Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders.

Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joint and related muscles. The diagnosis of TMDs typically involves clinical assessment through operator-based physical examination, a self-reported questionnaire and imaging studies. To objectivize the measurement of TMD, this study aims at investigating the feasibility of using machine-learning algorithms fed with data gathered from low-cost and portable instruments to identify the presence of TMD in adult subjects. Through this aim, the experimental protocol involved fifty participants, equally distributed between TMD and healthy subjects, acting as a control group. The diagnosis of TMD was performed by a skilled operator through the typical clinical scale. Participants underwent a baropodometric analysis by using a pressure matrix and the evaluation of the cervical mobility through inertial sensors. Nine machine-learning algorithms belonging to support vector machine, k-nearest neighbours and decision tree algorithms were compared. The k-nearest neighbours algorithm based on cosine distance was found to be the best performing, achieving performances of 0.94, 0.94 and 0.08 for the accuracy, F1-score and G-index, respectively. These findings open the possibility of using such methodology to support the diagnosis of TMDs in clinical environments.

A machine learning-based classification model to support university students with dyslexia with personalized tools and strategies.

Dyslexia is a specific learning disorder that causes issues related to reading, which affects around 10% of the worldwide population. This can compromise comprehension and memorization skills, and result in anxiety and lack of self-esteem, if no support is provided. Moreover, this support should be highly personalized, to be actually helpful. In this paper, a model to classify the most useful methodologies to support students with dyslexia has been created, with a focus on university alumni. The prediction algorithm is based on supervised machine learning techniques; starting from the issues that dyslexic students experience during their career, it is capable of suggesting customized support digital tools and learning strategies for each of them. The algorithm was trained and tested on data acquired through a self-evaluation questionnaire, which was designed and then spread to more than 1200 university students. It allowed 17 useful tools and 22 useful strategies to be detected. The results of the testing showed an average prediction accuracy higher than 90%, which rises to 94% by renouncing to guess the less-predictable 8 tools/strategies. In the light of this, it is possible to state that the implemented algorithm can achieve the set goal and, thus, reduce the gap between dyslexic and non-dyslexic students. This achievement paves the way for a new modality of facing the problem of dyslexia by university institutions, which aims at modifying teaching activities toward students' needs, instead of simply reducing their study load or duties. This complies with the definition and the aims of inclusivity.

WARNING: A Wearable Inertial-Based Sensor Integrated with a Support Vector Machine Algorithm for the Identification of Faults during Race Walking.

Due to subjectivity in refereeing, the results of race walking are often questioned. To overcome this limitation, artificial-intelligence-based technologies have demonstrated their potential. The paper aims at presenting WARNING, an inertial-based wearable sensor integrated with a support vector machine algorithm to automatically identify race-walking faults. Two WARNING sensors were used to gather the 3D linear acceleration related to the shanks of ten expert race-walkers. Participants were asked to perform a race circuit following three race-walking conditions: legal, illegal with loss-of-contact and illegal with knee-bent. Thirteen machine learning algorithms, belonging to the decision tree, support vector machine and k-nearest neighbor categories, were evaluated. An inter-athlete training procedure was applied. Algorithm performance was evaluated in terms of overall accuracy, F1 score and G-index, as well as by computing the prediction speed. The quadratic support vector was confirmed to be the best-performing classifier, achieving an accuracy above 90% with a prediction speed of 29,000 observations/s when considering data from both shanks. A significant reduction of the performance was assessed when considering only one lower limb side. The outcomes allow us to affirm the potential of WARNING to be used as a referee assistant in race-walking competitions and during training sessions.

Measuring the Immediate Effects of High-Intensity Functional Training on Motor, Cognitive and Physiological Parameters in Well-Trained Adults.

