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Objective: This study presents the walking abilities of participants fitted with transfemoral bone-anchored prostheses using a total of 14 gait parameters., Design: Two-centre retrospective cross-sectional comparative study., Setting: Research facilities equipped with tridimensional motion capture systems., Participants: Two control arms included eight able-bodied participants arm (54 ± 9 years, 1.75 ± 0.07 m, 76 ± 7 kg) and nine participants fitted with transfemoral socket-suspended prostheses arm (59 ± 9 years, 1.73 ± 0.07 m, 80 ± 16 kg). The intervention arm included nine participants fitted with transfemoral bone-anchored prostheses arm (51 ± 13 years, 1.78 ± 0.09 m, 87.3 ± 16.1 kg)., Intervention: Fitting of transfemoral bone-anchored prostheses., Main Measures: Comparisons were performed for two spatio-temporal, three spatial and nine temporal gait parameters., Results: The cadence and speed of walking were 107 ± 6 steps/min and 1.23 ± 0.19 m/s for the able-bodied participants arm, 88 ± 7 steps/min and 0.87 ± 0.17 m/s for the socket-suspended prosthesis arm, and 96 ± 6 steps/min and 1.03 ± 0.17 m/s for bone-anchored prosthesis arm, respectively. Able-bodied participants and bone-anchored prosthesis arms were comparable in age, height, and body mass index as well as cadence and speed of walking, but the able-bodied participant arm showed a swing phase 31% shorter. Bone-anchored and socket-suspended prostheses arms were comparable for age, height, mass, and body mass index as well as cadence and speed of walking, but the bone-anchored prosthesis arm showed a step width and duration of double support in seconds 65% and 41% shorter, respectively., Conclusions: Bone-anchored and socket-suspended prostheses restored equally well the gait parameters at a self-selected speed. This benchmark data provides new insights into the walking ability of individuals using transfemoral bionics bone-anchored prostheses., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Laurent Frossard, Professor of Bionics at Griffith university and Director/Chief Scientist Officer of YourResearchProject Pty Ltd, has worked as consultant for several organizations on non-related educational programs and projects of research focusing on recording loading data, developing of database to record clinical outcomes as well as drafting grants and manuscripts for APC, Cognitive Institute, Exercise & Sports Science Australia, Great Lakes Insurance c/-Meridian Lawyers, Griffith University, Institut national d’excellence en santé et service sociaux, International Confederation of Amputee Associations (IC2A), iPug Pty Ltd, Middlesex University, New Zealand Artificial Limb Service, Osseointegration Group of Australia Pty Ltd, OSSUR-France, OSSUR-Iceland, Poly-Orthodox International, Queensland Artificial Limb Service, Queensland University of Technology, Return to Work-South Australia, Royal Melbourne Hospital, South Australia Health, Tequir S.L., The University of Melbourne, University of the New South Whales, University of the Sunshine Coast.

Low back pain (LBP) is a leading cause of disability in the workplace, often caused by manually lifting of heavy loads. Instrumental-based assessment tools are used to quantitatively assess the biomechanical risk of lifting activities. This study aims to verify that, during the execution of fatiguing frequency-dependent lifting, high-density surface electromyography (HDsEMG) allows the discrimination of healthy controls (HC) versus people with LBP and biomechanical risk levels. Fifteen HC and eight people with LBP performed three lifting tasks with a progressively increasing lifting index, each lasting 15 min. Erector spinae (ES) activity was recorded using HDsEMG and amplitude parameters were calculated to characterize the spatial distribution of muscle activity. LBP group showed a less ES activity than HC (lower root mean square across the grid and of the activation region) and an involvement of the same muscular area across the task (lower coefficient of variation of the center of gravity of muscle activity). The results indicate the usefulness of HDsEMG parameters to classify risk levels for both HC and LBP groups and to determine differences between them. The findings suggest that the use of HDsEMG could expand the capabilities of existing instrumental-based tools for biomechanical risk classification during lifting activities., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Introduction: Human robot collaboration is quickly gaining importance in the robotics and ergonomics fields due to its ability to reduce biomechanical risk on the human operator while increasing task efficiency. The performance of the collaboration is typically managed by the introduction of complex algorithms in the robot control schemes to ensure optimality of its behavior; however, a set of tools for characterizing the response of the human operator to the movement of the robot has yet to be developed., Methods: Trunk acceleration was measured and used to define descriptive metrics during various human robot collaboration strategies. Recurrence quantification analysis was used to build a compact description of trunk oscillations., Results and Discussion: The results show that a thorough description can be easily developed using such methods; moreover, the obtained values highlight that, when designing strategies for human robot collaboration, ensuring that the subject maintains control of the rhythm of the task allows to maximize comfort in task execution, without affecting efficiency., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ranaldi, Bibbo, Corvini, Schmid and Conforto.)

The increasing incidence of cardiovascular diseases (CVDs) is reflected in additional costs for healthcare systems all over the world. To date, pulse transit time (PTT) is considered a key index of cardiovascular health status and for diagnosis of CVDs. In this context, the present study focuses on a novel image analysis-based method for PTT estimation through the application of equivalent time sampling. The method, which post-processes color Doppler videos, was tested on two different setups: a Doppler flow phantom set in pulsatile mode and an in-house arterial simulator. In the former, the Doppler shift was due to the echogenic properties of the blood mimicking fluid only, since the phantom vessels are non-compliant. In the latter, the Doppler signal relied on the wall movement of compliant vessels in which a fluid with low echogenic properties was pumped. Therefore, the two setups allowed the measurement of the flow average velocity (FAV) and the pulse wave velocity (PWV), respectively. Data were collected through an ultrasound diagnostic system equipped with a phased array probe. Experimental outcomes confirm that the proposed method can represent an alternative tool for the local measurement of both FAV in non-compliant vessels and PWV in compliant vessels filled with low echogenic fluids.

Mean and Median frequency are typically used for detecting and monitoring muscle fatigue. These parameters are extracted from power spectral density whose estimate can be obtained by several techniques, each one characterized by advantages and disadvantages. Previous works studied how the implementation settings can influence the performance of these techniques; nevertheless, the estimation results have never been fully evaluated when the power density spectrum is in a low-frequency zone, as happens to the surface electromyography (sEMG) spectrum during muscle fatigue. The latter is therefore the objective of this study that has compared the Welch and the autoregressive parametric approaches on synthetic sEMG signals simulating severe muscle fatigue. Moreover, the sensitivity of both the approaches to the observation duration and to the level of noise has been analyzed. Results showed that the mean frequency greatly depends on the noise level, and that for Signal to Noise Ratio (SNR) less than 10dB the errors make the estimate unacceptable. On the other hand, the error in calculating the median frequency is always in the range 2-10 Hz, so this parameter should be preferred in the tracking of muscle fatigue. Results show that the autoregressive model always outperforms the Welch technique, and that the 3rd order continuously produced accurate and precise estimates; consequently, the latter should be used when analyzing severe fatiguing contraction.

