Giovanni Magenes

Comparison of entropy-based regularity estimators: application to the fetal heart rate signal for the identification of fetal distress

This paper considers the multiscale entropy (MSE) approach for estimating the regularity of time series at different scales. Sample entropy (SampEn) and approximate entropy (ApEn) are evaluated in MSE analysis on simulated data to enhance the main features of both estimators. We applied the approximate entropy and the sample entropy estimators to fetal heart rate signals on both single and multiple scales for an early identification of fetal sufferance antepartum. Our results show that the ApEn index significantly distinguishes suffering from normal fetuses between the 30th and the 35th week of gestation. Furthermore, our data shows that the MSE entropy values are reliable indicators of the fetal distress associated with the presence of a pathological condition at birth.

Saccadic responses evoked by presentation of visual and auditory targets

SummarySaccadic eye movements evoked by the presentation of visual and auditory targets were examined and compared. Differences were found either in the pattern of the saccadic response and in the characteristics of single saccades of the same amplitude. The longer latency and the higher percentage of multiple saccade responses in the auditory case were attributed to a more complex central processing, whereas the longer duration and the lower peak velocity of the saccades to auditory targets were attributed to reduced performances of the execution mechanism in the absence of vision.

EYE-HEAD-HAND COORDINATION IN POINTING AT VISUAL TARGETS - SPATIAL AND TEMPORAL ANALYSIS

This study investigated whether the execution of an accurate pointing response depends on a prior saccade orientation towards the target, independent of the vision of the limb. A comparison was made between the accuracy of sequential responses (in which the starting position of the hand is known and the eye centred on the target prior to the onset of the hand pointing movement) and synergetic responses (where both hand and gaze motions are simultaneously initiated on the basis of unique peripheral retinal information). The experiments were conducted in visual closed-loop (hand visible during the pointing movement) and in visual openloop conditions (vision of hand interrupted as the hand started to move). The latter condition eliminated the possibility of a direct visual evaluation of the error between hand and target during pointing. Three main observations were derived from the present work: (a) the timing of coordinated eye-head-hand pointing at visual targets can be modified, depending on the executed task, without a deterioration in the accuracy of hand pointing; (b) mechanical constraints or instructions such as preventing eye, head or trunk motion, which limit the redundancy of degrees of freedom, lead to a decrease in accuracy; (c) the synergetic movement of eye, head and hand for pointing at a visible target is not trivially the superposition of eye and head shifts added to hand pointing. Indeed, the strategy of such a coordinated action can modify the kinematics of the head in order to make the movements of both head and hand terminate at approximately the same time. The main conclusion is that eye-head coordination is carried out optimally by a parallel processing in which both gaze and hand motor responses are initiated on the basis of a poorly defined retinal signal. The accuracy in hand pointing is not conditioned by head movement per se and does not depend on the relative timing of eye, head and hand movements (synergetic vs sequential responses). However, a decrease in the accuracy of hand pointing was observed in the synergetic condition, when target fixation was not stabilised before the target was extinguished. This suggests that when the orienting saccade reaches the target before hand movement onset, visual updating of the hand motor control signal may occur. A rapid processing of this final input allows a sharper redefinition of the hand landing point.

Principal components analysis based control of a multi-dof underactuated prosthetic hand

BackgroundFunctionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG). Driving a multi degrees of freedom (DoF) hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user.MethodsA Principal Components Analysis (PCA) based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand) with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs). Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control.ResultsTrials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture) may be achieved.ConclusionsThis work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.

A Real-Time and Self-Calibrating Algorithm Based on Triaxial Accelerometer Signals for the Detection of Human Posture and Activity

Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a “reliability” value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2% accuracy when classifying both static and dynamic activities.

The Delay Multiply and Sum Beamforming Algorithm in Ultrasound B-Mode Medical Imaging

Most of ultrasound medical imaging systems currently on the market implement standard Delay and Sum (DAS) beamforming to form B-mode images. However, image resolution and contrast achievable with DAS are limited by the aperture size and by the operating frequency. For this reason, different beamformers have been presented in the literature that are mainly based on adaptive algorithms, which allow achieving higher performance at the cost of an increased computational complexity. In this paper, we propose the use of an alternative nonlinear beamforming algorithm for medical ultrasound imaging, which is called Delay Multiply and Sum (DMAS) and that was originally conceived for a RADAR microwave system for breast cancer detection. We modify the DMAS beamformer and test its performance on both simulated and experimentally collected linear-scan data, by comparing the Point Spread Functions, beampatterns, synthetic phantom and in vivo carotid artery images obtained with standard DAS and with the proposed algorithm. Results show that the DMAS beamformer outperforms DAS in both simulated and experimental trials and that the main improvement brought about by this new method is a significantly higher contrast resolution (i.e., narrower main lobe and lower side lobes), which turns out into an increased dynamic range and better quality of B-mode images.

