Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

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Biomechanics And Motor Control Of Human Movement

The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS)

The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic.

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Diverse Applications of Nanomedicine

Wearable stretchable sensors are gaining significant interest in application related to joint motion monitoring. Stretchable strain sensors are a promising technological solution to develop wearable systems for several applications (e.g., sports, rehabilitation, health science) thanks to their flexibility, lightweight, compliance, and ease to wear. The aim of this study is twofold: i) to perform the metrological characterization of a stretchable sensing element based on a conductive textile fabric for developing a wearable sensor intended for joint motion monitoring and ii) to provide a preliminary assessment of the developed sensor in shoulder motion monitoring. The static characterization and the hysteresis analysis at different speeds (i.e., 50 mm.min−1, 100 mm.min−1, 200 mm.min−1, 400 mm.min−1and 600 mm.min−1) were carried out to assess the sensor's characteristics to mechanical strain. An explorative test to assess the sensor feasibility in monitoring horizontal flexion-extension of the shoulder was performed on one healthy volunteer. Results showed that the sensor's resistance decreased from $75 k \Omega$ at 0% strain level to $52 k \Omega$ at 10% strain level during static characterization. Moreover, the maximum hysteresis error $e_{H}\%$ was always lower than 2.59% during loading-unloading hysteresis cycles at different speeds. The developed stretchable sensor seems to be a promising solution for monitoring shoulder range of motion in applications were unobtrusiveness and wearability are key factors.

Wearable stretchable sensor based on conductive textile fabric for shoulder motion monitoring

Comfortable and easy to wear smart textiles have gained popularity for continuous respiratory monitoring. Among different emerging technologies, smart textiles based on fiber optic sensors (FOSs) have several advantages, like Magnetic Resonance (MR)-compatibility and good metrological properties. In this paper we report on the development and assessment of an MR-compatible smart textiles based on FOSs for respiratory monitoring. The system consists of six fiber Bragg grating (FBG) sensors glued on the textile to monitor six compartments of the chest wall (i.e., right and left upper thorax, right and left abdominal rib cage, and right and left abdomen). This solution allows monitoring both global respiratory parameters and each compartment volume change. The system converts thoracic movements into strain measured by the FBGs. The positioning of the FBGs was optimized by experiments performed using an optoelectronic system. The feasibility of the smart textile was assessed on 6 healthy volunteers. Experimental data were compared to the ones estimated by an optoelectronic plethysmography used as reference. Promising results were obtained on both breathing period (maximum percentage error is 1.14%), inspiratory and expiratory period, as well as on total volume change (mean percentage difference between the two systems was ∼14%). The Bland-Altman analysis shows a satisfactory accuracy for the parameters under investigation. The proposed system is safe and non-invasive, MR-compatible, and allows monitoring compartmental volumes.

Design and preliminary assessment of a smart textile for respiratory monitoring based on an array of Fiber Bragg Gratings

In this paper we propose and validate a teleoperated control approach for an anthropomorphic redundant robotic manipulator, using magneto-inertial sensors (IMUs). The proposed method allows mapping the motion of the human arm (used as the master) on the robot end-effector (the slave). We record arm movements using IMU sensors, and calculate human forward kinematics to be mapped on robot movements. In order to solve robot kinematic redundancy, we implemented different algorithms for inverse kinematics that allows imposing anthropomorphism criteria on robot movements. The main objective is to let the user to control the robotic platform in an easy and intuitive manner by providing the control input freely moving his/her own arm and exploiting redundancy and anthropomorphism criteria in order to achieve humanlike behaviour on the robot arm. Therefore, three inverse kinematics algorithms are implemented: Damped Least Squares (DLS), Elastic Potential (EP) and Augmented Jacobian (AJ). In order to evaluate the performance of the algorithms, four healthy subjects have been asked to control the motion of an anthropomorphic robot arm (i.e. the Kuka Light Weight Robot 4+) through four magneto-inertial sensors (i.e. Xsens Wireless Motion Tracking sensors — MTw) positioned on their arm. Anthropomorphism indices and position and orientation errors between the human hand pose and the robot end-effector pose were evaluated to assess the performance of our approach.

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A teleoperated control approach for anthropomorphic manipulator using magneto-inertial sensors

Facial somatosensory feedback is critical for breastfeeding in the first days of life. However, its development has never been investigated in humans. Here we develop a new interface to measure facial somatosensation in newborn infants. The novel system allows to measure neuronal responses to touching the face of the subject by synchronously recording scalp electroencephalography (EEG) and the force applied by the experimenter. This is based on a dedicated force transducer that can be worn on the finger underneath a clinical nitrile glove and linked to a commercial EEG acquisition system. The calibrated device measures the pressure applied by the investigator when tapping the skin concurrently with the resulting brain response. With this system, we were able to demonstrate that taps of 192 mN (mean) reliably elicited facial somatosensory responses in 7 pre-term infants. These responses had a time course similar to those following limbs stimulation, but more lateral topographical distribution consistent with body representations in primary somatosensory areas. The method introduced can therefore be used to reliably measure facial somatosensory responses in vulnerable infants.

A novel sensor design for accurate measurement of facial somatosensation in pre-term infants

A novel system to provide the user with proprioceptive feedback from a supernumerary robotic limb through vibrotactile stimulation is presented. The system is composed of a dedicated electronic board that converts the robot state, acquired through serial communication, into a vibration amplitude-frequency value and commands up to sixteen eccentric motors, placed on the user’s leg skin. The system is designed to allow the subject to have cues on the robot state even without visual feedback. Two different feedback encoding paradigms, one based on a kinematic approach and the other on a dynamic one, have been tested in three healthy subjects to validate the platform. In a planar task and with slow robot motions, conveying the robot state through the cartesian position (kinematic method) allows to achieve hugely better (d=4.278) performance than using joint torques (dynamic approach) or non-informative feedback.

Domenico Formica

Papers

Wearable stretchable sensor based on conductive textile fabric for shoulder motion monitoring

Design and preliminary assessment of a smart textile for respiratory monitoring based on an array of Fiber Bragg Gratings

A teleoperated control approach for anthropomorphic manipulator using magneto-inertial sensors

A novel sensor design for accurate measurement of facial somatosensation in pre-term infants

A Novel Proprioceptive Feedback System for Supernumerary Robotic Limb