Muscle synergy analysis has been widely adopted in the literature for the analysis of upper limb surface electromyographic signals during reaching tasks and for the prediction of movement direction for myoelectric control purposes. However, previous studies have characterized movements in constrained or semi-constrained scenarios, in which the subjects performing the movement were instructed to reach particular targets or were given some kind of feedback. In this work, the same synergy model has been applied to a completely unconstrained upper limb reaching experiment, with the aim of classifying the height of the target starting from the activity of the synergies. Results show that the synergistic model is able to extract compact features that can identify with good performance three different reaching heights. Moreover, this representation is able to isolate the signals that contain predictive information about the movement direction from the ones that are related to movement timing; this, together with the good performance of the synergy-based classifier supports the proposal of applying this model to the pre-processing of electromyographic signals when dealing with control systems that use signals from multiple muscles to predict movements.

The estimation of the sEMG-force relationship is an open problem in the scientific literature; current methods show different limitations and can achieve good performance only on limited scenarios, failing to identify a general solution to the optimization of this kind of analysis. In this work, this relationship has been estimated on two different datasets related to isometric force-tracking experiments by calculating the sEMG amplitude using different fixed-time constant moving-window filters, as well as an adaptive time-varying algorithm. Results show how the adaptive methods might be the most appropriate choice for the estimation of the correlation between the sEMG signal and the force time course. Moreover, the comparison between adaptive and standard filters highlights how the time constants exploited in the estimation strategy is not the only influence factor on this kind of analysis; a time-varying approach is able to constantly capture more information with respect to fixed stationary approaches with comparable window lengths.

Prosthetic gait implies the use of compensatory motor strategies, including alterations in gait biomechanics and adaptations in the neural control mechanisms adopted by the central nervous system. Despite the constant technological advancements in prostheses design that led to a reduction in compensatory movements and an increased acceptance by the users, a deep comprehension of the numerous factors that influence prosthetic gait is still needed. The quantitative prosthetic gait analysis is an essential step in the development of new and ergonomic devices and to optimize the rehabilitation therapies. Nevertheless, the assessment of prosthetic gait is still carried out by a heterogeneous variety of methodologies, and this limits the comparison of results from different studies, complicating the definition of shared and well-accepted guidelines among clinicians, therapists, physicians, and engineers. This perspective article starts from the results of a project funded by the Italian Worker's Compensation Authority (INAIL) that led to the generation of an extended dataset of measurements involving kinematic, kinetic, and electrophysiological recordings in subjects with different types of amputation and prosthetic components. By encompassing different studies published along the project activities, we discuss the specific information that can be extracted by different kinds of measurements, and we here provide a methodological perspective related to multimodal prosthetic gait assessment, highlighting how, for designing improved prostheses and more effective therapies for patients, it is of critical importance to analyze movement neural control and its mechanical actuation as a whole, without limiting the focus to one specific aspect., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 De Marchis, Ranaldi, Varrecchia, Serrao, Castiglia, Tatarelli, Ranavolo, Draicchio, Lacquaniti and Conforto.)

In recent years, statistical studies highlighted an increasing incidence of cardiovascular diseases (CVD) which reflected on additional costs on the healthcare systems worldwide. Pulse wave velocity (PWV) measurement is commonly considered a CVD predictor factor as well as a marker of Arterial Stiffness (AS) since it is closely related to the mechanical characteristics of the arterial wall. An increase in PWV is due to a more rigid arterial system. Because of the prevalence of the elastic component, in young people the PWV is lower than in the elderly. Nowadays, invasive and non-invasive methods for PWV assessment are employed: there is an increasing attention in the development of non-invasive devices which mostly perform a regional PWV measurement (over a long arterial portion) rather than local (over a short arterial portion). The accepted gold-standard for non-invasive AS measurement is the carotid-femoral PWV used to evaluate the arterial damage, the corresponding cardiovascular risk and to adapt the proper therapy. This review article considers the main commercially available devices underlining their operating principles in terms of sensors, execution mode, pulse waveforms acquired, site of measurement, distance and time estimation methods, as well as their main limitations in clinical practice.

Muscle synergy analysis is a useful tool for the evaluation of the motor control strategies and for the quantification of motor performance. Among the parameters that can be extracted, most of the information is included in the rank of the modular control model (i.e. the number of muscle synergies that can be used to describe the overall muscle coordination). Even though different criteria have been proposed in literature, an objective criterion for the model order selection is needed to improve reliability and repeatability of MSA results. In this paper, we propose an Akaike Information Criterion (AIC)-based method for model order selection when extracting muscle synergies via the original Gaussian Non-Negative Matrix Factorization algorithm. The traditional AIC definition has been modified based on a correction of the likelihood term, which includes signal dependent noise on the neural commands, and a Discrete Wavelet decomposition method for the proper estimation of the number of degrees of freedom of the model, reduced on a synergy-by-synergy and event-by-event basis. We tested the performance of our method in comparison with the most widespread ones, proving that our criterion is able to yield good and stable performance in selecting the correct model order in simulated EMG data. We further evaluated the performance of our AIC-based technique on two distinct experimental datasets confirming the results obtained with the synthetic signals, with performances that are stable and independent from the nature of the analysed task, from the signal quality and from the subjective EMG pre-processing steps.

