Noninvasive brain stimulation (NIBS) techniques can be used to monitor and modulate the excitability of intracortical neuronal circuits. Long periods of cortical stimulation can produce lasting effects on brain function, paving the way for therapeutic applications of NIBS in chronic neurological disease. The potential of NIBS in stroke rehabilitation has been of particular interest, because stroke is the main cause of permanent disability in industrial nations, and treatment outcomes often fail to meet the expectations of patients. Despite promising reports from many clinical trials on NIBS for stroke recovery, the number of studies reporting a null effect remains a concern. One possible explanation is that the interhemispheric competition model--which posits that suppressing the excitability of the hemisphere not affected by stroke will enhance recovery by reducing interhemispheric inhibition of the stroke hemisphere, and forms the rationale for many studies--is oversimplified or even incorrect. Here, we critically review the proposed mechanisms of synaptic and functional reorganization after stroke, and suggest a bimodal balance-recovery model that links interhemispheric balancing and functional recovery to the structural reserve spared by the lesion. The proposed model could enable NIBS to be tailored to the needs of individual patients.

Because wrist rotation dynamics are dominated by stiffness (Charles SK, Hogan N. J Biomech 44: 614 -621, 2011), understanding how humans plan and execute coordinated wrist rotations requires knowledge of the stiffness characteristics of the wrist joint. In the past, the passive stiffness of the wrist joint has been measured in 1 degree of freedom (DOF). Although these 1-DOF measurements inform us of the dynamics the neuromuscular system must overcome to rotate the wrist in pure flexion-extension (FE) or pure radial-ulnar deviation (RUD), the wrist rarely rotates in pure FE or RUD. Instead, understanding natural wrist rotations requires knowledge of wrist stiffness in combinations of FE and RUD. The purpose of this report is to present measurements of passive wrist stiffness throughout the space spanned by FE and RUD. Using a rehabilitation robot designed for the wrist and forearm, we measured the passive stiffness of the wrist joint in 10 subjects in FE, RUD, and combinations. For comparison, we measured the passive stiffness of the forearm (in pronation-supination), as well. Our measurements in pure FE and RUD agreed well with previous 1-DOF measurements. We have linearized the 2-DOF stiffness measurements and present them in the form of stiffness ellipses and as stiffness matrices useful for modeling wrist rotation dynamics. We found that passive wrist stiffness was anisotropic, with greater stiffness in RUD than in FE. We also found that passive wrist stiffness did not align with the anatomical axes of the wrist; the major and minor axes of the stiffness ellipse were rotated with respect to the FE and RUD axes by ϳ20°. The direction of least stiffness was between ulnar flexion and radial extension, a direction used in many natural movements (known as the "dart-thrower's motion"), suggesting that the nervous system may take advantage of the direction of least stiffness for common wrist rotations. wrist stiffness; muscle tone; neuromuscular; motor control; spasticity GRACEFUL MOVEMENT REQUIRES that the neuromuscular system compensate for unwanted dynamics of the body. For example, it is well known that simple reaching movements require complex torques to compensate for the coupled inertial dynamics of the arm (Hollerbach and Flash 1982). Decades of research have shown that the compensation of unwanted limb dynamics is accomplished through a combination of feedback control (through muscles, reflex loops, and adaptation) and predictive control, and much effort has focused specifically on how the brain compensates for the coupled inertial dynamics of the arm during reaching movements (Bastian et al. 1996;Bastian 2006).Recent work has shown that the dynamics of wrist rotations are quite different from reaching movements: wrist rotations are dominated by stiffness, not inertia (Charles and Hogan 2011), suggesting that a major part of wrist control involves compensating for the stiffness of the wrist joint. Several studies have measured wrist stiffness in flexion and/or extension (Axelson and Hagbarth 2001;Cornu et a...

