Motion analysis in sport training: the link between technology and pedagogy

Sport is an increasingly popular phenomenon among people probably due to the parallel evolution of the methods of development of physiological, technical and strategic capacities. People who play sports have learned to pay more and more attention to the loads they put on their bodies. This is because it is know that excessive loads during workouts can increase the risk of injuries. As the benefits of sport activity manifest themselves in many fields like in disability, in the presence of clinical pathologies, for recovery prison and especially in schools, it cannot be considered as simple gymnastics, since it involves physical, psychological, and cultural aspects and for these reasons we now increasingly speak of sport pedagogy. Many definitions have been proposed for the word training but all of them are almost always incomplete. This because training is to be understood as a complex pedagogical process in which various factors come into play such as, for example, motor, physical, technical, tactical but above all psychological, neurobiological and social factors. The aim of training is to describe, quantify and evaluate human movement. The analysis of human movement provide information about different aspects of a specific motor task (such as walking, jumping and running), through measuring instruments like cameras or sensors. These allow to obtain quantitative and qualitative descriptions of the observed sport gesture. The purpose of this review is to analyse how the motion analysis, through its different technologies, can help in the description and characterization of sport and training intended as pedagogical processes

Brain Networks and Cognitive Impairment in Parkinson's Disease

: Aim: The aim of the present study is to investigate the relationship between both functional connectivity and brain networks with cognitive decline, in patients with Parkinson's disease (PD). Introduction: PD phenotype is not limited to motor impairment but, rather, a wide range of non-motor disturbances can occur, with cognitive impairment being one of the most common. However, how the large-scale organization of brain activity differs in cognitively impaired patients, as opposed to cognitively preserved ones, remains poorly understood. Methods: Starting from source-reconstructed resting-state magnetoencephalography data, we applied the phase linearity measurement (PLM) to estimate functional connectivity, globally and between brain areas, in PD patients with and without cognitive impairment (respectively PD-CI and PD-NC), as compared with healthy subjects (HS). Further, using graph analysis, we characterized the alterations in brain network topology and related these, as well as the functional connectivity, to cognitive performance. Results: We found reduced global and nodal PLM in several temporal (fusiform gyrus, Heschl's gyrus, and inferior temporal gyrus), parietal (postcentral gyrus), and occipital (lingual gyrus) areas within the left hemisphere, in the gamma band, in PD-CI patients, as compared with PD-NC and HS. With regard to the global topological features, PD-CI patients, as compared with HS and PD-NC patients, showed differences in multi-frequencies bands (delta, alpha, gamma) in the Leaf fraction, Tree hierarchy (Th) (both higher in PD-CI), and Diameter (lower in PD-CI). Finally, we found statistically significant correlations between the Montreal Cognitive Assessment test and both the Diameter in delta band and the Th in the alpha band. Conclusion: Our work points to specific large-scale rearrangements that occur selectively in cognitively compromised PD patients and are correlated to cognitive impairment. Impact statement In this article, we want to test the hypothesis that the cognitive decline observed in Parkinson's disease (PD) patients may be related to specific changes of both functional connectivity and brain network topology. Specifically, starting from magnetoencephalography signals and by applying the phase linearity measurement (PLM), a connectivity metric that measures the synchronization between brain regions, we were able to highlight differences in the global and nodal PLM values in PD patients with cognitive impairment as compared with both cognitively unimpaired patients and healthy subjects. Further, using graph analysis, we analyzed alterations in brain network topology that were related to cognitive functioning

Amnestic Mild Cognitive Impairment Is Associated With Frequency-Specific Brain Network Alterations in Temporal Poles

There is general agreement that the neuropathological processes leading to Alzheimer’s disease (AD) begin decades before the clinical onset. In order to detect early topological changes, we applied functional connectivity and network analysis to magnetoencephalographic (MEG) data obtained from 16 patients with amnestic Mild Cognitive Impairment (aMCI), a prodromal stage of AD, and 16 matched healthy control (HCs). Significant differences between the two groups were found in the theta band, which is associated with memory processes, in both temporal poles (TPs). In aMCI, the degree and betweenness centrality (BC) were lower in the left superior TP, whereas in the right middle TP the BC was higher. A statistically significant negative linear correlation was found between the BC of the left superior TP and a delayed recall score, a sensitive marker of the “hippocampal memory” deficit in early AD. Our results suggest that the TPs, which are involved early in AD pathology and belong to the memory circuitry, have an altered role in the functional network in aMCI

A night of sleep deprivation alters brain connectivity and affects specific executive functions

: Sleep is a fundamental physiological process necessary for efficient cognitive functioning especially in relation to memory consolidation and executive functions, such as attentional and switching abilities. The lack of sleep strongly alters the connectivity of some resting-state networks, such as default mode network and attentional network. In this study, by means of magnetoencephalography (MEG) and specific cognitive tasks, we investigated how brain topology and cognitive functioning are affected by 24 h of sleep deprivation (SD). Thirty-two young men underwent resting-state MEG recording and evaluated in letter cancellation task (LCT) and task switching (TS) before and after SD. Results showed a worsening in the accuracy and speed of execution in the LCT and a reduction of reaction times in the TS, evidencing thus a worsening of attentional but not of switching abilities. Moreover, we observed that 24 h of SD induced large-scale rearrangements in the functional network. These findings evidence that 24 h of SD is able to alter brain connectivity and selectively affects cognitive domains which are under the control of different brain networks

Correction: Rucco, R.; et al. Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors 2018, 18, 1613

The authors wish to make a correction to their paper [1]. The following Table 1 should be replaced with the table shown below it[...

Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review

In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms of number of events and the consequent decrease in the quality of life. Stability control is a key approach for studying the genesis of falls, for detecting the event and trying to develop methodologies to prevent it. Wearable sensors have proved to be very useful in monitoring and analyzing the stability of subjects. Within this manuscript, a review of the approaches proposed in the literature for fall risk assessment, fall prevention and fall detection in healthy elderly is provided. The review has been carried out by using the most adopted publication databases and by defining a search strategy based on keywords and boolean algebra constructs. The analysis aims at evaluating the state of the art of such kind of monitoring, both in terms of most adopted sensor technologies and of their location on the human body. The review has been extended to both dynamic and static analyses. In order to provide a useful tool for researchers involved in this field, the manuscript also focuses on the tests conducted in the analyzed studies, mainly in terms of characteristics of the population involved and of the tasks used. Finally, the main trends related to sensor typology, sensor location and tasks have been identified

A new technical method to analyse the kinematics of the human movements and sports gesture

To improve the performance of an athlete or to identify an incorrect postures that can cause injuries, it is essential to study the kinematics of the movements. Normally,are used approaches that “photograph” the execution of the gesture and look at the angular opening of the joint of interest, ignoring its dynamics. These types of analyses have major limitations. In fact, they do not allow to observe the execution of a movement on all three anatomical planes at the same time and also provide mostly qualitative information. In fact, in order to obtain three-dimensional data, it is necessary to have three cameras that record the same execution of the gesture from different points of view (one for each anatomical plane), or a single camera that at each gesture execution is positioned with respect to a different anatomical plane.Furthermore, analysing the movement without his dynamic information limits its study because all the information related to the chained execution of kinematic, kinetic and muscular activities are lost. To overcome these limits and to obtain more complete and accurate information, the aim of this study is to propose a new method for analysing the kinematics of a sports gesture, which takes into account the all range of motion of a joint. In fact, it calculates the angular excursions between identifying events of the gesture, thus evaluating the angular variation of the joint between peaks of flexion and extension. This approach is more informative, compared to the classic space-time analyses, and therefore can be considered useful in the study of the kinematics of a sports gesture

An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas

International audienceAbstract Background Brain areas need to coordinate their activity in order to enable complex behavioral responses. Synchronization is one of the mechanisms neural ensembles use to communicate. While synchronization between signals operating at similar frequencies is fairly straightforward, the estimation of synchronization occurring between different frequencies of oscillations has proven harder to capture. One specifically hard challenge is to estimate cross-frequency synchronization between broadband signals when no a priori hypothesis is available about the frequencies involved in the synchronization. Methods In the present manuscript, we expand upon the phase linearity measurement, an iso-frequency synchronization metrics previously developed by our group, in order to provide a conceptually similar approach able to detect the presence of cross-frequency synchronization between any components of the analyzed broadband signals. Results The methodology has been tested on both synthetic and real data. We first exploited Gaussian process realizations in order to explore the properties of our new metrics in a synthetic case study. Subsequently, we analyze real source-reconstructed data acquired by a magnetoencephalographic system from healthy controls in a clinical setting to study the performance of our metrics in a realistic environment. Conclusions In the present paper we provide an evolution of the PLM methodology able to reveal the presence of cross-frequency synchronization between broadband data

A brain connectivity metric based on phase linearity measurement

The analysis of brain connectivity is gaining interest in recent years due to the relevant information it carries about the functioning of the brain in health and in disease. In brief, it consists in measuring the statistical dependencies between signals generated by different brain regions. Several metrics have been proposed in literature, related to three families: amplitude based, phase based on jointly amplitude and phase based. Due to the large amount of noise that typically affects the estimation of the connectivity maps, averaging over several epochs of a population is normally carried out. We propose a novel phase based metric, namely the Phase Linearity Metric (PLM), that is resilient to noise and volume conduction, bearing promise to lower the number of epochs needed for a reliable measurement. The comparison with the widely adopted PLI connectivity metric confirms the effectiveness of the PLM

Phase Linearity Measurement: a novel index for brain functional connectivity

The problem of describing how different brain areas interact between each other has been granted a great deal of attention in the last years. The idea that neuronal ensembles behave as oscillators and that they communicate through synchronization is now widely accepted. To this regard, EEG and MEG provide the signals that allow the estimation of such communication in vivo. Hence, phase-based metrics are essential. However, the application of phased-based metrics for measuring brain connectivity has proved problematic so far, since they appear to be less resilient to noise as compared to amplitude-based ones. In this paper, we address the problem of designing a purely phase-based brain connectivity metric, insensitive to volume conduction and resilient to noise. The proposed metric, named Phase Linearity Measurement (PLM), is based on the analysis of similar behaviors in the phases of the recorded signals. The PLM is tested in two simulated datasets as well as in real MEG data acquired at the Naples MEG center. Due to its intrinsic characteristics, the PLM shows considerable noise rejection properties as compared to other widely adopted connectivity metrics. We conclude that the PLM might be valuable in order to allow better estimation of phasebased brain connectivity