Background: Duchenne muscular dystrophy is an X-linked muscle disease caused by dystrophin absence. Muscle weakness is a major determinant of the gait impairments in patients with Duchenne muscular dystrophy and it affects lower limbs more often than upper limbs. Monitoring progression of motor symptoms is key to plan treatments for prolonging ambulation., Methods: The progression of gait impairment in a group of ten patients with Duchenne muscular dystrophy was observed longitudinally three times over a period of 2 years by computerized gait analysis system. Spatio-temporal parameters of gait, and variability indicators were extracted from kinematics, while lower limb muscles coactivation were measured at the baseline and at each follow-up evaluation. The 6-min walk test was used to evaluate functional capacity at each time session., Findings: We found a significant increase in stride width and in both stride width and stride length variability at the 1-and 2-year follow-up evaluations. Furthermore, significant higher values in proximal muscle coactivation and significant lower values in both distal muscle coactivation and functional capacity were found at the 2-year follow-up evaluation. Significant negative correlations between muscle coactivation at proximal level and functional capacity and between muscle coactivation at distal level and gait variability were observed., Interpretation: Our findings suggest that patients with Duchenne muscular dystrophy exhibit decline in functional capacity after 2 years from the baseline. Moreover, to cope with disease progression, patients try to maintain an effective gait by changing the balance dynamic strategies (i.e. increase in proximal muscle coactivation) during the course of disease., Competing Interests: Declaration of Competing Interest Authors declare no commercial or other associations that might pose a conflict of interest in connection with the present article., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Subjects with transfemoral amputation (TFA) show an asymmetric gait pattern associated with a decreased ability to recover mechanical energy and an increased metabolic cost of walking., Research Question: This study aimed to identify the spatio-temporal and kinematic gait variables correlated with mechanical energy values in subjects with TFA and to observe the ability of the identified parameters to discriminate between TFA and controls according to the type of prosthesis., Methods: The gait of 40 subjects with TFA was evaluated with a motion 3-D optoelectronic system. Nine subjects wore a mechanical prosthesis (TFAm), seventeen a C-Leg prosthesis (TFAc), and fourteen a Genium prosthesis (TFAg). Spatio-temporal and pelvic kinematic parameters were measured. Energy recovery was measured relative to the whole-body center of mass (CoM) kinematics as the fraction of mechanical energy recovered during each walking step (R-step). Correlation tests and multiple linear regression analyses were used to evaluate the correlation and association between kinematic and energy variables, respectively. Receiver operating characteristics curves were plotted to assess the ability of the correlated parameter to distinguish subjects with TFA from controls, and optimal cutoff point values were calculated according to the type of prosthesis., Results: Among the spatio-temporal and kinematic parameters correlated to R-step, only pelvic obliquity of the prosthetic side was significantly associated with R-step. It showed an excellent ability to discriminate between TFA and controls. Furthermore, pelvic obliquity showed an excellent discriminative ability in identifying TFAm and TFAc and a good discriminative ability in identifying TFAg from controls., Significance: Pelvic obliquity plays an important role in energy recovery during gait for subjects using prosthetics. This information might be exploited to monitor the adaptation of subjects with TFA to prosthetic devices, to lower the energetic cost of walking potentially, and to reduce the long-term risks of secondary physical complications in prosthetic users., Competing Interests: Declaration of Competing Interest The authors declare that there are no conflicts of interest, (Copyright © 2020 Elsevier B.V. All rights reserved.)

The aim of this study was to analyze the effect of the level of amputation and various prosthetic devices on the muscle activation of the sound limb in people with unilateral transfemoral and transtibial amputation. We calculated the global coactivation of 12 muscles using the time-varying multimuscle coactivation function method in 37 subjects with unilateral transfemoral amputation (10, 16, and 11 with mechanical, electronic, and bionic prostheses, respectively), 11 subjects with transtibial amputation, and 22 healthy subjects representing the control group. The results highlighted that people with amputation had a global coactivation temporal profile similar to that of healthy subjects. However, amputation increased the level of the simultaneous activation of many muscles during the loading response and push-off phases of the gait cycle and decreased it in the midstance and swing subphases. This increased coactivation probably plays a role in prosthetic gait asymmetry and energy consumption. Furthermore, people with amputation and wearing electronic prosthesis showed lower global coactivation when compared with people wearing mechanical and bionic prostheses. These findings suggest that the global lower limb coactivation behavior can be a useful tool to analyze the motor control strategies adopted and the ability to adapt to the prosthetic device.

Background: The above-knee amputation of a lower limb is a severe impairment that affects significantly the ability to walk; considering this, a complex adaptation strategy at the neuromuscular level is needed in order to be able to move safely with a prosthetic knee. In literature, it has been demonstrated that muscle activity during walking can be described via the activation of a small set of muscle synergies. The analysis of the composition and the time activation profiles of such synergies have been found to be a valid tool for the description of the motor control schemes in pathological subjects., Methods: In this study, we used muscle synergy analysis techniques to characterize the differences in the modular motor control schemes between a population of 14 people with trans-femoral amputation and 12 healthy subjects walking at two different (slow and normal self-selected) speeds. Muscle synergies were extracted from a 12 lower-limb muscles sEMG recording via non-negative matrix factorization. Equivalence of the synergy vectors was quantified by a cross-validation procedure, while differences in terms of time activation coefficients were evaluated through the analysis of the activity in the different gait sub-phases., Results: Four synergies were able to reconstruct the muscle activity in all subjects. The spatial component of the synergy vectors did not change in all the analysed populations, while differences were present in the activity during the sound limb's stance phase. Main features of people with trans-femoral amputation's muscle synergy recruitment are a prolonged activation of the module composed of calf muscles and an additional activity of the hamstrings' module before and after the prosthetic heel strike., Conclusions: Synergy-based results highlight how, although the complexity and the spatial organization of motor control schemes are the same found in healthy subjects, substantial differences are present in the synergies' recruitment of people with trans femoral amputation. In particular, the most critical task during the gait cycle is the weight transfer from the sound limb to the prosthetic one. Future studies will integrate these results with the dynamics of movement, aiming to a complete neuro-mechanical characterization of people with trans-femoral amputation's walking strategies that can be used to improve the rehabilitation therapies.

The present study's aim was to identify the kinematic and kinetic gait patterns and to measure the energy consumption in people with amputation according to both the anatomical level of amputation and the type of prosthetic components in comparison with a control group matched for the gait speed. Fifteen subjects with unilateral transtibial amputation (TTA), forty with unilateral transfemoral amputation (TFA) (9 with mechanical, 17 with CLeg and 14 with Genium prosthesis) and forty healthy subjects were recruited. We computed the time-distance gait parameters; the range of angular motion (RoM) at hip, knee and ankle joints, and at the trunk and pelvis; the values of the 2 peaks of vertical force curve; the full width at half maximum (FWHM) and center of activity (CoA) of vertical force; the mechanical behavior in terms of energy recovery (R-step) and energy consumption. The main results were: i) both TTA and TFA show a common gait pattern characterized by a symmetric increase of step length, step width, double support duration, pelvic obliquity, trunk lateral bending and trunk rotation RoMs compared to control groups. They show also an asymmetric increase of stance duration and of Peak1 in non-amputated side and a decrease of ankle RoM in amputated side; ii) only TFA show a specific gait pattern, depending on the level of amputation, characterized by a symmetric reduction of R-step and an asymmetric decrease of stance duration, CoA and FWHM and an increase of Peak1 in the amputated side and of hip and knee RoM, CoA and FWHM in the non-amputated side; iii) people with amputation with Genium prosthesis show a longer step length and increased hip and knee RoMs compared to people with amputation with mechanical prosthesis who conversely show an increased pelvic obliquity: these are specific gait patterns depending of the type of prosthesis. In conclusion, we identified both common and specific gait patterns in people with amputation, either regardless of, or according to their level of amputation and the type of prosthetic component., (Copyright © 2019 Elsevier B.V. All rights reserved.)

