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The widespread and pervasive use of smartphones for sending messages, calling, and entertainment purposes, mainly among young adults, is often accompanied by the concurrent execution of other tasks. Recent studies have analyzed how texting, reading or calling while walking–in some specific conditions–might significantly influence gait parameters. The aim of this study is to examine the effect of different smartphone activities on walking, evaluating the variations of several gait parameters. 10 young healthy students (all smartphone proficient users) were instructed to text chat (with two different levels of cognitive load), call, surf on a social network or play with a math game while walking in a real-life outdoor setting. Each of these activities is characterized by a different cognitive load. Using an inertial measurement unit on the lower trunk, spatio-temporal gait parameters, together with regularity, symmetry and smoothness parameters, were extracted and grouped for comparison among normal walking and different dual task demands. An overall significant effect of task type on the aforementioned parameters group was observed. The alterations in gait parameters vary as a function of cognitive effort. In particular, stride frequency, step length and gait speed show a decrement, while step time increases as a function of cognitive effort. Smoothness, regularity and symmetry parameters are significantly altered for specific dual task conditions, mainly along the mediolateral direction. These results may lead to a better understanding of the possible risks related to walking and concurrent smartphone use.

The aim of this study is to introduce a new platform, called En Plein, for the kinesthetic practice of phonological skills by preschool children and to examine its feasibility in combination with more traditional teaching methods. The rationale is that the manipulation of structural phonological units is important to train the necessary prerequisites for writing and reading and to help the identification of early learning disability precursors. The system includes a large number of phonological activities for children and allows interaction with a playful virtual environment via a cartoonavatar controlled by a gesture-based natural user interface. En Plein relies on the Microsoft Kinect TM motion sensor. In the pilot study, the system has been placed in a classroom of an Italian kindergarten for 5 weeks of training. A test for assessing the phonological skills in Italian language for kindergarten (Valutazione delle Competenze Metafonologiche [CMF]) has been administered before and after training. Children who worked with the platform showed improvements in their phonological

Background: Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an ad hoc markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.

Background: In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented.

"Sport practice can take advantage from the quantitative assessment of task execution, which is strictly connected to the implementation of optimized training procedures. To this aim, it is interesting to explore the effectiveness of biofeedback training techniques. This implies a complete chain for information extraction containing instrumented devices, processing algorithms and graphical user interfaces (GUIs) to extract valuable information (i.e. kinematics, dynamics, and electrophysiology) to be presented in real-time to the athlete. In cycling, performance indexes displayed in a simple and perceivable way can help the cyclist optimize the pedaling. To this purpose, in this study four different GUIs have been designed and used in order to understand if and how a graphical biofeedback can influence the cycling performance. In particular, information related to the mechanical efficiency of pedaling is represented in each of the designed interfaces and then displayed to the user. This index is real-time calculated on the basis of the force signals exerted on the pedals during cycling. Instrumented pedals for bikes, already designed and implemented in our laboratory, have been used to measure those force components. A group of subjects underwent an experimental protocol and pedaled with (the interfaces have been used in a randomized order) and without graphical biofeedback. Preliminary results show how the effective perception of the biofeedback influences the motor performance.

Piezoelectric transducers can be used to both harvest biomechanical energy and to detect gait cycle events. Several designing factors influence the efficiency of the energy harvesting system, such as the location of the transducers, their mechanical/electrical parameters and the correct matching of the load resistor. In this research, a piezoelectric polyvinylidene fluoride (PVDF) film-LDT4-028k-is simultaneously used as energy harvester for recovering energy associated with human walking, and as an active sensor to detect stance and swing phases of human gait. The PVDF transducer was placed on the back of the knee through an elastic cotton leotard, which allowed obtaining an output power of 1.45 μW during walking. Moreover, we presented a patterns' comparison between the signals of the PVDF transducer with a gyroscope. By using the output signal of the PVDF transducer we were able to distinguish the two phases of gait cycle with a difference in time of about 15 ms as compared to the events estimated through a gyroscope placed on the shank