Present results and methods of vectorcardiographic diagnostics of ischemic heart disease.

This article presents an overview of existing approaches to perform vectorcardiographic (VCG) diagnostics of ischemic heart disease (IHD). Individual methodologies are divided into categories to create a comprehensive and clear overview of electrical cardiac activity measurement, signal pre-processing, features extraction and classification procedures. An emphasis is placed on methods describing the electrical heart space (EHS) by several features extraction techniques based on spatiotemporal characteristics or signal modelling and signal transformations. Performance of individual methodologies are compared depending on classification of extent of ischemia, acute forms - myocardial infarction (MI) and myocardial scars localization. Based on a comparison of imaging methods, the advantages of VCG over the standard 12-leads ECG such as providing a 3D orthogonal leads imaging, better performance, and appropriate computer processing are highlighted. The issues of electrical cardiac activity measurements on body surface, the lack of VKG databases supported by a more accurate imaging method, possibility of comparison with the physiology of individual cases are outlined as potential reserves for future research.

Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review.

This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.

Modalities of sequential human robot collaboration trigger different modifications of trunk oscillations.

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.

Review of present method of glucose from human blood and body fluids assessment.

the work has been aimed to create an overview of available and used methods and ways to determine the concentration of glucose in body fluids, especially from a technical point of view. It also provides an overview of the clinical features of these methods. The survey found that today's market offers a large number of options and approaches to the issue. There are accurate reference laboratory methods, self-monitoring methods for measuring glucose levels using glucometers, or continuous methods for daily monitoring of blood glucose trends and for insulin pump control. However, it must not be forgotten that the development of full closure of feedback is still not complete today. Individual methods cannot always be compared with each other, precisely because of the focus and the use of these methods. Choosing the right method of blood glucose levels in the body measuring can help patients to manage their diabetes mellitus. The methods listed in the overview are divided in terms of measurement continuity and further according to the invasiveness of the method. Finally, the issues of accuracy in the detection of glycaemia variability and the possibility of further development of these methods are discussed, as it is clear from the survey that the development is focused mainly on continuous methods improving that get to the forefront and also on developing a biosensor that is purely non-invasive and continuous.

Progression of muscular co-activation and gait variability in children with Duchenne muscular dystrophy: A 2-year follow-up study.

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.

A New Approach for Testing Fetal Heart Rate Monitors.

In this paper, a new approach for the periodical testing and the functionality evaluation of a fetal heart rate monitor device based on ultrasound principle is proposed. The design and realization of the device are presented, together with the description of its features and functioning tests. In the designed device, a relay element, driven by an electric signal that allows switching at two specific frequencies, is used to simulate the fetus and the mother's heartbeat. The simulator was designed to be compliant with the standard requirements for accurate assessment and measurement of medical devices. The accuracy of the simulated signals was evaluated, and it resulted to be stable and reliable. The generated frequencies show an error of about 0.5% with respect to the nominal one while the accuracy of the test equipment was within ±3% of the test signal set frequency. This value complies with the technical standard for the accuracy of fetal heart rate monitor devices. Moreover, the performed tests and measurements show the correct functionality of the developed simulator. The proposed equipment and testing respect the technical requirements for medical devices. The features of the proposed device make it simple and quick in testing a fetal heart rate monitor, thus providing an efficient way to evaluate and test the correlation capabilities of commercial apparatuses.

Design and Development of a Novel Invasive Blood Pressure Simulator for Patient's Monitor Testing.

