T. D’Alessio

The sensitivity of posturographic parameters to acquisition settings

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.

Automatic detection of surface EMG activation timing using a wavelet transform based method

The problem of the identification of the muscle contraction timing by using surface electromyographic signal is addressed. The timing detection of the muscular activation in dynamic conditions has a real clinical diagnostic impact. Widely used single threshold methods still rely on the experience of the operator in manually setting that threshold. A new approach to detect the muscular activation intervals, that is based on discontinuities detection in the wavelet domain, is proposed. Accuracy and precision of the algorithm were assessed by using a set of simulated signals obtaining values lower than 11.0 and 8.7 ms for biases and standard deviations of the estimation, respectively. Moreover an experimental application of the algorithm was carried out recruiting a population of 10 able-bodied subjects and processing the myoelectric signals recorded from the lower limb during an isokinetic exercise. The algorithm was able to reveal correctly the timing of muscular activation with performance comparable to the state-of-the-art methods. The detection algorithm is automatic and user-independent, it manages the detection of both onset and offset activation, it can be fruitfully applied even in presence of noise and, therefore, it can be used also by unskilled operators.

An adaptive Kalman-based Bayes estimation technique to classify locomotor activities in young and elderly adults through accelerometers

An accelerometer-based system able to classify among different locomotor activities during real life conditions is here presented, and its performance evaluated. Epochs of walking at different speeds, and with different slopes, and stair descending and ascending, are detected, segmented, and classified by using an adaptation of a naïve 2D-Bayes classifier, which is updated on-line through the history of the estimated activities, in a Kalman-based scheme. The feature pair used for classification is mapped from an ensemble of 16 features extracted from the accelerometer data for each activity epoch. Two different versions of the classifier are presented to combine the multi-dimensional nature of the accelerometer data, and their results are compared in terms of correct recognition rate of the segmented activities, on two population samples of different age. The classification algorithm achieves correct classification rates higher than 90% and higher than 92%, for young and elderly adults, respectively.

Novel formulation of a double threshold algorithm for the estimation of muscle activation intervals designed for variable SNR environments

The aim of this work is the development of an improved formulation of the double threshold algorithm for sEMG onset-offset detection presented by Bonato and co-workers. The original formulation, which keeps the threshold fixed, suffers from performance degradation whenever the SNR changes during the analysis. The novel approach is designed to be adaptive to SNR changes in either burst or inter-burst zones of sEMG signals recorded in static and dynamic conditions. The detection parameters (i.e. detection and false alarm probabilities) are updated on the basis of an on-line estimation of the SNR. The proposed formulation has been assessed on both simulated and real sEMG data. For constant SNR the performance of the original formulation is confirmed (for SNR > 8 dB, bias and standard deviation less than 10 and 15 ms, respectively; detection percentage higher than 95%), while the novel implementation performs better with time-varying SNR (for SNR varying in the range 10-25 dB the standard approach detection percentage decreases at 50%). Detection on signals recorded during isometric contractions at different force levels confirms the performance on simulated signals (StD = 134 ms; FP = 22%, and StD = 42 ms; FP = 2%, respectively for standard and novel implementation calculated as average on five experimental trials). The pseudo real-time detection allowed by this formulation can be profitably exploited by biofeedback applications based on myoelectric information.

A free geometry model-independent neural eye-gaze tracking system

BackgroundEye Gaze Tracking Systems (EGTSs) estimate the Point Of Gaze (POG) of a user. In diagnostic applications EGTSs are used to study oculomotor characteristics and abnormalities, whereas in interactive applications EGTSs are proposed as input devices for human computer interfaces (HCI), e.g. to move a cursor on the screen when mouse control is not possible, such as in the case of assistive devices for people suffering from locked-in syndrome. If the user’s head remains still and the cornea rotates around its fixed centre, the pupil follows the eye in the images captured from one or more cameras, whereas the outer corneal reflection generated by an IR light source, i.e. glint, can be assumed as a fixed reference point. According to the so-called pupil centre corneal reflection method (PCCR), the POG can be thus estimated from the pupil-glint vector.MethodsA new model-independent EGTS based on the PCCR is proposed. The mapping function based on artificial neural networks allows to avoid any specific model assumption and approximation either for the user’s eye physiology or for the system initial setup admitting a free geometry positioning for the user and the system components. The robustness of the proposed EGTS is proven by assessing its accuracy when tested on real data coming from: i) different healthy users; ii) different geometric settings of the camera and the light sources; iii) different protocols based on the observation of points on a calibration grid and halfway points of a test grid.ResultsThe achieved accuracy is approximately 0.49°, 0.41°, and 0.62° for respectively the horizontal, vertical and radial error of the POG.ConclusionsThe results prove the validity of the proposed approach as the proposed system performs better than EGTSs designed for HCI which, even if equipped with superior hardware, show accuracy values in the range 0.6°-1°.

A wireless integrated system to evaluate efficiency indexes in real time during cycling

In cycling, instrumented devices providing a quantitative assessment of the task execution could facilitate the functional evaluation and the training organization generally carried on by sport trainers. To this purpose, this work deals with the design and the implementation of a system able to evaluate the pedaling efficiency. The system is based on an Instrumented Pedal (IPed) that measures the components of the force (i.e. the perpendicular to the load plane and the tangential to the motion direction) exerted during pedaling and the angle between the pedal and the crank. The force signals are transmitted to a PDA by using a wireless connection and are processed in real time to obtain a performance index. This index is based on the ratio between the amplitude of the force component tangent to the crank (i.e. the component useful to the task) and the amplitude of the overall force vector applied to the pedal. The software packet calculates the performance index and displays it by using both numerical and graphical representations. The performance index can be used by the athletes to monitor the execution of the motor task in order to develop a more efficient pedaling strategy, and also by the trainers to control the training program and the ongoing performance.