Classification of cardiotocographic records by neural networks

Antepartum fetal monitoring based on the classical cardiotocography (CTG) is a noninvasive and low-price tool for checking fetal status. Its introduction in the clinical routine limited the occurrence of fetal problems leading to a reduction of the precocious child mortality. Nevertheless very poor indications on fetal pathologies can be inferred from the actual CTG analysis methods, either they consist of the clinician eye inspection or of automatic algorithms. A relevant amount of this unsatisfactory performance resides on the weakness of methods used for classifying fetal conditions and generate a risk alarm during pregnancy. In the paper three neural classifiers are proposed to discriminate among fetal behavioral states and among normal and pathological fetal conditions, on the basis of CTG recordings. All classifiers are fed by indexes extracted from fetal heart rate signal. Results show very promising performance towards the prediction of fetal outcomes on the set of collected FHR signals.

Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis

BackgroundIn spite of the advances made in the design of dexterous anthropomorphic hand prostheses, these sophisticated devices still lack adequate control interfaces which could allow amputees to operate them in an intuitive and close-to-natural way. In this study, an anthropomorphic five-fingered robotic hand, actuated by six motors, was used as a prosthetic hand emulator to assess the feasibility of a control approach based on Principal Components Analysis (PCA), specifically conceived to address this problem. Since it was demonstrated elsewhere that the first two principal components (PCs) can describe the whole hand configuration space sufficiently well, the controller here employed reverted the PCA algorithm and allowed to drive a multi-DoF hand by combining a two-differential channels EMG input with these two PCs. Hence, the novelty of this approach stood in the PCA application for solving the challenging problem of best mapping the EMG inputs into the degrees of freedom (DoFs) of the prosthesis.MethodsA clinically viable two DoFs myoelectric controller, exploiting two differential channels, was developed and twelve able-bodied participants, divided in two groups, volunteered to control the hand in simple grasp trials, using forearm myoelectric signals. Task completion rates and times were measured. The first objective (assessed through one group of subjects) was to understand the effectiveness of the approach; i.e., whether it is possible to drive the hand in real-time, with reasonable performance, in different grasps, also taking advantage of the direct visual feedback of the moving hand. The second objective (assessed through a different group) was to investigate the intuitiveness, and therefore to assess statistical differences in the performance throughout three consecutive days.ResultsSubjects performed several grasp, transport and release trials with differently shaped objects, by operating the hand with the myoelectric PCA-based controller. Experimental trials showed that the simultaneous use of the two differential channels paradigm was successful.ConclusionsThis work demonstrates that the proposed two-DoFs myoelectric controller based on PCA allows to drive in real-time a prosthetic hand emulator into different prehensile patterns with excellent performance. These results open up promising possibilities for the development of intuitive, effective myoelectric hand controllers.

Heart Rate and Accelerometer Data Fusion for Activity Assessment of Rescuers During Emergency Interventions

The current state of the art in wearable electronics is the integration of very small devices into textile fabrics, the so-called ¿smart garment.¿ The ProeTEX project is one of many initiatives dedicated to the development of smart garments specifically designed for people who risk their lives in the line of duty such as fire fighters and Civil Protection rescuers. These garments have integrated multipurpose sensors that monitor their activities while in action. To this aim, we have developed an algorithm that combines both features extracted from the signal of a triaxial accelerometer and one ECG lead. Microprocessors integrated in the garments detect the signal magnitude area of inertial acceleration, step frequency, trunk inclination, heart rate (HR), and HR trend in real time. Given these inputs, a classifier assigns these signals to nine classes differentiating between certain physical activities (walking, running, moving on site), intensities (intense, mild, or at rest) and postures (lying down, standing up). Specific classes will be identified as dangerous to the rescuer during operation, such as, ¿subject motionless lying down¿ or ¿subject resting with abnormal HR.¿ Laboratory tests were carried out on seven healthy adult subjects with the collection of over 4.5 h of data. The results were very positive, achieving an overall classification accuracy of 88.8%.

Towards the realization of an artificial tactile system: fine-form discrimination by a tensorial tactile sensor array and neural inversion algorithms

This paper describes techniques and methodologies so far developed to investigate object fine-form discrimination by means of artificial tactile sensors. Sensor arrays, selectively sensitive to stress-tensor components and based on piezoelectric polymer technology, have been realized. Sensor output data are used to solve inverse elastic contact problems, by means of neural networks suitably trained to learn regularized inverse maps. Two possible neural network designs are considered: one is based on the multilayer perceptron trained with the standard backpropagation algorithm, and the other is based on the use of radial basis functions. In both cases, reconstruction of object shapes is demonstrated to be effective and robust with both simulated and real data. >