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

Thank you very much for downloading biomechanics and motor control of human movement. Maybe you have knowledge that, people have search hundreds times for their favorite books like this biomechanics and motor control of human movement, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious virus inside their laptop.

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

The aim of this work was to investigate the use Principal Component Analysis (PCA) applied to signals recorded by 4 textile piezoresistive sensors embedded within a smart garment (SG) to estimate respiratory rate (RR) during walking/running tasks. To this aim, we enrolled 6 subjects who were asked to perform 7 trials on a treadmill at different speeds: one static condition (quiet breathing at rest), three walking trials at 1.6 km/h, 3.0 km/h and 5.0 km/h, and three running trials at 8.0 km/h, 10.0 km/h and 12.0 km/h. We recorded breathing activity using both the SG embedding 4 piezoresistive sensors, located on lower thorax and abdomen, and a reference flowmeter. We estimated RR using three different signals from SG: i) the average along sensors of the band-pass filtered piezoresistive signals (Xbp), the first principal component (P1st) and the average along components of the sub-set of principal components needed to obtain an accounted variance of 0.95 (P95). On the basis of the RR computed with the reference flowmeter, we obtained that, on average, the error committed relying on P95 is 1.02 bpm, the error obtained considering Xbp is 1.04 bpm and the error obtained using P1st is 1.13 bpm. However, the use of P95 allows obtaining a better estimation at high speed tasks than both Xbp and P1st. This finding may suggest that the use of PCA and, specifically, of the signal obtained as the average along all those components required to obtain an accounted variance of 0.95 may be conveniently used to selectively discard breathing-unrelated components, thus improving the estimation of RR.

Respiratory Rate Estimation During Walking/Running Activities Using Principal Components Estimated from Signals Recorded by a Smart Garment Embedding Piezoresistive Sensors

The role of gravity and inertial forces on shoulder during pointing task in children with hemiplegia

Limb viscoelasticity is a critical factor used to regulate the interaction with the environment. It plays a key role in modelling human sensorimotor control, and can be used to assess the condition of healthy and neurologically affected individuals. This paper reports the estimation of hip joint viscoelasticity during voluntary force control using a novel device that applies a leg displacement without constraining the hip joint. The influence of hip angle, applied limb force and perturbation direction on the stiffness values was studied in ten subjects. No difference was detected in the hip joint stiffness between the dominant and non-dominant legs, but a small dependency was observed on the perturbation direction. Both hip stiffness and viscosity increased monotonically with the applied force magnitude, with posture to being observed to have a slight influence. These results are in line with previous measurements carried out on upper limbs, and can be used as a baseline for lower limb movement simulation and further neuromechanical investigations.

The influence of posture, applied force and perturbation direction on hip joint viscoelasticity.

Archery is a static sport that requires high postural stability during shooting since every tiniest movement can negatively compromise the archer's performance. One of the main crucial activities responsible for involuntary movements is the heart beating. However, keeping low HR is not always implementable especially during biggest tournaments. Innovative technology such as wearables can serve as an important opportunity to quantitatively measure HR during practice and competition without limiting the archer movements. In the literature, wearable inertial units (IMUs) have been proposed for monitoring HR but their rigidness and low body compliance limit the use in the field. Soft sensors can overcome this issue by improving the adherence to the skin through the sensor encapsulation into flexible substrates. Soft sensors based on Fiber Bragg gratings (FBGs) are reaching growing interest for HR monitoring due to their superior properties (e.g., miniaturized size, high sensitivity to strain, good frequency response and capability of working in harsh environments) over conventional methods. This study exploits the advantages of an FBG-based soft for HR monitoring in archery. Firstly, the fabrication and the metrological characterization of the soft sensor was described. Then, a preliminary assessment was carried out to investigate the sensor performance in HR monitoring. The promising results together with the possibility of automatically identifying the shooting session on the FBG signal will be useful for driving personalized training strategies according to the way in which the athlete responses to stressful conditions during both practice and official tournaments.

A soft sensor based on FBG technology for heart rate monitoring in archery

Abstract Pointing at a screen using wrist and forearm movements is a kinematically redundant task, and the Central Nervous System seems to manage this redundancy by using a simplifying strategy, named Donders’ Law for the wrist. In this work we investigated (1) whether this simplifying approach is stable over time and (2) whether a visuomotor perturbation provided in the task space influences the strategy used to solve the redundancy problem. We conducted two experiments asking participants to perform the same pointing task in four different days (first experiment), and providing a visual perturbation, i.e. a visuomotor rotation to the controlled cursor (second experiment), while recording their wrist and forearm rotations. Results showed that the participant-specific wrist redundancy management (described by the Donders’ surfaces) (1) neither changes over time (2) nor varies when a visuomotor perturbation is provided in the task space. 

https://www.nature.com/articles/s41598-023-33531-2.pdf

Domenico Formica

Papers

Respiratory Rate Estimation During Walking/Running Activities Using Principal Components Estimated from Signals Recorded by a Smart Garment Embedding Piezoresistive Sensors

The role of gravity and inertial forces on shoulder during pointing task in children with hemiplegia

The influence of posture, applied force and perturbation direction on hip joint viscoelasticity.

A soft sensor based on FBG technology for heart rate monitoring in archery

Wrist redundancy management during pointing tasks remains stable over time and in presence of a visuomotor perturbation