Brain network involved in visual processing of movement stimuli used in upper limb robotic training: an fMRI study.

BackgroundThe potential of robot-mediated therapy and virtual reality in neurorehabilitation is becoming of increasing importance. However, there is limited information, using neuroimaging, on the neural networks involved in training with these technologies. This study was intended to detect the brain network involved in the visual processing of movement during robotic training. The main aim was to investigate the existence of a common cerebral network able to assimilate biological (human upper limb) and non-biological (abstract object) movements, hence testing the suitability of the visual non-biological feedback provided by the InMotion2 Robot.MethodsA visual functional Magnetic Resonance Imaging (fMRI) task was administered to 22 healthy subjects. The task required observation and retrieval of motor gestures and of the visual feedback used in robotic training. Functional activations of both biological and non-biological movements were examined to identify areas activated in both conditions, along with differential activity in upper limb vs. abstract object trials. Control of response was also tested by administering trials with congruent and incongruent reaching movements.ResultsThe observation of upper limb and abstract object movements elicited similar patterns of activations according to a caudo-rostral pathway for the visual processing of movements (including specific areas of the occipital, temporal, parietal, and frontal lobes). Similarly, overlapping activations were found for the subsequent retrieval of the observed movement. Furthermore, activations of frontal cortical areas were associated with congruent trials more than with the incongruent ones.ConclusionsThis study identified the neural pathway associated with visual processing of movement stimuli used in upper limb robot-mediated training and investigated the brain’s ability to assimilate abstract object movements with human motor gestures. In both conditions, activations were elicited in cerebral areas involved in visual perception, sensory integration, recognition of movement, re-mapping on the somatosensory and motor cortex, storage in memory, and response control. Results from the congruent vs. incongruent trials revealed greater activity for the former condition than the latter in a network including cingulate cortex, right inferior and middle frontal gyrus that are involved in the go-signal and in decision control. Results on healthy subjects would suggest the appropriateness of an abstract visual feedback provided during motor training. The task contributes to highlight the potential of fMRI in improving the understanding of visual motor processes and may also be useful in detecting brain reorganisation during training.

A two-step model to optimise transcutaneous electrical stimulation of the human upper arm

Purpose – A novel model of the upper arm under transcutaneous electrical stimulation with multi-pad electrodes is presented and experimentally validated. The model aims at simulating and analysing the effects of surface electrical stimulation on biceps brachii. The paper aims to discuss these issues. Design/methodology/approach – Both the passive properties of tissues surrounding nerve bundles and the active characteristics of the nervous system are included. The output of the proposed model is nerve recruitment and muscle contraction. Findings – Simulations and experimental tests on six healthy young adults have been conducted and results show that the proposed model gives information on electrically elicited muscle contraction in accordance with in-vivo tests and literature on motor unit recruitment order. Tests with different electrodes configurations show that the spatial distribution of active electrodes is a critical factor in electrically elicited muscle contractions, and that multi-pad electrodes ...

Evaluation of a Motion-Based Platform for Practicing Phonological Awareness of Preschool Children.

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 cartoon-avatar controlled by a gesture-based natural user interface. En Plein relies on the Microsoft Kinect™ 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 awareness (mean increase of CMF scores of 9%), while their peers who received only the traditional education had a mean increase of CMF scores of 1%. Results from this preliminary study show that children who used the system were more confident in manipulating structural phonological units, increased their awareness of words sound structures and generalized these skills by applying them to different tasks. The performance of En Plein is particularly encouraging for future large-scale applications in kindergartens and possible tests with children at risk of developing specific learning disabilities.

Tremor control during movement of the upper limb using artificial neural networks

This work aims at introducing a novel way of controlling tremor in ballistic planar movements of the upper limb using Artificial Neural Networks. The idea is that of designing a smart controller based on Artificial Neural Networks, able to drive adaptively an electrical stimulator device in order to promote the voluntary movement and to deploy the tremor simultaneously. This paper would like to present the preliminary results of the feasibility study carried on by studying the neural controller when dealing with simulated movements affected by Parkinson’s action tremor. A biomechanical arm model with two pairs of muscles modelled using Hill’s lump circuit has been developed in order to simulate ballistic planar movement of the upper limb. The tremor has been simulated by adding noise in frequency range of Parkinson’s action tremor to the torques at the joints. The neural activations that generate this noise have been found using an Artificial Neural Network trained by using a Reinforcement Learning paradigm. A Noise-Box has been developed in order to combine the neural activations of the noise at the joints with those of a correct movement, thus obtaining the neural activations of a voluntary movement affected by tremor. The Noise-Box also calculates the additive neural activations the controller has to provide to the biomechanical arm model in order to correct the tremor during the movement. Finally the controller has been implemented using a Feed Forward Artificial Neural Network receiving as inputs only the neural activations of a tremorous movement and providing as output the correcting activations. This Neural Network has been trained with a supervised paradigm using the information given by the Noise-Box. Various tests have been performed with different noise amplitudes.