Abstract: A novel approach to view-based eye gaze tracking for human computer interface (HCI) is presented. The proposed method combines different techniques to address the problems of head motion, illumination and usability in the framework of low cost applications. Feature detection and tracking algorithms have been designed to obtain an automatic setup and strengthen the robustness to light conditions. An extensive analysis of neural solutions has been performed to deal with the non-linearity associated with gaze mapping under free-head conditions. No specific hardware, such as infrared illumination or high-resolution cameras, is needed, rather a simple commercial webcam working in visible light spectrum suffices. The system is able to classify the gaze direction of the user over a 15-zone graphical interface, with a success rate of 95% and a global accuracy of around 2 degrees , comparable with the vast majority of existing remote gaze trackers.

Abstract: 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. METHODS: The markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system. RESULTS: The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of +/- 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements. CONCLUSION: A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.

Abstract: 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. METHODS: The developed system is composed of three main computational blocks: 1) a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2) a pulse generator, which is responsible for the creation of muscular synergies; and 3) a limb model based on two joints (two degrees of freedom) and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans. RESULTS: The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians. Curvature values are similar to those encountered in experimental measures with humans. The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector. CONCLUSION: The proposed system has been shown to properly simulate the development of internal models and to control the generation and execution of ballistic planar arm movements. Since the neural controller learns to manage movements on the basis of kinematic information and arm characteristics, it could in perspective command a neuroprosthesis instead of a biomechanical model of a human upper limb, and it could thus give rise to novel rehabilitation techniques.

Abstract: Inferring relations between cognitive processes and postural control is a relatively topical challenge in developmental neurology. This study investigated the effect of a concurrent cognitive task on postural control in a sample of 50 nine-year-old children. Each subject completed two balance trials of 60 s, one with a concurrent cognitive task (cognitive load) and another with no cognitive load. The concurrent cognitive task consisted of mentally counting backwards in steps of 2. Twelve posturographic parameters (PPs) were extracted from the centre of pressure (CoP) trajectory obtained through a load cell force plate. Analysis of variance revealed significant differences in the majority of the extracted PPs. CoP was found to travel faster, farther, and with substantially different features demonstrating an overall broadening of the spectrum in the frequency domain. Nonlinear stability factors revealed significant differences when exposed to a concurrent cognitive task, showing an increase of instability in the intervention rate of the postural control system. By grouping children through selected items from Teachers Ratings and PANESS assessment, specific significant differences were also found both in time and frequency domain PPs, thus confirming the hypothesis of an interaction between cognitive processes (and their development), and postural control.

Abstract: In the context of tele-monitoring, great interest is presently devoted to physical activity, mainly of elderly or people with disabilities. In this context, many researchers studied the recognition of activities of daily living by using accelerometers. The present work proposes a novel algorithm for activity recognition that considers the variability in movement speed, by using dynamic programming. This objective is realized by means of a matching and recognition technique that determines the distance between the signal input and a set of previously defined templates. Two different approaches are here presented, one based on Dynamic Time Warping (DTW) and the other based on the Derivative Dynamic Time Warping (DDTW). The algorithm was applied to the recognition of gait, climbing and descending stairs, using a biaxial accelerometer placed on the shin. The results on DDTW, obtained by using only one sensor channel on the shin showed an average recognition score of 95%, higher than the values obtained with DTW (around 85%). Both DTW and DDTW consistently show higher classification rate than classical Linear Time Warping (LTW).

Abstract: The extraction of the Time-to-boundary function (TtB) from CoP data in upright stance trials has recently gained much attention. In this contribution, some parameters extracted from TtB in unperturbed upright stance trials were analysed in terms of variability with respect to different hypothesized stability limits. In particular, nonlinear stability factors were analyzed as a function of the different values for the boundary limits through which TtB is calculated. A sample population of 5 healthy young adults underwent upright stance trials in different conditions. The amplitude nonlinear stability factors showed a strong dependency with the boundary limits, which on the contrary did not affect the rhythmical nonlinear stability factors.

Abstract: A novel method to evaluate postural sway is presented: balance strategies are identified by applying a markerless image processing algorithm to video sequences obtained from commercially available systems. The motion estimation technique for the analysis of video sequences is a coarse to fine procedure based on the block matching algorithm (BMA). The method makes it possible to estimate the movement of selected elements on the scene with a sub-pixel precision. It has been done by applying a bicubic spline interpolation to the coarse results obtained by the BMA. Results achieved through the analysis of synthetic video tests make it possible to determine the accuracy of the proposed sub-pixel algorithm. Figures show how the proposed method can be confidently applied to evaluate postural sway. The proposed method has been applied to videos recorded during orthostatic posture trials in different conditions, and to combined tests where signals from a balance plate have been acquired simultaneously to the video sequence. The results show the usefulness of the proposed approach in order to evaluate balance strategies in posturography.

Abstract: In this paper different estimation techniques are evaluated for the assessment of electromechanical delay (EMD). The following techniques are compared for benchmarking purposes: envelope estimation and thresholding, with different subjective combinations of filters and thresholds, and a double threshold statistical detector (DTD). Performances are compared in terms of bias, standard deviation and erroneous detections of the estimations. DTD showed higher robustness and repeatability of results, guaranteed by the objective settings based on the statistical characteristics of the algorithm.

