Automatic Detection of Magnetic Disturbances in Magnetic Inertial Measurement Unit Sensors Based on Recurrent Neural Networks

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. The model was trained and tested using 15 folds, which considered independence in sensor, disturbance direction, and signal type. On average, the network can adequately detect the disturbances in 98% of the cases, which represents a significant improvement over current threshold-based detection algorithms.

Transcranial magnetic stimulation combined with endogenous human hippocampal and motor cortical activity enhances memory

The hippocampus is a fundamental cortical structure in the memory process of encoding, retaining, and recalling information. Transcranial Magnetic Stimulation (TMS) following a Paired Associative Stimulation (PAS) enhances nervous system excitability and promotes cortical plasticity mechanisms by synchronizing two stimuli in the same neural pathway. However, PAS has not been shown to improve memorization capacity yet. Here, we present an innovative protocol stemming from the PAS paradigm, which combines single-pulse TMS to the hippocampus with endogenous hippocampal activity during a working memory (WM) task. 96 volunteers were randomized across one experimental group and three parallel groups (motor cortex stimulation, sham stimulation, and no stimulation) in a single session. This combined-stimuli configuration resulted in an increased memorization capacity in the WM task, which was dependent on the stimulated brain location and subjects' basal memory performance. These results are potentially significant for clinical research on memory dysfunction and its related neurological disorders. Future research on paired associative or combined stimulation is required to unveil stimulation-derived neural mechanisms that enhance the ability to memorize.

The complex landscape of TMS devices: A brief overview

The increasing application of TMS in research and therapy has spawned an ever-growing number of commercial and non-commercial TMS devices and technology development. New CE-marked devices appear at a rate of approximately one every two years, with new FDA-approved application of TMS occurring at a similar rate. With the resulting complex landscape of TMS devices and their application, accessible information about the technological characteristics of the TMS devices, such as the type of their circuitry, their pulse characteristics, or permitted protocols would be beneficial. We here present an overview and open access database summarizing key features and applications of available commercial and non-commercial TMS devices (http://www.tmsbase.info). This may guide comparison and decision making about the use of these devices. A bibliometric analysis was performed by identifying commercial and non-commercial TMS devices from which a comprehensive database was created summarizing their publicly available characteristics, both from a technical and clinical point of view. In this document, we introduce both the commercial devices and prototypes found in the literature. The technical specifications that unify these devices are briefly analysed in two separate tables: power electronics, waveform, protocols, and coil types. In the prototype TMS systems, the proposed innovations are focused on improving the treatment regarding the patient: noise cancellation, controllable parameters, and multiple stimulation. This analysis shows that the landscape of TMS is becoming increasingly fragmented, with new devices appearing ever more frequently. The review provided here can support development of benchmarking frameworks and comparison between TMS systems, inform the choice of TMS platforms for specific research and therapeutic applications, and guide future technology development for neuromodulation devices. This standardisation strategy will allow a better end-user choice, with an impact on the TMS manufacturing industry and a homogenisation of patient samples in multi-centre clinical studies. As an open access repository, we envisage the database to grow along with the dynamic development of TMS devices and applications through community-lead curation.

Muscle synergies analysis shows altered neural strategies in women with patellofemoral pain during walking

Several studies suggest that the central nervous system coordinates muscle activation by modulating neural commands directed to groups of muscles combined to form muscle synergies. Individuals with patellofemoral pain (PFP) move differently from asymptomatic individuals. Understanding the neural strategies involved in the execution of tasks such as walking can help comprehend how the movement is planned and better understand this clinical condition. The objective of this study was to compare muscle synergies between women with and without PFP during walking. Eleven women with PFP and thirteen asymptomatic women were assessed using three-dimensional kinematics and electromyography (EMG) while walking at self-selected speed. Kinematics of the trunk, pelvis and lower limbs were analyzed through the Movement Deviation Profile. Muscle synergies were extracted from the EMG signals of eight lower limb muscles collected throughout the whole gait cycle. Kinematic differences between the two groups (p<0.001, z-score = 3.06) were more evident during loading response, terminal stance, and pre-swing. PFP group presented a lower number of muscle synergies (p = 0.037), and greater variability accounted for (VAFtotal) when using 3 (p = 0.017), 4 (p = 0.004), and 5 (p = 0.012) synergies to reconstruct all EMG signals. The PFP group also presented higher VAFmuscle for rectus femoris (p = 0.048) and gastrocnemius medialis (p = 0.019) when considering 4 synergies. Our results suggest that women with PFP show lower motor complexity and deficit in muscle coordination to execute gait, indicating that gait in PFP is the result of different neural commands compared to asymptomatic women.

