J. A. Gallego

Real-time estimation of pathological tremor parameters from gyroscope data.

This paper presents a two stage algorithm for real-time estimation of instantaneous tremor parameters from gyroscope recordings. Gyroscopes possess the advantage of providing directly joint rotational speed, overcoming the limitations of traditional tremor recording based on accelerometers. The proposed algorithm first extracts tremor patterns from raw angular data, and afterwards estimates its instantaneous amplitude and frequency. Real-time separation of voluntary and tremorous motion relies on their different frequency contents, whereas tremor modelling is based on an adaptive LMS algorithm and a Kalman filter. Tremor parameters will be employed to drive a neuroprosthesis for tremor suppression based on biomechanical loading.

Non-invasive characterization of motor unit behaviour in pathological tremor

This paper presents the fully automatic identification of motor unit spike trains from high-density surface electromyograms (EMG) in pathological tremor. First, a mathematical derivation is provided to theoretically prove the possibility of decomposing noise-free high-density surface EMG signals into motor unit spike trains with high correlation, which are typical of tremor contractions. Further, the proposed decomposition method is tested on simulated signals with different levels of noise and on experimental signals from 14 tremor-affected patients. In the case of simulated tremor with central frequency ranging from 5 Hz to 11 Hz and signal-to-noise ratio of 20 dB, the method identified ∼8 motor units per contraction with sensitivity in spike timing identification ≥ 95% and false alarm and miss rates ≤ 5%. In experimental signals, the number of identified motor units varied substantially (range 0-21) across patients and contraction types, as expected. The behaviour of the identified motor units was consistent with previous data obtained by intramuscular EMG decomposition. These results demonstrate for the first time the possibility of a fully non-invasive investigation of motor unit behaviour in tremor-affected patients. The method provides a new means for physiological investigations of pathological tremor.

Finite element three-dimensional analysis of the vibrational behaviour of the Langevin-type transducer

The vibrational behaviour of the Langevin transducer is usually analysed using 1D Mason model which is valid when the lateral dimensions of the transducer are smaller than a quarter wavelength at the fundamental longitudinal resonance. In this work a 3D finite element analysis of the Langevin transducers behaviour operating in the underwater sonar range of frequencies (30-140 kHz) is presented. Several samples with total length greater, comparable to, and smaller than the diameter of the transducer are analysed. For each sample, the resonance frequencies in the observed frequency range are computed and compared with those obtained experimentally from the measurements carried out using several in-house built prototypes. For the most important aspect ratios the resonance displacement distributions are presented and discussed. The results obtained permit to gain insight into the coupling phenomenon between thickness-extensional and radial modes and suggest that in practical applications transducers with diameters comparable to or greater than total length of the active stack can also be used. The trade-off between the efficiency and power handling ability for different designs is also discussed.

A neuroprosthesis for tremor management through the control of muscle co-contraction

BackgroundPathological tremor is the most prevalent movement disorder. Current treatments do not attain a significant tremor reduction in a large proportion of patients, which makes tremor a major cause of loss of quality of life. For instance, according to some estimates, 65% of those suffering from upper limb tremor report serious difficulties during daily living. Therefore, novel forms for tremor management are required. Since muscles intrinsically behave as a low pass filter, and tremor frequency is above that of volitional movements, the authors envisioned the exploitation of these properties as a means of developing a novel treatment alternative. This treatment would rely on muscle co-contraction for tremor management, similarly to the strategy employed by the intact central nervous system to stabilize a limb during certain tasks.MethodsWe implemented a neuroprosthesis that regulated the level of muscle co-contraction by injecting current at a pair of antagonists through transcutaneous neurostimulation. Co-contraction was adapted to the instantaneous parameters of tremor, which were estimated from the raw recordings of a pair of solid state gyroscopes with a purposely designed adaptive algorithm. For the experimental validation, we enrolled six patients suffering from parkinsonian or essential tremor of different severity, and evaluated the effect of the neuroprosthesis during standard tasks employed for neurological examination.ResultsThe neuroprosthesis attained significant attenuation of tremor (p<0.001), and reduced its amplitude up to a 52.33±25.48%. Furthermore, it alleviated both essential and parkinsonian tremor in spite of their different etiology and symptomatology. Tremor severity was not a limiting factor on the performance of the neuroprosthesis, although there was a subtle trend towards larger attenuation of more severe tremors. Tremor frequency was not altered during neurostimulation, as expected from the central origin of Parkinson’s disease and essential tremor. All patients showed a good tolerance to neurostimulation in terms of comfort and absence of pain, and some spontaneously reported that they felt that tremor was reduced when the neuroprosthesis was activated.ConclusionsThe results presented herein demonstrate that the neuroprosthesis provides systematic attenuation of the two major types of tremor, irrespectively from their severity. This study sets the basis for the validation of the neuroprosthesis as an alternative, non-invasive means for tremor management.