The importance of physical activity has been widely demonstrated both in clinics and in sports. One of the new frontier training programs is high-intensity functional training (HIFT). The immediate effects of HIFT on the psychomotor and cognitive performance of well-trained people are still not clear. This paper aims to evaluate the immediate effects induced by HIFT on blood lactate levels, physical performance in terms of body stability and jump ability, and cognitive performance in terms of reaction time. Nineteen well-trained participants were enrolled in the experimental studies and asked to execute six repetitions of a circuit training. Data were gathered both in a pre-training session and after each one of the circuit repetitions. An immediate significant increase with respect to the baseline was observed during the first repetition, with a further increase after the third one. No effects on jump ability were found, whereas a deterioration in body stability was found. Positive immediate effects on cognitive performance in terms of accuracy and speed in task execution were assessed. The findings can be exploited by trainers during coaching to optimize the design of training programs.

Reliability of wearable sensors-based parameters for the assessment of knee stability.

Anterior cruciate ligament (ACL) rupture represents one of the most recurrent knee injuries in soccer players. To allow a safe return to sport after ACL reconstruction, standardised and reliable procedures/criteria are needed. In this context, wearable sensors are gaining momentum as they allow obtaining objective information during sport-specific and in-the-field tasks. This paper aims at proposing a sensor-based protocol for the assessment of knee stability and at quantifying its reliability. Seventeen soccer players performed a single leg squat and a cross over hop test. Each participant was equipped with two magnetic-inertial measurement units located on the tibia and foot. Parameters related to the knee stability were obtained from linear acceleration and angular velocity signals. The intraclass correlation coefficient (ICC) and minimum detectable change (MDC) were calculated to evaluate each parameter reliability. The ICC ranged from 0.29 to 0.84 according to the considered parameter. Specifically, angular velocity-based parameters proved to be more reliable than acceleration-based counterparts, particularly in the cross over hop test (average ICC values of 0.46 and 0.63 for acceleration- and angular velocity-based parameters, respectively). An exception was represented, in the single leg squat, by parameters extracted from the acceleration trajectory on the tibial transverse plane (0.60≤ICC≤0.76), which can be considered as promising candidates for ACL injury risk assessment. Overall, greater ICC values were found for the dominant limb, with respect to the non-dominant one (average ICC: 0.64 and 0.53, respectively). Interestingly, this between-limb difference in variability was not always mirrored by LSI results. MDC values provide useful information in the perspective of applying the proposed protocol on athletes with ACL reconstruction. Thus, The outcome of this study sets the basis for the definition of reliable and objective criteria for return to sport clearance after ACL injury.

Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults.

There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these are not entirely maintained in older age.

CSA Antisense Targeting Enhances Anticancer Drug Sensitivity in Breast Cancer Cells, including the Triple-Negative Subtype.

Breast cancer (BC) is the most common cancer with the highest frequency of death among women. BC is highly heterogenic at the genetic, biological, and clinical level. Despite the significant improvements in diagnosis and treatments of BC, the high rate of cancer recurrence and resistance to treatment remains a major challenge in clinical practice. This issue is particularly relevant in Triple-Negative Breast Cancer (TNBC) subtype, for which chemotherapy remains the main standard therapeutic approach. Here, we observed that BC cells, belonging to different subtypes, including the TNBC, display an increased expression of Cockayne Syndrome group A (CSA) protein, which is involved in multiple functions such as DNA repair, transcription, mitochondrial homeostasis, and cell division and that recently was found to confer cell robustness when it is up-regulated. We demonstrated that CSA ablation by AntiSense Oligonucleotides (ASOs) drastically impairs tumorigenicity of BC cells by hampering their survival and proliferative capabilities without damaging normal cells. Moreover, suppression of CSA dramatically sensitizes BC cells to platinum and taxane derivatives, which are commonly used for BC first-line therapy, even at very low doses not harmful to normal cells. Finally, CSA ablation restores drug sensitivity in oxaliplatin-resistant cells. Based on these results, we conclude that CSA might be a very attractive target for the development of more effective anticancer therapies.

Measuring Kinematic Response to Perturbed Locomotion in Young Adults.