12 young adults were requested to walk along a circuitous path including turns, slaloms, stair ascending and descending, while wearing an inertial sensor placed on the back at the lumbar level. The path was completed under two conditions: with no additive cognitive task, and while performing a cognitive task and texting on a smartphone. Different temporal global parameters of gait were extracted from the inertial sensor data, to check for differences driven by the presence of the cognitive task. Regularity, durations, and temporal characteristics of gait resulted significantly affected from the presence of the additional task, and this effect was only in part due to a modification coming from the decrease in walking speed.

Muscular weakness is one of the main signs associated with the onset and progression of Duchenne Muscular Dystrophy. During motor functions, this disease also determines deviations in muscular activity, especially in terms of coordination and activation between muscles acting on the same joints. In this study, surface EMG activity of the lower limb muscles of 10 children with Duchenne Muscular Dystrophy at different times from disease onset were recorded along with kinematics during unconstrained gait. Muscular co-activation of muscle pairs was then evaluated by extracting different co-activation indicators, and linking them with kinematic markers of motor function. The combination of disease progression and pharmacological treatment resulted in a significant decrease in terms of co-activation indexes for two pairs of agonist-antagonist muscles, and for one of these two pairs the decrease in co-activation was correlated with a decrease in the motor function of gait.

The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a novel deep learning-based algorithm suited for volumetric colorectal tumor segmentation is proposed. The proposed CNN architecture, based on densely connected neural network, contains multiscale dense interconnectivity between layers of fine and coarse scales, thus leveraging multiscale contextual information in the network to get better flow of information throughout the network. Additionally, the 3D level-set algorithm was incorporated as a postprocessing task to refine contours of the network predicted segmentation. The method was assessed on T2-weighted 3D MRI of 43 patients diagnosed with locally advanced colorectal tumor (cT3/T4). Cross validation was performed in 100 rounds by partitioning the dataset into 30 volumes for training and 13 for testing. Three performance metrics were computed to assess the similarity between predicted segmentation and the ground truth (i.e., manual segmentation by an expert radiologist/oncologist), including Dice similarity coefficient (DSC), recall rate (RR), and average surface distance (ASD). The above performance metrics were computed in terms of mean and standard deviation (mean ± standard deviation). The DSC, RR, and ASD were 0.8406 ± 0.0191, 0.8513 ± 0.0201, and 2.6407 ± 2.7975 before postprocessing, and these performance metrics became 0.8585 ± 0.0184, 0.8719 ± 0.0195, and 2.5401 ± 2.402 after postprocessing, respectively. We compared our proposed method to other existing volumetric medical image segmentation baseline methods (particularly 3D U-net and DenseVoxNet) in our segmentation tasks. The experimental results reveal that the proposed method has achieved better performance in colorectal tumor segmentation in volumetric MRI than the other baseline techniques.

An office chair for analyzing the seated posture variation during the performance of a stress-level test is presented in this work. To meet this aim, we placed a set of textile pressure sensors both on the backrest and on the seat of the chair. The position of the sensors was selected for maximizing the detection of variations of user's posture. The effectiveness of the designed system was evaluated through an experiment where increasing stress levels were obtained by administering a Stroop test. The collected results had been analyzed by considering three different time intervals based on the difficulty level of the test (low, medium, and high). A transition analysis conducted on postures assumed during the test showed that participants reached a different posture at the end of the test, when the cognitive engagement increased, with respect to the beginning. This evidence highlighted the presence of movement presumably due to the increased cognitive engagement. Overall, the performed analysis showed the proposed monitoring system could be used to identify body posture variations related to different levels of engagement of a seated user while performing cognitive tasks., Competing Interests: The authors declare no conflict of interest.

Muscle co-activation is the mechanism that regulates simultaneous activity of agonist and antagonist muscles crossing the same joint. During functional movements, robust measurement techniques are required for an accurate determination of muscle co-activation, since various environmental and processing factors in the surface electromyography (sEMG) measurement process might influence the estimation of linear envelope profiles, and therefore the outcome of co-activation evaluated from the signal envelope. The aim of this study is to verify the performance of the co-activation indexes introduced in six different techniques used to assess muscle co-activation. A sensitivity analysis with respect to both noise and pre-processing choices for envelope estimation has been done by using a data-set of simulated sEMG signals. The results show how the indexes are affected by both the level of noise and pre-processing choices. The Vector Coding Technique and the Time-varying Multi-muscle approach perform better than the others in terms of both sensitivity to varying levels of co-activation and robustness to noise., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Muscle coactivation is the mechanism that regulates the simultaneous activity of antagonist muscles around the same joint. During walking, muscle joint coactivation varies within the gait cycle according to the functional role of the lower limb joints. In the present study, we used a time-varying multi-muscle coactivation function (TMCf) with the aim of investigating the coactivation of 12 lower limb muscles and its relationship with the gait cycle, gait speed (low, self-selected, and fast), ground reaction force, gait variability, and mechanical energy consumption, and recovery in a sample of 20 healthy subjects. Results show that the TMCf is speed dependent and highly repeatable within and between subjects, similar to the vertical force profile, and negatively correlated with energy recovery and positively correlated with both energy consumption and balance-related gait parameters. These findings suggest that the global lower limb coactivation behavior could be a useful measure of the motor control strategy, limb stiffness, postural stability, energy efficiency optimization, and several aspects in pathological conditions., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Surface ElectroMyography (sEMG) is widely used as a non-invasive tool for the assessment of motor control strategies. However, the standardization of the methods used for the estimation of sEMG amplitude is a problem yet to be solved; in most cases, sEMG amplitude is estimated through the extraction of the envelope of the signal via different low-pass filtering procedures with fixed cut-off frequencies chosen by the experimenter. In this work, we have shown how it is not possible to find the optimal choice of the cut-off frequency without any a priori knowledge on the signal; considering this, we have proposed an updated version of an iterative adaptive algorithm already present in literature, aiming to completely automatize the sEMG amplitude estimation. We have compared our algorithm to most of the typical solutions (fixed window filters and the previous version of the adaptive algorithm) for the extraction of the sEMG envelope, showing how the proposed adaptive procedure significantly improves the quality of the estimation, with a lower fraction of variance unexplained by the extracted envelope for different simulated modulating waveforms (p < 0.005). The definition of an entropy-based convergence criterion has allowed for a complete automatization of the process. We infer that this algorithm can ensure repeatability of the estimation of the sEMG amplitude, due to its independence from the experimental choices, so allowing for a quantitative interpretation in a clinical environment., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Humans can adapt their motor commands in response to alterations in the movement environment. This is achieved by tuning different motor primitives, generating adaptations that can be generalized also to relevant untrained scenarios. A theory of motor primitives has shown that natural movements can be described as combinations of muscle synergies. Previous studies have shown that motor adaptations are achieved by tuning the recruitment of robust synergy modules. Here we tested if: 1) different synergistic tunings can be achieved in response to the same perturbations applied with different orders of exposure; 2) different synergistic tunings can explain different patterns of generalization of adaptation. We found that exposing healthy individuals to two visuomotor rotation perturbations covering different parts of the same workspace in a different order resulted in different tunings of the activation of the same set of synergies. Nevertheless, these tunings resulted in the same net biomechanical adaptation patterns. We also show that the characteristics of the different tunings correlate with the presence and extent of generalization of adaptation to untrained portions of the workspace. Our results confirm synergies as invariant motor primitives whose recruitment is dynamically tuned during motor adaptations.