This paper presents a newly-designed and realized Invasive Blood Pressure (IBP) device for the simulation on patient's monitors. This device shows improvements and presents extended features with respect to a first prototype presented by the authors and similar systems available in the state-of-the-art. A peculiarity of the presented device is that all implemented features can be customized from the developer and from the point of view of the end user. The realized device has been tested, and its performances in terms of accuracy and of the back-loop measurement of the output for the blood pressure regulation utilization have been described. In particular, an accuracy of ±1 mmHg at 25 °C, on a range from -30 to 300 mmHg, was evaluated under different test conditions. The designed device is an ideal tool for testing IBP modules, for zero setting, and for calibrations. The implemented extended features, like the generation of custom waveforms and the Universal Serial Bus (USB) connectivity, allow use of this device in a wide range of applications, from research to equipment maintenance in clinical environments to educational purposes. Moreover, the presented device represents an innovation, both in terms of technology and methodologies: It allows quick and efficient tests to verify the proper functioning of IBP module of patients' monitors. With this innovative device, tests can be performed directly in the field and faster procedures can be implemented by the clinical maintenance personnel. This device is an open source project and all materials, hardware, and software are fully available for interested developers or researchers.

A Sitting Posture Monitoring Instrument to Assess Different Levels of Cognitive Engagement.

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.

Wearable-based Temporal Parameters of Gait in Circuitous Routes under Dual-Task Conditions.

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.

Thermal Energy Harvesting on the Bodily Surfaces of Arms and Legs through a Wearable Thermo-Electric Generator.

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.

Measurements of Generated Energy/Electrical Quantities from Locomotion Activities Using Piezoelectric Wearable Sensors for Body Motion Energy Harvesting.

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.

Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors.

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 Effect of Continuous and Discretized Presentations of Concurrent Augmented Visual Biofeedback on Postural Control in Quiet Stance.

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.

Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer.

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.

Validity and reliability of an alternative method for measuring power output during six-second all-out cycling.

In a laboratory setting where both a mechanically-braked cycling ergometer and a motion analysis (MA) system are available, flywheel angular displacement can be estimated by using MA. The purpose of this investigation was to assess the validity and reliability of a MA method for measuring maximal power output (Pmax) in comparison with a force transducer (FT) method. Eight males and eight females undertook three identical sessions, separated by 4 to 6 days; the first being a familiarization session. Individuals performed three 6-second sprints against 50% of the maximal resistance to complete two pedal revolutions with a 3-minute rest between trials. Power was determined independently using both MA and FT analyses. Validity: MA recorded significantly higher Pmax than FT (P < .05). Bland-Altman plots showed that there was a systematic bias in the difference between the measures of the two systems. This difference increased as power increased. Repeatability: Intraclass correlation coefficients were on average 0.90 ± 0.05 in males and 0.85 ± 0.08 in females. Measuring Pmax by MA, therefore, is as appropriate for use in exercise physiology research as Pmax measured by FT, provided that a bias between these measurements methods is allowed for.

SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds.

Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon's mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon's mapping on the whole dataset. In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

Inter-individual variability of forces and modular muscle coordination in cycling: a study on untrained subjects.

The aim of this study was to investigate the muscle coordination underlying pedaling in untrained subjects by using the muscle synergies paradigm, and to connect it with the inter-individual variability of EMG patterns and applied forces. Nine subjects performed a pedaling exercise on a cycle-simulator. Applied forces were recorded by means of instrumented pedals able to measure two force components. EMG signals were recorded from eight muscles of the dominant leg, and Nonnegative Matrix Factorization was applied to extract muscle synergy vectors W and time-varying activation coefficients H. Inter-individual variability was assessed for EMG patterns, force profiles, and H. Four modules were sufficient to reconstruct the muscle activation repertoire for all the subjects (variance accounted for >90% for each muscle). These modules were found to be highly similar between subjects in terms of W (mean r=.89), while most of the variability in force profiles and EMG patterns was reflected, in the muscle synergy structure, in the variability of H. These four modules have a functional interpretation when related to force distribution along the pedaling cycle, and the structure of W is shared with that present in human walking, suggesting the existence of a modular motor control in humans.

How to assess performance in cycling: the multivariate nature of influencing factors and related indicators.

