A novel approach to estimate the upper limb reaching movement in three-dimensional space

Background: In spite of the complexity that the number of redundancy levels suggests, humans show amazingly regularities when generating movement. When moving the hand between pairs of targets, subjects tended to generate roughly straight hand trajectories with single-peaked, bell-shaped speed profiles. The original minimum-jerk model, in which limb displacement is represented by a fifth order polynomial, has been shown to predict qualitative features of experimental trajectories recorded in monkeys performing intermediate speed one-joint elbow movements to a target. However, it is difficult to compare a real (experimentally measured) movement to its equivalent minimum-jerk trajectory (MJT) because the exact start and end times and positions of real movements are usually not well defined: even discrete movements usually exhibit an extended period of low (but non-zero) velocity and acceleration before and after a movement, making estimation of the exact start and end times inaccurate. Aim: The purpose of this study was to describe a method used for correctly fitting the minimum jerk trajectory to real movement data assuming that the minimum-jerk trajectory satisfies the same threshold condition as the real movement (the same position and the same percentage of maximum velocity), rather than the movements start and end at full rest. Thus, the original minimum-jerk model was revised. Materials and methods: Starting from the original minimum-jerk model, in this work is proposed a method used for correctly fitting the minimum jerk trajectory to real movement data defined by a threshold condition. This method enables users to accurately compare a minimum-jerk trajectory to real movements. The latter were recorded using APDM inertial sensors. To estimate if the ideal model fits adequately the real reaching movements we consider three kinematic indexes. Results: and Discussion: A total of 100 upper arm straight line reaching movements executed by healthy subjects were acquired. MJTs follow closely to the reaching movements when they have been computed considering the revised model. On the contrary, the MJTs do not follow the real profiles when considering the original formulation. This behaviour is confirmed when we consider the three kinematic indexes. These findings help us better understand important characteristics of movements in health. Future works will focus on the investigation of the performance of the upper arm straight line reaching movements in a larger healthy subjects sample and then in pathological conditions. Keywords: Reaching movements, Minimum jerk model, Reaching movements, Rehabilitatio

A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks

Lifting is one of the most potentially harmful activities for work-related musculoskeletal disorders (WMSDs), due to exposure to biomechanical risk. Risk assessment for work activities that involve lifting loads can be performed through the NIOSH (National Institute of Occupational Safety and Health) method, and specifically the Revised NIOSH Lifting Equation (RNLE). Aim of this work is to explore the feasibility of a logistic regression model fed with time and frequency domains features extracted from signals acquired through one inertial measurement unit (IMU) to classify risk classes associated with lifting activities according to the RNLE. Furthermore, an attempt was made to evaluate which are the most discriminating features relating to the risk classes, and to understand which inertial signals and which axis were the most representative. In a simplified scenario, where only two RNLE variables were altered during lifting tasks performed by 14 healthy adults, inertial signals (linear acceleration and angular velocity) acquired using one IMU placed on the subject's sternum during repeated rhythmic lifting tasks were automatically segmented to extract several features in the time and frequency domains. The logistic regression model fed with significant features showed good results to discriminate "risk" and "no risk" NIOSH classes with an accuracy, sensitivity and specificity equal to 82.8%, 84.8% and 80.9%, respectively. This preliminary work indicated that a logistic regression model-fed with specific inertial features extracted by signals acquired using a single IMU sensor placed on the sternum-is able to discriminate risk classes according to the RNLE in a simplified context, and therefore could be a valid tool to assess the biomechanical risk in an automatic way also in more complex conditions (e.g., real working scenarios)

A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition

Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control

Design and validation of an e-textile-based wearable system for remote health monitoring

The paper presents a new e-textile-based system, named SWEET Shirt, for the remote monitoring of biomedical signals. The system includes a textile sensing shirt, an electronic unit for data transmission, a custom-made Android application for real-time signal visualisation and a software desktop for advanced digital signal processing. The device allows for the acquisition of electrocardiographic, bicep electromyographic and trunk acceleration signals. The sensors, electrodes, and bus structures are all integrated within the textile garment, without any discomfort for users. A wide-ranging set of algorithms for signal processing were also developed for use within the system, allowing clinicians to rapidly obtain a complete and schematic overview of a patient's clinical status. The aim of this work was to present the design and development of the device and to provide a validation analysis of the electrocardiographic measurement and digital processing. The results demonstrate that the information contained in the signals recorded by the novel system is comparable to that obtained via a standard medical device commonly used in clinical environments. Similarly encouraging results were obtained in the comparison of the variables derived from the signal processing.</p