This work analyzes the results of measurements on thermal energy harvesting through a wearable Thermo-electric Generator (TEG) placed on the arms and legs. Four large skin areas were chosen as locations for the placement of the TEGs. In order to place the generator on the body, a special manufactured band guaranteed the proper contact between the skin and TEG. Preliminary measurements were performed to find out the value of the resistor load which maximizes the power output. Then, an experimental investigation was conducted for the measurement of harvested energy while users were performing daily activities, such as sitting, walking, jogging, and riding a bike. The generated power values were in the range from 5 to 50 &mu;W. Moreover, a preliminary hypothesis based on the obtained results indicates the possibility to use TEGs on leg for the recognition of locomotion activities. It is due to the rather high and different biomechanical work, produced by the gastrocnemius muscle, while the user is walking rather than jogging or riding a bike. This result reflects a difference between temperatures associated with the performance of different activities.

The term "task failure" describes the point when a person is not able to maintain the level of force required by a task. As task failure approaches, the corticospinal command to the muscles increases to maintain the required level of force in the face of a decreased mechanical efficacy. Nevertheless, most motor tasks require the synergistic recruitment of several muscles. How this recruitment is affected by approaching task failure is still not clear. The increase in the corticospinal drive could be due to an increase in synergistic recruitment or to overlapping commands sent to the muscles individually. Herein, we investigated these possibilities by combining intermuscular coherence and synergy analysis on signals recorded from three muscles of the quadriceps during dynamic leg extension tasks. We employed muscle synergy analysis to investigate changes in the coactivation of the muscles. Three different measures of coherence were used. Pooled coherence was used to estimate the command synchronous to all three muscles, pairwise coherence the command shared across muscle pairs and residual coherence the command peculiar to each couple of muscles. Our analysis highlights an overall decrease in synergistic command at task failure and an intensification of the contribution of the nonsynergistic shared command.

This study aimed to analyze the biomechanical consequences of reducing the base of support in patients with ataxia. Specifically, we evaluated the spatio-temporal parameters, upper- and lower-body kinematics, muscle co-activation, and energy recovery and expenditure. The gaits of 13 patients were recorded using a motion analysis system in unperturbed and perturbed walking conditions. In the latter condition, patients had to walk using the same step width and speed of healthy controls. The perturbed walking condition featured reduced gait speed, step length, hip and knee range of motion, and energy recovery and increased double support duration, gait variability, trunk oscillation, and ankle joint muscle co-activation. Narrowing the base of support increased gait instability (e.g., gait variability and trunk oscillations) and induced patients to further use alternative compensatory mechanisms to maintain dynamic balance at the expense of a reduced ability to recover mechanical energy. A widened step width gait is a global strategy employed by patients to increase dynamic stability, reduce the need for further compensatory mechanisms, and thus recover mechanical energy. Our findings suggest that rehabilitative treatment should more specifically focus on step width training.

The main goal of this work was to assess the performance of different initializations of matrix factorization algorithms for an accurate identification of muscle synergies. Currently, nonnegative matrix factorization (NNMF) is the most commonly used method to identify muscle synergies. However, it has been shown that NNMF performance might be affected by different kinds of initialization. The present study aims at optimizing the traditional NNMF initialization for data with partial or complete temporal dependencies. For this purpose, three different initializations are used: random, SVD-based, and sparse. NNMF was used to identify muscle synergies from simulated data as well as from experimental surface EMG signals. Simulated data were generated from synthetic independent and dependent synergy vectors (i.e., shared muscle components), whose activation coefficients were corrupted by simulating controlled degrees of correlation. Similarly, EMG data were artificially modified, making the extracted activation coefficients temporally dependent. By measuring the quality of identification of the original synergies underlying the data, it was possible to compare the performance of different initialization techniques. Simulation results demonstrate that sparse initialization performs significantly better than all other kinds of initialization in reconstructing muscle synergies, regardless of the correlation level in the data.

Purpose: Haralick features Texture analysis is a recent oncologic imaging biomarker used to assess quantitatively the heterogeneity within a tumor. The aim of this study is to evaluate which Haralick's features are the most feasible in predicting tumor response to neoadjuvant chemoradiotherapy (CRT) in colorectal cancer., Materials and Methods: After MRI and histological assessment, eight patients were enrolled and divided into two groups based on response to neoadjuvant CRT in complete responders (CR) and non-responders (NR). Oblique Axial T2-weighted MRI sequences before CRT were analyzed by two radiologists in consensus drawing a ROI around the tumor. 14 over 192 Haralick's features were extrapolated from normalized gray-level co-occurrence matrix in four different directions. A dedicated statistical analysis was performed to evaluate distribution of the extracted Haralick's features computing mean and standard deviation., Results: Pretreatment MRI examination showed significant value (p < 0.05) of 5 over 14 computed Haralick texture. In particular, the significant features are the following: concerning energy, contrast, correlation, entropy and inverse difference moment., Conclusions: Five Haralick's features showed significant relevance in the prediction of response to therapy in colorectal cancer and might be used as additional imaging biomarker in the oncologic management of colorectal patients.

Optical coherence tomography (OCT) is a prevalent, interferometric, high-resolution imaging method with broad biomedical applications. Nonetheless, OCT images suffer from an artifact called speckle, which degrades the image quality. Digital filters offer an opportunity for image improvement in clinical OCT devices, where hardware modification to enhance images is expensive. To reduce speckle, a wide variety of digital filters have been proposed; selecting the most appropriate filter for an OCT image/image set is a challenging decision, especially in dermatology applications of OCT where a different variety of tissues are imaged. To tackle this challenge, we propose an expandable learnable despeckling framework, we call LDF. LDF decides which speckle reduction algorithm is most effective on a given image by learning a figure of merit (FOM) as a single quantitative image assessment measure. LDF is learnable, which means when implemented on an OCT machine, each given image/image set is retrained and its performance is improved. Also, LDF is expandable, meaning that any despeckling algorithm can easily be added to it. The architecture of LDF includes two main parts: (i) an autoencoder neural network and (ii) filter classifier. The autoencoder learns the FOM based on several quality assessment measures obtained from the OCT image including signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, edge preservation index, and mean structural similarity index. Subsequently, the filter classifier identifies the most efficient filter from the following categories: (a) sliding window filters including median, mean, and symmetric nearest neighborhood, (b) adaptive statistical-based filters including Wiener, homomorphic Lee, and Kuwahara, and (c) edge preserved patch or pixel correlation-based filters including nonlocal mean, total variation, and block matching three-dimensional filtering., ((2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).)