Daily life activities often require humans to perform locomotion in challenging scenarios. In this context, this study aimed at investigating the effects induced by anterior-posterior (AP) and medio-lateral (ML) perturbations on walking. Through this aim, the experimental protocol involved 12 participants who performed three tasks on a treadmill consisting of one unperturbed and two perturbed walking tests. Inertial measurement units were used to gather lower limb kinematics. Parameters related to joint angles, as the range of motion (ROM) and its variability (CoV), as well as the inter-joint coordination in terms of continuous relative phase (CRP) were computed. The AP perturbation seemed to be more challenging causing differences with respect to normal walking in both the variability of the ROM and the CRP amplitude and variability. As ML, only the ankle showed different behavior in terms of joint angle and CRP variability. In both tasks, a shortening of the stance was found. The findings should be considered when implementing perturbed rehabilitative protocols for falling reduction.

Preventing and Monitoring Work-Related Diseases in Firefighters: A Literature Review on Sensor-Based Systems and Future Perspectives in Robotic Devices.

In recent years, the necessity to prevent work-related diseases has led to the use of sensor-based systems to measure important features during working activities. This topic achieved great popularity especially in hazardous and demanding activities such as those required of firefighters. Among feasible sensor systems, wearable sensors revealed their advantages in terms of possibility to conduct measures in real conditions and without influencing the movements of workers. In addition, the advent of robotics can be also exploited in order to reduce work-related disorders. The present literature review aims at providing an overview of sensor-based systems used to monitor physiological and physical parameters in firefighters during real activities, as well as to offer ideas for understanding the potentialities of exoskeletons and assistive devices.

Sensor-Based Indices for the Prediction and Monitoring of Anterior Cruciate Ligament Injury: Reliability Analysis and a Case Study in Basketball.

The possibility of measuring predictive factors to discriminate athletes at higher risk of anterior cruciate ligament (ACL) injury still represents an open research question. We performed an observational study with thirteen female basketball players who performed monopodalic jumps and single-leg squat tests. One of them suffered from an ACL injury after the first test session. Data gathered from twelve participants, who did not suffer from ACL injury, were used for a reliability analysis. Parameters related to leg stability, load absorption capability and leg mobility showed good-to-excellent reliability. Path length, root mean square of the acceleration and leg angle with respect to the vertical axis revealed themselves as possible predictive factors to identify athletes at higher risk. Results confirm that six months after reconstruction represents the correct time for these athletes to return to playing. Furthermore, the training of leg mobility and load absorption capability could allow athletes to reduce the probability of new injuries.

A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players.

Anterior cruciate ligament (ACL) injury represents one of the main disorders affecting players, especially in contact sports. Even though several approaches based on artificial intelligence have been developed to allow the quantification of ACL injury risk, their applicability in training sessions compared with the clinical scale is still an open question. We proposed a machine-learning approach to accomplish this purpose. Thirty-nine female basketball players were enrolled in the study. Leg stability, leg mobility and capability to absorb the load after jump were evaluated through inertial sensors and optoelectronic bars. The risk level of athletes was computed by the Landing Error Score System (LESS). A comparative analysis among nine classifiers was performed by assessing the accuracy, F1-score and goodness. Five out nine examined classifiers reached optimum performance, with the linear support vector machine achieving an accuracy and F1-score of 96 and 95%, respectively. The feature importance was computed, allowing us to promote the ellipse area, parameters related to the load absorption and the leg mobility as the most useful features for the prediction of anterior cruciate ligament injury risk. In addition, the ellipse area showed a strong correlation with the LESS score. The results open the possibility to use such a methodology for predicting ACL injury.

Reliability and Repeatability Analysis of Indices to Measure Gait Deterioration in MS Patients during Prolonged Walking.