A case of pulmonary hyperinflation in chronic heart failure: role of diuretic therapy and cardiorespiratory rehabilitation

Persistent dyspnoea during daily activities is commonly observed in patient with chronic heart failure (CHF) despite optimised pharmacological therapy. In CHF patients diuretics are essential for symptomatic treatment when fluid overloads with consequent pulmonary congestion or peripheral oedema. Appropriate use of diuretics is key element versus other drugs used for the success of the treatment of HF. Conversely, the inappropriate use of high doses of diuretics can cause adverse effects as electrolyte and fluid depletion, hypotension and hyperazotemia. Dyspnoea and fatigue, in patients with stable HF, are not related only to fluid overload and/or fluid retention but likely other mechanisms are linked to symptoms increase. These patients at the end of a rehabilitative treatment take less diuretic doses than during the period before the rehabilitative treatment, so reducing the principal adverse effects and improving the symptoms. In these patients in absence of venous congestion but in presence of an increase of symptoms augmenting diuretic drugs is not useful: it is very useful, instead, to undergo these patients a rehabilitative treatment because other mechanisms are linked to symptoms increase. In fact, in our report the predominant mechanism determining the increase in dyspnoea is likely related to an increase in physiological dead space

Predictors of Response to Hydroxyurea and Switch to Ruxolitinib in HU-Resistant Polycythaemia VERA Patients: A Real-World PV-NET Study

In polycythemia vera (PV), the prognostic relevance of an ELN-defined complete response (CR) to hydroxyurea (HU), the predictors of response, and patients' triggers for switching to ruxolitinib are uncertain. In a real-world analysis, we evaluated the predictors of response, their impact on the clinical outcomes of CR to HU, and the correlations between partial or no response (PR/NR) and a patient switching to ruxolitinib. Among 563 PV patients receiving HU for ≥12 months, 166 (29.5%) achieved CR, 264 achieved PR, and 133 achieved NR. In a multivariate analysis, the absence of splenomegaly (p = 0.03), pruritus (p = 0.002), and a median HU dose of ≥1 g/day (p &lt; 0.001) remained associated with CR. Adverse events were more frequent with a median HU dose of ≥1 g/day. Overall, 283 PR/NR patients (71.3%) continued HU, and 114 switched to ruxolitinib. In the 449 patients receiving only HU, rates of thrombosis, hemorrhages, progression, and overall survival were comparable among the CR, PR, and NR groups. Many PV patients received underdosed HU, leading to lower CR and toxicity rates. In addition, many patients continued HU despite a PR/NR; however, splenomegaly and other symptoms were the main drivers of an early switch. Better HU management, standardization of the criteria for and timing of responses to HU, and adequate intervention in poor responders should be advised

Prognostic decision support using symbolic dynamics in CTG monitoring

Foetal heart rate variability is one of the most important parameters to monitor foetal wellbeing. Linear parameters, widely employed to study foetal heart variability, have shown some limitations in highlight dynamics potentially relevant. During the last decades, therefore, nonlinear analysis methods have gained a growing interest to analyze the chaotic nature of cardiac activity. Parameters derived by techniques investigating nonlinear can be included in computerised systems of cardiotocographic monitoring. In this work, we described an application of symbolic dynamics to analyze foetal heart rate variability in healthy foetuses and a concise index, introduced for its classification in antepartum CTG monitoring. The introduced index demonstrated to be capable to highlight differences in heart rate variability and resulted correlated with the Apgar score at birth, in particular, higher variability indexes values are associated to early greater vitality at birth. These preliminary results confirm that SD can be a helpful tool in CTG monitoring, supporting medical decisions in order to assure the maximum well-being of newborn

An application of symbolic dynamics for FHRV assessment

Fetal heart rate variability is surely one of the most important parameters to monitor fetal wellbeing. Linear studies, widely employed to study fetal heart variability and its correlations with the development of the autonomous nervous system, have shown some limitations in highlight dynamics potentially relevant. During the last decades, therefore, nonlinear analysis methods have gained a growing interest to analyze the chaotic nature of cardiac activity. Techniques investigating nonlinear dynamics have been already successfully employed in adults, to analyze different physiological and pathological states. Concerning fetal monitoring, instead, a smaller number of papers is available in the literature; even if symbolic dynamics was recently employed to quantify fetal heart rate regularity, demonstrating that the use of this technique may lead to a better and more differentiated understanding of normal fetal physiological development. In this work, we applied the symbolic dynamics to analyze fetal heart rate variability in healthy fetuses at the end of a physiological pregnancy. Our results confirmed the potentiality of the technique to highlight differences between signals characterized by more or less variabilit