During last decades, Magnetic Resonance (MR)—compatible sensors based on different techniques have been developed due to growing demand for application in medicine. There are several technological solutions to design MR-compatible sensors, among them, the one based on optical fibers presents several attractive features. The high elasticity and small size allow designing miniaturized fiber optic sensors (FOS) with metrological characteristics (e.g., accuracy, sensitivity, zero drift, and frequency response) adequate for most common medical applications; the immunity from electromagnetic interference and the absence of electrical connection to the patient make FOS suitable to be used in high electromagnetic field and intrinsically safer than conventional technologies. These two features further heightened the potential role of FOS in medicine making them especially attractive for application in MRI. This paper provides an overview of MR-compatible FOS, focusing on the sensors employed for measuring physical parameters in medicine (i.e., temperature, force, torque, strain, and position). The working principles of the most promising FOS are reviewed in terms of their relevant advantages and disadvantages, together with their applications in medicine.

Small, compact and embedded sensors are a pervasive technology in everyday life for a wide number of applications (e.g., wearable devices, domotics, e-health systems, etc.). In this context, wireless transmission plays a key role, and among available solutions, Bluetooth Low Energy (BLE) is gaining more and more popularity. BLE merges together good performance, low-energy consumption and widespread diffusion. The aim of this work is to review the main methodologies adopted to investigate BLE performance. The first part of this review is an in-depth description of the protocol, highlighting the main characteristics and implementation details. The second part reviews the state of the art on BLE characteristics and performance. In particular, we analyze throughput, maximum number of connectable sensors, power consumption, latency and maximum reachable range, with the aim to identify what are the current limits of BLE technology. The main results can be resumed as follows: throughput may theoretically reach the limit of ~230 kbps, but actual applications analyzed in this review show throughputs limited to ~100 kbps; the maximum reachable range is strictly dependent on the radio power, and it goes up to a few tens of meters; the maximum number of nodes in the network depends on connection parameters, on the network architecture and specific device characteristics, but it is usually lower than 10; power consumption and latency are largely modeled and analyzed and are strictly dependent on a huge number of parameters. Most of these characteristics are based on analytical models, but there is a need for rigorous experimental evaluations to understand the actual limits.

BackgroundIn the last decades, several studies showed that wearable sensors, used for assessing Parkinson’s disease (PD) motor symptoms and recording their fluctuations, could provide a quantitative and reliable tool for patient’s motor performance monitoring.ObjectiveThe aim of this study is to make a step forward the capability of quantitatively describing PD motor symptoms. The specific aims are: identify the most sensible place where to locate sensors to monitor PD bradykinesia and rigidity, and identify objective indexes able to discriminate PD OFF/ON motor status, and PD patients from healthy subjects (HSs).MethodsFourteen PD patients (H&Y stage 1–2.5), and 13 age-matched HSs, were enrolled. Five magneto-inertial wearable sensors, placed on index finger, thumb, metacarpus, wrist, and arm, were used as motion tracking systems. Sensors were placed on the most affected arm of PD patients, and on dominant hand of HS. Three UPDRS part III tasks were evaluated: rigidity (task 22), finger tapping (task 23), and prono-supination movements of the hands (task 25). A movement disorders expert rated the three tasks according to the UPDRS part III scoring system. In order to describe each task, different kinematic indexes from sensors were extracted and analyzed.ResultsFour kinematic indexes were extracted: fatigability; total time; total power; smoothness. The last three well-described PD OFF/ON motor status, during finger-tapping task, with an index finger sensor. During prono-supination task, wrist sensor was able to differentiate PD OFF/ON motor condition. Smoothness index, used as a rigidity descriptor, provided a good discrimination of the PD OFF/ON motor status. Total power index, showed the best accuracy for PD vs healthy discrimination, with any sensor location among index finger, thumb, metacarpus, and wrist.ConclusionThe present study shows that, in order to better describe the kinematic features of Parkinsonian movements, wearable sensors should be placed on a distal location on upper limb, on index finger or wrist. The proposed indexes demonstrated a good correlation with clinical scores, thus providing a quantitative tool for research purposes in future studies in this field.