Gait deterioration caused by prolonged walking represents one of the main consequences of multiple sclerosis (MS). This study aims at proposing quantitative indices to measure the gait deterioration effects. The experimental protocol consisted in a 6-min walking test and it involved nine patients with MS and twenty-six healthy subjects. Pathology severity was assessed through the Expanded Disability Status Scale. Seven inertial units were used to gather lower limb kinematics. Gait variability and asymmetry were assessed by coefficient of variation (CoV) and symmetry index (SI), respectively. The evolution of ROM (range of motion), CoV, and SI was computed analyzing data divided into six 60-s subgroups. Maximum difference among subgroups and the difference between the first minute and the remaining five were computed. The indices were analyzed for intra- and inter-day reliability and repeatability. Correlation with clinical scores was also evaluated. Good to excellent reliability was found for all indices. The computed standard deviations allowed us to affirm the good repeatability of the indices. The outcomes suggested walking-related fatigue leads to an always more variable kinematics in MS, in terms of changes in ROM, increase of variability and asymmetry. The hip asymmetry strongly correlated with the clinical disability.

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview.

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes' performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes' performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment.

Assessing the Effects of Kata and Kumite Techniques on Physical Performance in Elite Karatekas.

This study aimed at assessing physical performance of elite karatekas and non-karatekas. More specifically, effects of kumite and kata technique on joint mobility, body stability, and jumping ability were assessed by enrolling twenty-four karatekas and by comparing the results with 18 non-karatekas healthy subjects. Sensor system was composed by a single inertial sensor and optical bars. Karatekas are generally characterized by better motor performance with respect nonkaratekas, considering all the examined factors, i.e., mobility, stability, and jumping. In addition, the two techniques lead to a differentiation in joint mobility; in particular, kumite athletes are characterized by a greater shoulder extension and, in general, by a greater value of preferred velocity to perform joint movements. Conversely, kata athletes are characterized by a greater mobility of the ankle joint. By focusing on jumping skills, kata technique leads to an increase of the concentric phase when performing squat jump. Finally, kata athletes showed better stability inclosed eyes condition. The outcomes reported here can be useful for optimizing coaching programs for both beginners and karatekas based on the specific selected technique.

On the OCRA Measurement: Automatic Computation of the Dynamic Technical Action Frequency Factor.

OCRA (OCcupational Repetitive Action) is currently one of the most widespread procedures for assessing biomechanical risks related to upper limb repetitive movements. Frequency factor of the technical actions represents one of the OCRA elements. Actually, the frequency factor computation is based on workcycle video analysis, which is time-consuming and may lead to up to 30% of intra-operator variability. This paper aims at proposing an innovative procedure for the automatic counting of dynamic technical actions on the basis of inertial data. More specifically, a threshold-based algorithm was tested in four industrial case studies, involving a cohort of 20 workers. Nine combinations of the algorithm were tested by varying threshold values related to time and amplitude. The computation of frequency factor showed an average relative error lower than 5.7% in all industrial-based case studies after the appropriate selection of the time and amplitude threshold values. These findings open the possibility to use the threshold-based algorithm proposed here for the automatic computation of OCRA frequency factor, avoiding the time efforts in video analysis.

Reactive Postural Responses to Continuous Yaw Perturbations in Healthy Humans: The Effect of Aging.

Maintaining balance stability while turning in a quasi-static stance and/or in dynamic motion requires proper recovery mechanisms to manage sudden center-of-mass displacement. Furthermore, falls during turning are among the main concerns of community-dwelling elderly population. This study investigates the effect of aging on reactive postural responses to continuous yaw perturbations on a cohort of 10 young adults (mean age 28 ± 3 years old) and 10 older adults (mean age 61 ± 4 years old). Subjects underwent external continuous yaw perturbations provided by the RotoBit1D platform. Different conditions of visual feedback (eyes opened and eyes closed) and perturbation intensity, i.e., sinusoidal rotations on the horizontal plane at different frequencies (0.2 Hz and 0.3 Hz), were applied. Kinematics of axial body segments was gathered using three inertial measurement units. In order to measure reactive postural responses, we measured body-absolute and joint absolute rotations, center-of-mass displacement, body sway, and inter-joint coordination. Older adults showed significant reduction in horizontal rotations of body segments and joints, as well as in center-of-mass displacement. Furthermore, older adults manifested a greater variability in reactive postural responses than younger adults. The abnormal reactive postural responses observed in older adults might contribute to the well-known age-related difficulty in dealing with balance control during turning.