A Multimodal Human–Robot Interface to Drive a Neuroprosthesis for Tremor Management

Tremor is the most prevalent movement disorder, and its incidence is increasing with aging. In spite of the numerous therapeutic solutions available, 65% of those suffering from upper limb tremor report serious difficulties during their daily living. This gives rise to research on different treatment alternatives, amongst which wearable robots that apply selective mechanical loads constitute an appealing approach. In this context, the current work presents a multimodal human-robot interface to drive a neuroprosthesis for tremor management. Our approach relies on the precise characterization of the tremor to modulate a functional electrical stimulation system that compensates for it. The neuroprosthesis is triggered by the detection of the intention to move derived from the analysis of electroencephalographic activity, which provides a natural interface with the user. When a prediction is delivered, surface electromyography serves to detect the actual onset of the tremor in the presence of volitional activity. This information in turn triggers the stimulation, which relies on tremor parameters-amplitude and frequency-derived from a pair of inertial sensors that record the kinematics of the affected joint. Surface electromyography also yields a first characterization of the tremor, together with precise information on the preferred stimulation site. Apart from allowing for an optimized performance of the system, our multimodal approach permits the implementation of redundant methods to both enhance the reliability of the system and adapt to the specific needs of different users. Results with a representative group of patients illustrate the performance of the interface presented here and demonstrate its feasibility.

PRECISE MEASUREMENTS OF PARTICLE ENTRAINMENT IN A STANDING-WAVE ACOUSTIC FIELD BETWEEN 20 AND 3500 Hz

Abstract An experimental study is presented to investigate entrainment coefficients of aerosol particles in a sonic wave. The experimental results are contrasted with a well-established analytical expressions for the entrainment coefficient derived from the Brandt–Freund–Hiedemann (BFH) equation. The measurements are carried out in a standing-wave field with an extremely homogeneous and well-determined acoustic velocity field. A CCD-camera with a microscope lens attachment is used to visualize the displacement amplitude of 7.9 μ m glass beads in the frequency range from 20 Hz to 3.5 kHz. The particle entrainment coefficients obtained with this visualization method are in good agreement with the theoretical predictions of the BFH equation and with earlier experimental data by Gucker and Doyle. While this prior study revealed some apparent experimental limitations, the present data is derived with an accurate and repeatable measurement technique. The large number of measurements in the current study helps to draw a detailed representation of the acoustic entrainment coefficient.

Multimodal BCI-mediated FES suppression of pathological tremor

Tremor constitutes the most common movement disorder; in fact 14.5% of population between 50 to 89 years old suffers from it. Moreover, 65% of patients with upper limb tremor report disability when performing their activities of daily living (ADL). Unfortunately, 25% of patients do not respond to drugs or neurosurgery. In this regard, TREMOR project proposes functional compensation of upper limb tremors with a soft wearable robot that applies biomechanical loads through functional electrical stimulation (FES) of muscles. This wearable robot is driven by a Brain Neural Computer Interface (BNCI). This paper presents a multimodal BCI to assess generation, transmission and execution of both volitional and tremorous movements based on electroencephalography (EEG), electromyography (EMG) and inertial sensors (IMUs). These signals are combined to obtain: 1) the intention to perform a voluntary movement from cortical activity (EEG), 2) tremor onset, and an estimation of tremor frequency from muscle activation (EMG), and 3) instantaneous tremor amplitude and frequency from kinematic measurements (IMUs). Integration of this information will provide control signals to drive the FES-based wearable robot.

Brain-controlled neuromuscular stimulation to drive neural plasticity and functional recovery

There is mounting evidence that appropriately timed neuromuscular stimulation can induce neural plasticity and generate functional recovery from motor disorders. This review addresses the idea that coordinating stimulation with a patients voluntary effort might further enhance neurorehabilitation. Studies in cell cultures and behaving animals have delineated the rules underlying neural plasticity when single neurons are used as triggers. However, the rules governing more complex stimuli and larger networks are less well understood. We argue that functional recovery might be optimized if stimulation were modulated by a brain machine interface, to match the details of the patients voluntary intent. The potential of this novel approach highlights the need for a better understanding of the complex rules underlying this form of plasticity.

A Robust Kalman Algorithm to Facilitate Human-Computer Interaction for People with Cerebral Palsy, Using a New Interface Based on Inertial Sensors

This work aims to create an advanced human-computer interface called ENLAZA for people with cerebral palsy (CP). Although there are computer-access solutions for disabled people in general, there are few evidences from motor disabled community (e.g., CP) using these alternative interfaces. The proposed interface is based on inertial sensors in order to characterize involuntary motion in terms of time, frequency and range of motion. This characterization is used to design a filtering technique that reduces the effect of involuntary motion on person-computer interaction. This paper presents a robust Kalman filter (RKF) design to facilitate fine motor control based on the previous characterization. The filter increases mouse pointer directivity and the target acquisition time is reduced by a factor of ten. The interface is validated with CP users who were unable to control the computer using other interfaces. The interface ENLAZA and the RKF enabled them to use the computer.

An EEG-based design for the online detection of movement intention

The development of EEG-based wearable technologies for real-life environments has experienced an increasing interest over the last years. During activities of daily living, these systems need to be able to distinguish predefined mental states from the ongoing EEG signal, and these states of interest can be given after long periods of inactivity. A detector of the intention to move that is conceived to be used in real-time is proposed and offline validated with an experimental protocol with long intervals of inactivity that are also used for the detectors validation.