is to assess whether functional deficits are present during gait, posture and heel rise test (HRT)., Method: From 2008-2013 we treated 9 patients suffering from complex injury of the lower third of the leg (Gustilo III B). A population of 9 patients (group A) was examined after reconstruction with a soleus muscle flap. The results were compared with a control population (group B: 9 patients). Each patient performed at least 3 gait tests at a fast pace, 2 posture tests and 2 HRT on an inclined plane., Results: Gait parameters for group A: Spatio-temporal parameters such as step length, cadence were reduced compared to the homolateral leg (p < 0.05). The initial double support phase was shorter in relation to the final phase and the first peak in the vertical ground reaction component was reduced compared to the contralateral leg.Posture for the group A: the mediolateral excursion (during open- and closed-eyes test) and the related mean velocity (in the open-eyes test) were significantly lower compared to the control group (p < 0.05).HRT for group A: the number of rises and relative height and angular excursion were reduced in the operated leg in relation to the control group (p < 0.05) and to the contralateral leg., Conclusion: The patients treated with the soleus muscle used as tissue defect coverage completely recovered the ability to walk but showed some deficits in kinetic parameters during the body weight-loading phase. During gait prolonged muscle activity compared to controls was observed. The absence of soleus (group A) revealed no significant alterations in postural control. However, the HRT remained the most relevant test to measure patients' performance when treated with soleus flap in relation to the control group., Competing Interests: The authors declare that they have no conflict of interest., (© Georg Thieme Verlag KG Stuttgart · New York.)

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.

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.

Upright stance maintenance under perturbed condition is a complex phenomenon in which the Central Nervous System(CNS) is engaged to regulate the balance preventing subject to fall. Many models of unperturbed stance are present in literature. However, the necessity to model balance maintenance in presence of external disturbance is still an open problem. In this paper, a new model representing the human balance maintenance under perturbed condition is presented. An adaptive sliding mode approach is used to model the action played by the CNS, the control parameters are then optimized in order to match real and simulated data. The trend of optimized parameters seems to reveal the development of different postural strategies throughout the experimental trials.

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.

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.