Automatic Detection of Faults in Race Walking: A Comparative Analysis of Machine-Learning Algorithms Fed with Inertial Sensor Data.

The validity of results in race walking is often questioned due to subjective decisions in the detection of faults. This study aims to compare machine-learning algorithms fed with data gathered from inertial sensors placed on lower-limb segments to define the best-performing classifiers for the automatic detection of illegal steps. Eight race walkers were enrolled and linear accelerations and angular velocities related to pelvis, thighs, shanks, and feet were acquired by seven inertial sensors. The experimental protocol consisted of two repetitions of three laps of 250 m, one performed with regular race walking, one with loss-of-contact faults, and one with knee-bent faults. The performance of 108 classifiers was evaluated in terms of accuracy, recall, precision, F1-score, and goodness index. Generally, linear accelerations revealed themselves as more characteristic with respect to the angular velocities. Among classifiers, those based on the support vector machine (SVM) were the most accurate. In particular, the quadratic SVM fed with shank linear accelerations was the best-performing classifier, with an F1-score and a goodness index equal to 0.89 and 0.11, respectively. The results open the possibility of using a wearable device for automatic detection of faults in race walking competition.

Feasibility of Muscle Synergy Outcomes in Clinics, Robotics, and Sports: A Systematic Review.

In the last years, several studies have been focused on understanding how the central nervous system controls muscles to perform a specific motor task. Although it still remains an open question, muscle synergies have come to be an appealing theory to explain the modular organization of the central nervous system. Even though the neural encoding of muscle synergies remains controversial, a large number of papers demonstrated that muscle synergies are robust across different tested conditions, which are within a day, between days, within a single subject, and between subjects that have similar demographic characteristics. Thus, muscle synergy theory has been largely used in several research fields, such as clinics, robotics, and sports. The present systematical review aims at providing an overview on the applications of muscle synergy theory in clinics, robotics, and sports; in particular, the review is focused on the papers that provide tangible information for (i) diagnosis or pathology assessment in clinics, (ii) robot-control design in robotics, and (iii) athletes' performance assessment or training guidelines in sports.

On the Reliability and Repeatability of Surface Electromyography Factorization by Muscle Synergies in Daily Life Activities.

Muscle synergy theory is a new appealing approach for different research fields. This study is aimed at evaluating the robustness of EMG reconstruction via muscle synergies and the repeatability of muscle synergy parameters as potential neurophysiological indices. Eight healthy subjects performed walking, stepping, running, and ascending and descending stairs' trials for five repetitions in three sessions. Twelve muscles of the dominant leg were analyzed. The "nonnegative matrix factorization" and "variability account for" were used to extract muscle synergies and to assess EMG goodness reconstruction, respectively. Intraclass correlation was used to quantify methodology reliability. Cosine similarity and coefficient of determination assessed the repeatability of the muscle synergy vectors and the temporal activity patterns, respectively. A 4-synergy model was selected for EMG signal factorization. Intraclass correlation was excellent for the overall reconstruction, while it ranged from fair to excellent for single muscles. The EMG reconstruction was found repeatable across sessions and subjects. Considering the selection of neurophysiological indices, the number of synergies was not repeatable neither within nor between subjects. Conversely, the cosine similarity and coefficient of determination values allow considering the muscle synergy vectors and the temporal activity patterns as potential neurophysiological indices due to their similarity both within and between subjects. More specifically, some synergies in the 4-synergy model reveal themselves as more repeatable than others, suggesting focusing on them when seeking at the neurophysiological index identification.