Finding an optimum for the cycling performance is not a trivial matter, since the literature shows the presence of many controversial aspects. In order to quantify different levels of performance, several indexes have been defined and used in many studies, reflecting variations in physiological and biomechanical factors. In particular, indexes such as Gross Efficiency (GE), Net Efficiency (NE) and Delta Efficiency (DE) have been referred to changes in metabolic efficiency (EffMet), while the Indexes of Effectiveness (IE), defined over the complete crank revolution or over part of it, have been referred to variations in mechanical effectiveness (EffMech). All these indicators quantify the variations of different factors [i.e., muscle fibers type distribution, pedaling cadence, setup of the bicycle frame, muscular fatigue (MFat), environmental variables, ergogenic aids, psychological traits (PsychTr)], which, moreover, show high mutual correlation. In the attempt of assessing cycling performance, most studies in the literature keep all these factors separated. This may bring to misleading results, leaving unanswered the question of how to improve cycling performance. This work provides an overview on the studies involving indexes and factors usually related to performance monitoring and assessment in cycling. In particular, in order to clarify all those aspects, the mutual interactions among these factors are highlighted, in view of a global performance assessment. Moreover, a proposal is presented advocating for a model-based approach that considers all factors mentioned in the survey, including the mutual interaction effects, for the definition of an objective function E representing the overall effectiveness of a training program in terms of both EffMet and EffMech.

Feedback of mechanical effectiveness induces adaptations in motor modules during cycling.

Recent studies have reported evidence that the motor system may rely on a modular organization, even if this behavior has yet to be confirmed during motor adaptation. The aim of the present study is to investigate the modular motor control mechanisms underlying the execution of pedaling by untrained subjects in different biomechanical conditions. We use the muscle synergies framework to characterize the muscle coordination of 11 subjects pedaling under two different conditions. The first one consists of a pedaling exercise with a strategy freely chosen by the subjects (Preferred Pedaling Technique, PPT), while the second condition constrains the gesture by means of a real time visual feedback of mechanical effectiveness (Effective Pedaling Technique, EPT). Pedal forces, recorded using a pair of instrumented pedals, were used to calculate the Index of Effectiveness (IE). EMG signals were recorded from eight muscles of the dominant leg and Non-negative Matrix Factorization (NMF) was applied for the extraction of muscle synergies. All the synergy vectors, extracted cycle by cycle for each subject, were pooled across subjects and conditions and underwent a 2-dimensional Sammon's non-linear mapping. Seven representative clusters were identified on the Sammon's projection, and the corresponding eight-dimensional synergy vectors were used to reconstruct the repertoire of muscle activation for all subjects and all pedaling conditions (VAF > 0.8 for each individual muscle pattern). Only 5 out of the 7 identified modules were used by the subjects during the PPT pedaling condition, while 2 additional modules were found specific for the pedaling condition EPT. The temporal recruitment of three identified modules was highly correlated with IE. The structure of the identified modules was found similar to that extracted in other studies of human walking, partly confirming the existence of shared and task specific muscle synergies, and providing further evidence on the modularity of the motor system.

Respiration and postural sway: detection of phase synchronizations and interactions.

The aim of the central nervous system in upright stance is to control an intrinsically unstable plant. Internal disturbances, such as haemodynamics and respiration, constitute an endogenous threat to equilibrium. The way CNS reacts to those perturbations was studied in this work, through the analysis of summary scores taken from posturographic and pneumographic data. Signals were recorded simultaneously during trials administered on a sample population of healthy young adults, while sitting and standing and at paced and spontaneous uncontrolled breathing. The extraction of posturographic and pneumographic parameters was accompanied by the utilization of techniques for the detection of phase synchronization in bivariate data, and the extraction of an interaction index, the mutual information MI. The effects of the biomechanical condition and respiratory amplitude on MI and summary measures were tested with a two-way ANOVA. Summary scores clearly depend on posture condition. Synchronization between breath and postural sway is always present, depends on both biomechanical condition and respiratory threat, and cannot be reduced to a simple linear relation.