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Quality assessment of ultrasound medical systems is a demanding task due to the high number of parameters to quantify their performance: in the present study, a Kiviat diagram-based integrated approach was proposed to effectively combine the contribution of some experimental parameters and quantify the overall performance of pulsed wave Doppler (PWD) systems for clinical applications. Four test parameters were defined and assessed through custom-written measurement methods based on image analysis, implemented in the MATLAB environment, and applied to spectral images of a flow phantom, i.e., average maximum velocity sensitivity (AMVS), velocity measurements accuracy (VeMeA), lowest detectable signal (LDS), and the velocity profile discrepancy index (VPDI). The parameters above were scaled in a standard range to represent the four vertices of a Kiviat plot, whose area was considered the overall quality index of the ultrasound system in PWD mode. Five brand-new ultrasound diagnostic systems, equipped with linear array probes, were tested in two different working conditions using a commercial flow phantom as a reference. The promising results confirm the robustness of AMVS, VeMeA, and LDS parameters while suggesting further investigations on the VPDI.

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.

IntroductionHuman 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.MethodsTrunk 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 discussionThe 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.

Nowadays, in both scientific and medical communities, Pulse Wave Velocity (PWV) is commonly considered as a predicting factor of cardiovascular diseases. Such issue leads to the necessity of accurate PWV measurements as PWV non-local assessment is currently affected by low accuracy distance measurements. Despite the great interest on such predictor, a reference standard for PWV assessment is still awaited. In sight of the development of a reference standard able to reproduce PWV variations, the necessity arises of a mathematical model involving the main physical quantities influencing PWV. Therefore, the present study aims at giving a contribution in the development of such model as well as in the implementation of numerical simulations for the design of a sensorized PWV-varying arterial simulator as regards its metrological and physical characteristics. Despite the promising results, further developments could include a greater system parametrization, a system miniaturization as well as the investigation of other metrological characteristics.

Color Doppler (CD) is one of the most used ultrasound (US) Doppler techniques as it allows the 2D representation of blood flow. Even if a clarification on whether such technique provides qualitative or quantitative diagnostic information on velocity assessment is still awaited, the necessity of periodic Quality Control (QC) tests is ongoing. Consequently, in the present preliminary study, a previously proposed sensitivity parameter, namely the Average Maximum Velocity Sensitivity (AMVS), has been further investigated for Color Doppler QC. Three diagnostic US systems, equipped with phased array probes, have been used to collect Color Doppler images. Two Doppler phantom flow rate regimes and two US system settings have been employed during data acquisition. Despite the limitations encountered, the AMVS results are promising, therefore further studies should be carried out on CD clips, on a higher number of US systems and probes as well as on a greater number of phantom settings.

Smartphone texting while walking is a very common activity among people of different ages, with the so-called “digital natives” being the category most used to interacting with an electronic device during daily activities, mostly for texting purposes. Previous studies have shown how the concurrency of a smartphone-related task and walking can result in a worsening of stability and an increased risk of injuries for adults; an investigation of whether this effect can be identified also in people of a younger age can improve our understanding of the risks associated with this common activity. In this study, we recruited 29 young adolescents (12 ± 1 years) to test whether walking with a smartphone increases fall and injuries risk, and to quantify this effect. To do so, participants were asked to walk along a walkway, with and without the concurrent writing task on a smartphone; several different parameters linked to stability and risk of fall measures were then calculated from an inertial measurement unit and compared between conditions. Smartphone use determined a reduction of spatio-temporal parameters, including step length (from 0.64 ± 0.08 to 0.55 ± 0.06 m) and gait speed (1.23 ± 0.16 to 0.90 ± 0.16 m/s), and a general worsening of selected indicators of gait stability. This was found to be mostly independent from experience or frequency of use, suggesting that the presence of smartphone activities while walking may determine an increased risk of injury or falls also for a population that grew up being used to this concurrency.

We propose an event-based dynamic segmentation technique for the classification of locomotion activities, able to detect the mid-swing, initial contact and end contact events. This technique is based on the use of a shank-mounted inertial sensor incorporating a tri-axial accelerometer and a tri-axial gyroscope, and it is tested on four different locomotion activities: walking, stair ascent, stair descent and running. Gyroscope data along one component are used to dynamically determine the window size for segmentation, and a number of features are then extracted from these segments. The event-based segmentation technique has been compared against three different fixed window size segmentations, in terms of classification accuracy on two different datasets, and with two different feature sets. The dynamic event-based segmentation showed an improvement in terms of accuracy of around 5% (97% vs. 92% and 92% vs. 87%) and 1-2% (89% vs. 87% and 97% vs. 96%) for the two dataset, respectively, thus confirming the need to incorporate an event-based criterion to increase performance in the classification of motion activities.

Work-related low-back disorders (WLBDs) can be caused by manual lifting tasks. Wearable devices used to monitor these tasks can be one possible way to assess the main risk factors for WLBDs. This study aims at analyzing the sensitivity of kinematic data to the risk level changes, and to define an instrument-based tool for risk classification by using kinematic data and artificial neural networks (ANNs). Twenty workers performed lifting tasks, designed by following the rules of the revised NIOSH lifting equation, with an increasing lifting index (LI). From the acquired kinematic data, we computed smoothness parameters together with kinetic, potential and mechanical energy. We used ANNs for mapping different set of features on LI levels to obtain an automatic risk estimation during these tasks. The results show that most of the calculated kinematic indexes are significantly affected by changes in LI and that all the lifting condition pairs can be correctly distinguished. Furthermore, using specific set of features, different topologies of ANNs can lead to a reliable classification of the biomechanical risk related to lifting tasks. In particular, the training sets and numbers of neurons in each hidden layer influence the ANNs performance, which is instead independent from the numbers of hidden layers. Reliable biomechanical risk estimation can be obtained by using training sets combining body and load kinematic features.