Currently, diagnosis of skin diseases is based primarily on the visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography (OCT) has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and, in conjunction with decision-theoretic approaches, used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue.

The aims of the present work were: to calculate lifting energy consumption (LEC) in work activities designed to have a growing lifting index (LI) by means of revised NIOSH lifting equation; to evaluate the relationship between LEC and forces at the L 5 -S 1 joint. The kinematic and kinetic data of 20 workers were recorded during the execution of lifting tasks in three conditions. We computed kinetic, potential and mechanical energy and the corresponding LEC by considering three different centers of mass of: 1) the load (CoM L ); 2) the multi-segment upper body model and load together (CoM Upp+L ); 3) the whole body and load together (CoM Tot ). We also estimated compression and shear forces. Results shows that LEC calculated for CoM Upp+L and CoM Tot grew significantly with the LI and that all the lifting condition pairs are discriminated. The correlation analysis highlighted a relationship between LEC and forces that determine injuries at the L 5 -S 1 joint.

Background: The aim of this study was to investigate the lower limb muscle coactivation and its relationship with muscles spasticity, gait performance, and metabolic cost in patients with hereditary spastic paraparesis., Methods: Kinematic, kinetic, electromyographic and energetic parameters of 23 patients and 23 controls were evaluated by computerized gait analysis system. We computed ankle and knee antagonist muscle coactivation indexes throughout the gait cycle and during the subphases of gait. Energy consumption and energy recovery were measured as well. In addition to the correlation analysis between coactivation indexes and clinical variables, correlations between coactivation indexes and time-distance, kinematic, kinetic, and energetic parameters were estimated., Findings: Increased coactivity indexes of both knee and ankle muscles throughout the gait cycle and during the subphases of gait were observed in patients compared with controls. Energetic parameters were significantly higher in patients than in controls. Both knee and ankle muscle coactivation indexes were positively correlated with knee and ankle spasticity (Ashworth score), respectively. Knee and ankle muscle coactivation indexes were both positively correlated with energy consumption and both negatively correlated with energy recovery., Interpretation: Positive correlations between the Ashworth score and lower limb muscle coactivation suggest that abnormal lower limb muscle coactivation in patients with hereditary spastic paraparesis reflects a primary deficit linked to lower limb spasticity. Furthermore, these abnormalities influence the energetic mechanisms during walking. Identifying excessive muscle coactivation may be helpful in individuating the rehabilitative treatments and designing specific orthosis to restrain spasticity., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Humans generate remarkable quantities of energy while performing daily activities, but this energy usually dissipates into the environment. Here, we address recent progress in the development of nanogenerators (NGs): devices that are able to harvest such body-produced biomechanical and thermal energies by exploiting piezoelectric, triboelectric, and thermoelectric physical effects. In designing NGs, the end-user's comfort is a primary concern. Therefore, we focus on recently developed materials giving flexibility and stretchability to NGs. In addition, we summarize common fabrics for NG design. Finally, the mid-2020s market forecasts for these promising technologies highlight the potential for the commercialization of NGs because they may help contribute to the route of innovation for developing self-powered systems., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

It has been shown that continuous theta burst stimulation (cTBS) over the precuneus acts on specific memory retrieval abilities. In order to study the neural mechanisms beyond these findings, we combined cTBS and resting-state functional magnetic resonance imaging. Our experimental protocol involved stimulation and sham conditions on a group of healthy subjects, and each condition included a baseline and two follow-up acquisitions (5 and 15 min after baseline) after cTBS. We analysed brain functional connectivity by means of graph theoretical measures, with a specific focus on the network modular structure. Our results showed that cTBS of the precuneus selectively affects the left temporal pole, decreasing its functional connectivity in the first follow-up. Moreover, we observed a significant increase in the size of the module of the precuneus in the second follow-up. Such effects were absent in the sham condition. We observed here a modulation of functional connectivity as a result of inhibitory stimulation over the precuneus. Such a modulation first acts indirectly on the temporal area and then extends the connectivity of the precuneus itself by a feed-back mechanism. Our current findings extend our previous behavioural observations and increase our understanding of the mechanisms underlying the stimulation of the precuneus.

Background: The development of postural control across the primary school time horizon is a complex process, which entails biomechanics modifications, the maturation of cognitive ability and sensorimotor organization, and the emergence of anticipatory behaviour. Postural stability in upright stance has been thus object of a multiplicity of studies to better characterize postural control in this age span, with a variety of methodological approaches. The analysis of the Time-to-Boundary function (TtB), which specifies the spatiotemporal proximity of the Centre of Pressure (CoP) to the stability boundaries in the regulation of posture in upright stance, is among the techniques used to better characterize postural stability in adults, but, as of now, it has not yet been introduced in developmental studies. The aim of this study was thus to apply this technique to evaluate the development of postural control in a sample population of primary school children., Methods: In this cross-sectional study, upright stance trials under eyes open and eyes closed were administered to 107 healthy children, divided into three age groups (41 for Seven Years' Group, Y7; 38 for Nine Years' Group, Y9; 28 for Eleven Years' Group, Y11). CoP data were recorded to calculate the Time-to-Boundary function (TtB), from which four spatio-temporal parameters were extracted: the mean value and the standard deviation of TtB minima (M min , Std min ), and the mean value and the standard deviation of the temporal distance between two successive minima (M dist , Std dist )., Results: With eyes closed, M min and Std min significantly decreased and M dist and Std dist increased for the Y7 group, at Y9 M min significantly decreased and Std dist increased, while no effect of vision resulted for Y11. Regarding age groups, M min was significantly higher for Y9 than Y7, and Std min for Y9 was higher than both Y7 and Y11; M dist and Std dist resulted higher for Y11 than for Y9., Conclusion: From the combined results from the spatio-temporal TtB parameters, it is suggested that, at 9 years, children look more efficient in terms of exploring their limits of stability than at 7, and at 11 the observed TtB behaviour hints at the possibility that, at that age, they have almost completed the maturation of postural control in upright stance, also in terms of integration of the spatio-temporal information.

Cycling training is strongly applied in post-stroke rehabilitation, but how its modular control is altered soon after stroke has been not analyzed yet. EMG signals from 9 leg muscles and pedal forces were measured bilaterally during recumbent pedaling in 16 post-acute stroke patients and 12 age-matched healthy controls. Patients were asked to walk over a GaitRite mat and standard gait parameters were computed. Four muscle synergies were extracted through nonnegative matrix factorization in healthy subjects and patients unaffected legs. Two to four synergies were identified in the affected sides and the number of synergies significantly correlated with the Motricity Index (Spearman's coefficient = 0.521). The reduced coordination complexity resulted in a reduced biomechanical performance, with the two-module sub-group showing the lowest work production and mechanical effectiveness in the affected side. These patients also exhibited locomotor impairments (reduced gait speed, asymmetrical stance time, prolonged double support time). Significant correlations were found between cycling-based metrics and gait parameters, suggesting that neuro-mechanical quantities of pedaling can inform on walking dysfunctions. Our findings support the use of pedaling as a rehabilitation method and an assessment tool after stroke, mainly in the early phase, when patients can be unable to perform a safe and active gait training.