Abstract: When learning a novel motor task, the sensorimotor system must develop new strategies to efficiently control the limb(s) involved, and this adaptation appears to be developed through the construction of a behavioral map known as an 'internal model'. A common method to uncover the mechanisms of adaptation and reorganization processes is to expose the system to new environmental conditions, typically by introducing visual or mechanical distortions. The present study investigated the adaptation mechanisms of the human sensorimotor system to horizontal and vertical mirror distortions (HMD and VMD) during the execution of fast goal-directed arm movements. Mirror distortions (MDs) were created by means of virtual visual feedback on a computer screen while the movement was executed on a graphics tablet. Twenty healthy adult participants were recruited and assigned to one of two groups of 10 people each. Tests were divided in two subsequent blocks of five trials. The first block consisted of trials with no mirror distortion (NMD), while the second block was recorded when exposing one group to HMD and the other to VMD. Both MDs resulted in kinematic changes: during the tests with the MDs the participants did not reach the performance level found at the NMD test. Motor performance during HMD appeared to be globally better than during VMD and the adaptation process to VMD appeared to be slower than to HMD, but data interpretation was hampered by large within-participant and between-participant variability. In-depth analyses of the data revealed that most of the motor performance information was contained in the direction of movement. The data supported the idea that the internal model for HMD was already partially built.

Abstract: In recent years many approaches to foreground extraction from images related to human movement have been presented. The foreground extraction represents a pre-processing procedure to be implemented in a system for capturing human movement in order to facilitate the tracking of anatomical landmarks on human bodies. In this work, an approach based on an unsupervised neural network has been studied: a Kohonen map has been designed to recognize and separate structures characterizing foreground and background. The proposed technique is fully automatic and its performance has been compared with those of two further approaches based on differences between foreground and background images. In order to quantify the segmentation quality, an already validated, objective, and automatic criterion has been used. The obtained results are adequate with the final aim of the application and show the feasibility of the proposed approach.

Abstract: BACKGROUND: The present study investigates balance control mechanisms, their variations with the absence of visual input, and their development in children from 7 to 11 years old, in order to provide insights on the development of balance control in the pediatric population. METHODS: Posturographic data were recorded during 60 s trials administered on a sample population of 148 primary school children while stepping and then quietly standing on a force plate in two different vision conditions: eyes closed and eyes open. The extraction of posturographic parameters on the quiet standing phase of the experiment was preceded by the implementation of an algorithm to identify the settling time after stepping on the force plate. The effect of different conditions on posturographic parameters was tested with a two-way ANOVA (Age x Vision), and the corresponding eyes-closed/eyes-open (Romberg) Ratios underwent a one-way ANOVA. RESULTS: Several posturographic measures were found to be sensitive to testing condition (eyes closed vs. eyes open) and some of them to age and anthropometric parameters. The latter relationship did not explain all the data variability with age. An evident modification of postural strategy was observed between 7 and 11 years old children. CONCLUSION: Simple measures extracted from posturographic signals resulted sensitive to vision and age: data acquired from force plate made it possible to confirm the hypothesis of the development of postural strategies in children as a more mature selection and re-weighting of proprioceptive inputs to postural control in absence of visual input.

Abstract: The aim of the central nervous system in upright stance is to control an intrinsically unstable plant. Internal disturbances, such as haemodynamics and respiration, constitute an endogenous threat to equilibrium. The way CNS reacts to those perturbations was studied in this work, through the analysis of summary scores taken from posturographic and pneumographic data. Signals were recorded simultaneously during trials administered on a sample population of healthy young adults, while sitting and standing and at paced and spontaneous uncontrolled breathing. The extraction of posturographic and pneumographic parameters was accompanied by the utilization of techniques for the detection of phase synchronization in bivariate data, and the extraction of an interaction index, the mutual information MI. The effects of the biomechanical condition and respiratory amplitude on MI and summary measures were tested with a two-way ANOVA. Summary scores clearly depend on posture condition. Synchronization between breath and postural sway is always present, depends on both biomechanical condition and respiratory threat, and cannot be reduced to a simple linear relation.

Abstract: The objective of this study was to evaluate the sensitivity of posturographic parameters (PP) to changes in acquisition settings. A group of eight young adults underwent a set of typical orthostatic posture trials, and selected PP were then calculated from a set of centre of pressure (CoP) displacement time series obtained by applying different cut-off frequencies to the same set of raw data. Four PP out of 11 showed significant changes with respect to cut-off frequency. Statistical mechanics parameters exhibited smaller sensitivity than summary measures. On the basis of the results obtained, a proposal for a standard cut-off frequency and a sampling rate value is embodied in the paper together with some suggestions on measurement settings, with a view to standardized use of instrumentation for quantitative analysis in orthostatic posturography.

Abstract: The postural control system is assessed by observing body sway while the subject involved aims at maintaining a specified up-right posture. Internal masses generate internal reaction forces that constitute an internal mechanical stimulus that may contribute to cause segmental displacements, i.e. body sway. Thus, gaining knowledge about the amplitude and direction of these reaction forces would contribute to gain insights into the mechanisms that influence the maintenance of balance and into its control. The 3-D force vector that acts on the body centre of mass (COM) and is associated with the transient blood movement at each cardiac cycle was assessed in a population sample of 20 young adults during the maintenance of a quiet up-right posture. Typical patterns of the three components of this force vector were identified. Relevant parameters were selected and submitted to sample statistics. For a number of them, linear correlation with subject-specific parameters was found. The antero-posterior force component was characterised by a triphasic major wave, the peaks of which had values up to 0.40 N. The vertical component showed a repeatable triphasic wave with peak-to-peak values in the range 1.3-3.0 N. The medio-lateral component showed relatively low peak-to-peak values (in the range 0.05-0.10 N). The resultant vector had an amplitude that underwent several oscillations during the cardiac cycle and reached its maximal value in the range 0.6-1.7 N.