During a non-invasive procedure, participants both helped and helped by a confederate with features that create social distance (membership in an ethnic outgroup or another social group). For this purpose, we created a set of virtual scenarios in which the confederate’s ethnicity (white vs. black) and appearance (business man vs. beggar, with casual dress as a control condition) were crossed. The study aimed to explore how the emotional reactions of participants changed according to the confederate’s status signals as well as signals that they belong to the same or a different ethnic group. Participants’ alertness, calmness, and engagement were monitored using electroencephalogram (EEG) during the original virtual reality (VR) video sessions. Participants’ distress and empathy when exposed to helping interactions were self-assessed after the VR video sessions. The results pointed out that, irrespective of whether they helped the confederate or were helped by him/her, white participants showed higher levels of alertness when exposed to helping interactions involving a white beggar or a black businessman, and their emotional calmness and engagement were higher when interacting with a black beggar or a white businessman. The results for self-assessed distress and empathy followed the same tendency, indicating how physiological and self-assessed measures can both contribute to a better understanding of the emotional processes in virtual intergroup helping situations. Based on the presented results, the methodological and practical implications of VR in terms of enhancing self-reflective capacities in intergroup helping processes are discussed.

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 corrects the article DOI: 10.1371/journal.pone.0185825.].

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 widespread and pervasive use of smartphones for sending messages, calling, and entertainment purposes, mainly among young adults, is often accompanied by the concurrent execution of other tasks. Recent studies have analyzed how texting, reading or calling while walking-in some specific conditions-might significantly influence gait parameters. The aim of this study is to examine the effect of different smartphone activities on walking, evaluating the variations of several gait parameters. 10 young healthy students (all smartphone proficient users) were instructed to text chat (with two different levels of cognitive load), call, surf on a social network or play with a math game while walking in a real-life outdoor setting. Each of these activities is characterized by a different cognitive load. Using an inertial measurement unit on the lower trunk, spatio-temporal gait parameters, together with regularity, symmetry and smoothness parameters, were extracted and grouped for comparison among normal walking and different dual task demands. An overall significant effect of task type on the aforementioned parameters group was observed. The alterations in gait parameters vary as a function of cognitive effort. In particular, stride frequency, step length and gait speed show a decrement, while step time increases as a function of cognitive effort. Smoothness, regularity and symmetry parameters are significantly altered for specific dual task conditions, mainly along the mediolateral direction. These results may lead to a better understanding of the possible risks related to walking and concurrent smartphone use.

This paper documents the research towards the development of a system based on Artificial Neural Networks to predict muscle force patterns of an athlete during cycling. Two independent inverse problems must be solved for the force estimation: evaluation of the kinematic model and evaluation of the forces distribution along the limb. By solving repeatedly the two inverse problems for different subjects and conditions, a training pattern for an Artificial Neural Network was created. Then, the trained network was validated against an independent validation set, and compared to evaluate agreement between the two alternative approaches using Bland-Altman method. The obtained neural network for the different test patterns yields a normalized error well below 1% and the Bland-Altman plot shows a considerable correlation between the two methods. The new approach proposed herein allows a direct and fast computation for the inverse dynamics of a cyclist, opening the possibility of integrating such algorithm in a real time environment such as an embedded application.

The accurate and reliable extraction of specific gait events from a single inertial sensor at waist level has been shown to be challenging. Among several techniques, a wavelet-based method for initial contact (IC) and final contact (FC) estimation was shown to be the most accurate in healthy subjects. In this study, we evaluated the sensitivity of events detection to the wavelet scale of the algorithm, when walking at different speeds, in order to optimize its selection. A single inertial sensor recorded the lumbar vertical acceleration of 20 subjects walking at three different self-selected speeds (slow, normal, and fast) in a motion analysis lab. The scale of the wavelet method was varied. ICs were generally accurately detected in a wide range of wavelet scales under all the walking speeds. FCs detection proved highly sensitive to scale choice. Different gait speeds required the selection of a different scale for accurate detection and timing, with the optimal scale being strongly correlated with subjects’ step frequency. The best speed-dependent scales of the algorithm led to highly accurate timing in the detection of IC (RMSE < 22 ms) and FC (RMSE < 25 ms) across all speeds. Our results pave the way for the optimal adaptive selection of scales in future applications using this algorithm.

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.

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 μW. 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 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.

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.

Modelling has recently been exploited for gaining knowledge on the mechanisms of motor control. Computational models, simulating the behaviour of complex systems, have often been used in combination with soft computing strategies, thus shifting the rationale of the modelling from the description of a behaviour to the understanding of the mechanisms behind it. In this context, computational models are preferred to deterministic schemes because they deal better with complex systems. The literature offers some biological inspired models, which perform better than traditional approaches when dealing with both learning and adaptivity mechanisms. Can these theoretical studies be transferred into an application framework? That is, can biologically inspired models be used to implement rehabilitative devices? Some evidences, even if preliminary, are presented here, and allow an affirmative answer to the previous question, thus opening new perspectives.