Transcranial direct current stimulation (tDCS) is a neuromodulation technique that can alter cortical excitability and modulate behaviour in a polarity-dependent way. Despite the widespread use of this method in the neuroscience field, its effects on ongoing local or global (network level) neuronal activity are still not foreseeable. A way to shed light on the neuronal mechanisms underlying the cortical connectivity changes induced by tDCS is provided by the combination of tDCS with electroencephalography (EEG). In this study, twelve healthy subjects underwent online tDCS-EEG recording (i.e., simultaneous), during resting-state, using 19 EEG channels. The protocol involved anodal, cathodal and sham stimulation conditions, with the active and the reference electrodes in the left frontocentral area (FC3) and on the forehead over the right eyebrow, respectively. The data were processed using a network model, based on graph theory and the synchronization likelihood. The resulting graphs were analysed for four frequency bands (theta, alpha, beta and gamma) to evaluate the presence of tDCS-induced differences in synchronization patterns and graph theory measures. The resting state network connectivity resulted altered during tDCS, in a polarity-specific manner for theta and alpha bands. Anodal tDCS weakened synchronization with respect to the baseline over the fronto-central areas in the left hemisphere, for theta band (p<0.05). In contrast, during cathodal tDCS a significant increase in inter-hemispheric synchronization connectivity was observed over the centro-parietal, centro-occipital and parieto-occipital areas for the alpha band (p<0.05). Local graph measures showed a tDCS-induced polarity-specific differences that regarded modifications of network activities rather than specific region properties. Our results show that applying tDCS during the resting state modulates local synchronization as well as network properties in slow frequency bands, in a polarity-specific manner., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Graph-theoretical approaches have become a popular way to model brain data collected using magnetic resonance imaging (MRI), both from the structural and the functional perspectives. In structural networks, tract-based mapping allows to model different aspects of brain structures by means of the specific characteristics of the different MRI modalities. However, there has been little effort to join the information carried by each modality and to understand what level of common variance is shown in these data. In this paper, we proposed a combined approach based on graph theory and factor analysis to model magnetization transfer and microstructural properties in 18 relapsing remitting multiple sclerosis (RRMS) patients and 17 healthy controls. After defining the common factors and outlining their relationships with MRI data, we evaluated between-group differences using global and local graph measures. The results showed that one common factor describes brain structures in terms of myelin and global integrity, and such factor is able to highlight specific between-group differences.

Background. The main goal of the present study was to explore theta and alpha event-related desynchronization/synchronization (ERD/ERS) activity during shooting performance. We adopted the idiosyncratic framework of the multi-action plan (MAP) model to investigate different processing modes underpinning four types of performance. In particular, we were interested in examining the neural activity associated with optimal-automated (Type 1) and optimal-controlled (Type 2) performances. Methods. Ten elite shooters (6 male and 4 female) with extensive international experience participated in the study. ERD/ERS analysis was used to investigate cortical dynamics during performance. A 4 × 3 (performance types × time) repeated measures analysis of variance was performed to test the differences among the four types of performance during the three seconds preceding the shots for theta, low alpha, and high alpha frequency bands. The dependent variables were the ERD/ERS percentages in each frequency band (i.e., theta, low alpha, high alpha) for each electrode site across the scalp. This analysis was conducted on 120 shots for each participant in three different frequency bands and the individual data were then averaged. Results. We found ERS to be mainly associated with optimal-automatic performance, in agreement with the "neural efficiency hypothesis." We also observed more ERD as related to optimal-controlled performance in conditions of "neural adaptability" and proficient use of cortical resources. Discussion. These findings are congruent with the MAP conceptualization of four performance states, in which unique psychophysiological states underlie distinct performance-related experiences. From an applied point of view, our findings suggest that the MAP model can be used as a framework to develop performance enhancement strategies based on cognitive and neurofeedback techniques.

In this paper, two different piezoelectric transducers-a ceramic piezoelectric, lead zirconate titanate (PZT), and a polymeric piezoelectric, polyvinylidene fluoride (PVDF)-were compared in terms of energy that could be harvested during locomotion activities. The transducers were placed into a tight suit in proximity of the main body joints. Initial testing was performed by placing the transducers on the neck, shoulder, elbow, wrist, hip, knee and ankle; then, five locomotion activities-walking, walking up and down stairs, jogging and running-were chosen for the tests. The values of the power output measured during the five activities were in the range 6 µW-74 µW using both transducers for each joint.

Background: Functional Electrical Stimulation (FES) is increasingly applied in neurorehabilitation. Particularly, the use of electrode arrays may allow for selective muscle recruitment. However, detecting the best electrode configuration constitutes still a challenge., Methods: A multi-contact set-up with thirty electrodes was applied for combined FES and electromyography (EMG) recording of the forearm. A search procedure scanned all electrode configurations by applying single, sub-threshold stimulation pulses while recording M-waves of the extensor digitorum communis (EDC), extensor carpi radialis (ECR) and extensor carpi ulnaris (ECU) muscles. The electrode contacts with the best electrophysiological response were then selected for stimulation with FES bursts while capturing finger/wrist extension and radial/ulnar deviation with a kinematic glove., Results: The stimulation electrodes chosen on the basis of M-waves of the EDC/ECR/ECU muscles were able to effectively elicit the respective finger/wrist movements for the targeted extension and/or deviation with high specificity in two different hand postures., Conclusions: A subset of functionally relevant stimulation electrodes could be selected fast, automatic and non-painful from a multi-contact array on the basis of muscle responses to subthreshold stimulation pulses. The selectivity of muscle recruitment predicted the kinematic pattern. This electrophysiologically driven approach would thus allow for an operator-independent positioning of the electrode array in neurorehabilitation.

α-Motor neurons receive synaptic inputs from spinal and supraspinal centers that comprise components either common to the motor neuron pool or independent. The input shared by motor neurons--common input--determines force control. The aim of the study was to investigate the changes in the strength of common synaptic input delivered to motor neurons with changes in force and with fatigue, two conditions that underlie an increase in the net excitatory drive to the motor neurons. High-density surface electromyogram (EMG) signals were recorded from the tibialis anterior muscle during contractions at 20, 50, and 75% of the maximal voluntary contraction force (in 3 sessions separated by at least 2 days), all sustained until task failure. EMG signal decomposition identified the activity of a total of 1,245 motor units. The coherence values between cumulative motor unit spike trains increased with increasing force, especially for low frequencies. This increase in coherence was not observed when comparing two subsets of motor units having different recruitment thresholds, but detected at the same force level. Moreover, the coherence values for frequencies <5 Hz increased at task failure with respect to the beginning of the contractions for all force levels. In conclusion, the results indicated that the relative strength of common synaptic input to motor neurons increases with respect to independent input when the net excitatory drive to motor neurons increases as a consequence of a change in force and fatigue., (Copyright © 2015 the American Physiological Society.)

Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. For monitoring and fitness applications, it is crucial to develop methods able to segment each activity cycle, e.g., a gait cycle, so that the successive classification step may be more accurate. To increase detection accuracy, pre-processing is often used, with a concurrent increase in computational cost. In this paper, the effect of pre-processing operations on the detection and classification of locomotion activities was investigated, to check whether the presence of pre-processing significantly contributes to an increase in accuracy. The pre-processing stages evaluated in this study were inclination correction and de-noising. Level walking, step ascending, descending and running were monitored by using a shank-mounted inertial sensor. Raw and filtered segments, obtained from a modified version of a rule-based gait detection algorithm optimized for sequential processing, were processed to extract time and frequency-based features for physical activity classification through a support vector machine classifier. The proposed method accurately detected >99% gait cycles from raw data and produced >98% accuracy on these segmented gait cycles. Pre-processing did not substantially increase classification accuracy, thus highlighting the possibility of reducing the amount of pre-processing for real-time applications.

The goal of this preliminary study was to assess the difference in postural stability between blind and sighted children using the Time to Boundary function (TtB). The experiment was conducted in twelve children (6-12 yrs), six of them had no visual impairment, and other six had congenital blindness. The participants stood on RotoBit force plate maintaining upright stance in static conditions. Each blind subject executed the task three times, each sighted subject executed the task six times, three with eyes closed (EC) and three with eyes open (EO). For all subjects each repetition lasted 30 s. The Centre of Pressure (CoP) coordinates, extracted directly from a force plate, are used to calculate four classical parameters (sway path, sway area, mean amplitude and mean frequency) and a predictive variable called Time to Boundary (TtB). The latter is the time it would take the CoP, given its instantaneous trajectory, to contact a stability boundary. Descriptive statistics were calculated for all parameters. Twoway ANOVA test was done considering the visual condition (EO, EC, BLIND) and the repetitions (RP) as a factor. In the first comparison (BLIND/EO) the results showed significant difference for all the parameters except for TtB. In the second comparison (BLIND/EC) the results showed significant difference only for TtB. In the third comparison (EO/EC) the results showed significant difference for all the calculated parameters. Therefore the TtB would be used to asses the postural control in children with blindness.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method that can be used in cognitive and clinical protocols in order to modulate neural activity. Although some macro effects are known, the underlying mechanisms are still not clear. tDCS in combination with electroencephalography (EEG) could help to understand these mechanisms from a neural point of view. However, simultaneous tDCS-EEG still remains challenging because of the artifacts that affect the recorded signals. In this paper, an automated artifact cancellation method based on adaptive filtering is proposed. Using independent component analysis (ICA), the artifacts were characterized using data from both a phantom and a group of healthy subjects. The resulting filter can successfully remove tDCS-related artifacts during anodal and cathodal stimulations.

To check or to prevent failures in ultrasound medical systems, some tests should be scheduled for both clinical suitability and technical functionality evaluation: among them, image quality assurance tests performed by technicians through ultrasound phantoms are widespread today and their results depend on issues related to scanner settings as well as phantom features and operator experience. In the present study variations on some features of the B-mode image were measured when the ultrasound probe is handled by the technician in a routine image quality test: ultrasound phantom images from two array transducers are processed to evaluate measurement dispersion in distance accuracy, high contrast spatial resolution and penetration depth when probe is handled by the operator. All measurements are done by means of an in-house image analysis software that minimizes errors due to operator's visual acuity and subjective judgment while influences of ultrasound transducer position on quality assurance test results are estimated as expanded uncertainties on parameters above (measurement reproducibility at 95 percent confidence level): depending on the probe model, they ranged from ±0.1 to ±1.9 mm in high contrast spatial resolution, from ±0.1 to ±5.5 percent in distance measurements error and from ±1 to ±10 mm in maximum depth of signal visualization. Although numerical results are limited to the two examined probes, they confirm some predictions based on general working principles of diagnostic ultrasound systems: (a) measurements strongly depend on settings as well on phantoms features, probes and parameters investigated; (b) relative uncertainty due to probe manipulation on spatial resolution can be very high, i.e. from 10 to more than 30 percent;, ((c) Field of View settings must be taken into account for measurement reproducibility as well as Dynamic Range compression and phantom attenuation.)

The purpose of this study was to evaluate the effect of a continuous and a discretized Visual Biofeedback (VBF) on balance performance in upright stance. The coordinates of the Centre of Pressure (CoP), extracted from a force plate, were processed in real-time to implement the two VBFs, administered to two groups of 12 healthy participants. In the first group, a representation of the CoP was continuously shown, while in the second group, the discretized VBF was provided at an irregular frequency (that depended on the subject's performance) by displaying one out of a set of five different emoticons, each corresponding to a specific area covered by the current position of the CoP. In the first case, participants were asked to maintain a white spot within a given square area, whereas in the second case they were asked to keep the smiling emoticon on. Trials with no VBF were administered as control. The effect of the two VBFs on balance was studied through classical postural parameters and a subset of stabilogram diffusion coefficients. To quantify the amount of time spent in stable conditions, the percentage of time during which the CoP was inside the stability area was calculated. Both VBFs improved balance maintainance as compared to the absence of any VBF. As compared to the continuous VBF, in the discretized VBF a significant decrease of sway path, diffusion and Hurst coefficients was found. These results seem to indicate that a discretized VBF favours a more natural postural behaviour by promoting a natural intermittent postural control strategy.

Accuracy of systems able to recognize in real time daily living activities heavily depends on the processing step for signal segmentation. So far, windowing approaches are used to segment data and the window size is usually chosen based on previous studies. However, literature is vague on the investigation of its effect on the obtained activity recognition accuracy, if both short and long duration activities are considered. In this work, we present the impact of window size on the recognition of daily living activities, where transitions between different activities are also taken into account. The study was conducted on nine participants who wore a tri-axial accelerometer on their waist and performed some short (sitting, standing, and transitions between activities) and long (walking, stair descending and stair ascending) duration activities. Five different classifiers were tested, and among the different window sizes, it was found that 1.5 s window size represents the best trade-off in recognition among activities, with an obtained accuracy well above 90%. Differences in recognition accuracy for each activity highlight the utility of developing adaptive segmentation criteria, based on the duration of the activities., (Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.)