Two motor neuron synergies, invariant across ankle joint angles, activate the triceps surae during plantarflexion

Recent studies have suggested that the nervous system generates movements by controlling groups of motor neurons (synergies) that do not always align with muscle anatomy. In this study, we determined whether these synergies are robust across tasks with different mechanical constraints. We identified motor neuron synergies using principal component analysis (PCA) and cross-correlations between smoothed discharge rates of motor neurons. In part 1, we used simulations to validate these methods. The results suggested that PCA can accurately identify the number of common inputs and their distribution across active motor neurons. Moreover, the results confirmed that cross-correlation can separate pairs of motor neurons that receive common inputs from those that do not receive common inputs. In part 2, 16 individuals performed plantarflexion at three ankle angles while we recorded EMG signals from the gastrocnemius lateralis (GL) and medialis (GM) and the soleus (SOL) with grids of surface electrodes. The PCA revealed two motor neuron synergies. These motor neuron synergies were relatively stable, with no significant differences in the distribution of motor neuron weights across ankle angles (P = 0.62). When the cross-correlation was calculated for pairs of motor units tracked across ankle angles, we observed that only 13.0% of pairs of motor units from GL and GM exhibited significant correlations of their smoothed discharge rates across angles, confirming the low level of common inputs between these muscles. Overall, these results highlight the modularity of movement control at the motor neuron level, suggesting a sensible reduction of computational resources for movement control. KEY POINTS: The CNS might generate movements by activating groups of motor neurons (synergies) with common inputs. We show here that two main sources of common inputs drive the motor neurons innervating the triceps surae muscles during isometric ankle plantarflexions. We report that the distribution of these common inputs is globally invariant despite changing the mechanical constraints of the tasks, i.e. the ankle angle. These results suggest the functional relevance of the modular organization of the CNS to control movements.

Toward a generalizable deep CNN for neural drive estimation across muscles and participants

Objective.High-density electromyography (HD-EMG) decomposition algorithms are used to identify individual motor unit (MU) spike trains, which collectively constitute the neural code of movements, to predict motor intent. This approach has advanced from offline to online decomposition, from isometric to dynamic contractions, leading to a wide range of neural-machine interface applications. However, current online methods need offline retraining when applied to the same muscle on a different day or to a different person, which limits their applications in a real-time neural-machine interface. We proposed a deep convolutional neural network (CNN) framework for neural drive estimation, which takes in frames of HD-EMG signals as input, extracts general spatiotemporal properties of MU action potentials, and outputs the number of spikes in each frame. The deep CNN can generalize its application without retraining to HD-EMG data recorded in separate sessions, muscles, or participants.Approach.We recorded HD-EMG signals from the vastus medialis and vastus lateralis muscles from five participants while they performed isometric contractions during two sessions separated by ∼20 months. We identified MU spike trains from HD-EMG signals using a convolutive blind source separation (BSS) method, and then used the cumulative spike train (CST) of these MUs and the HD-EMG signals to train and validate the deep CNN.Main results.On average, the correlation coefficients between CST from the BSS and that from deep CNN were0.983±0.006for leave-one-out across-sessions-and-muscles validation and0.989±0.002for leave-one-out across-participants validation. When trained with more than four datasets, the performance of deep CNN saturated at0.984±0.001for cross validations across muscles, sessions, and participants.Significance.We can conclude that the deep CNN is generalizable across the aforementioned conditions without retraining. We could potentially generate a robust deep CNN to estimate neural drive to muscles for neural-machine interfaces.

Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f₁ score. The LSTM models achieved 0.98 f₁ scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.

Transcranial Magnetic Stimulation Following a Paired Associative Stimulation Protocol Based on a Video Game Neuromodulates Cortical Excitability and Motor Behavior

Transcranial Magnetic Stimulation (TMS) can be used to modulate cortico-spinal excitability following a paired associative stimulation (PAS) protocol. Movement-related cortical stimulation (MRCS) is a PAS protocol based on the synchronization of a single-pulse TMS with a movement task. However, plasticity and motor performance potentiation due to MRCS has been related exclusively to single-movement tasks. In order to unveil the effects of an MRCS protocol in complex movements, we applied PAS synchronized with a movement-related dynamic task (MRDT) with a customized video game. In 22 healthy subjects, we measured the reaction time (RT), trajectory error (TE), and the number of collected and avoided items when playing the custom video game to evaluate the task motor performance. Moreover, we assessed the recruitment curve of Motor Evoked Potentials (MEPs) with five different intensities to evaluate the motor corticospinal excitability. MEPs were recorded in Abductor Pollicis Brevis (APB) and Abductor Digiti Minimi (ADM), before, right after, and 30 min after the PAS intervention, in an active versus sham experimental design. The MRCS PAS intervention resulted in RT reduction, and motor corticospinal excitability was modulated, reflected as significant MEP amplitude change at 110% RMT intensity in ADM and at 130% RMT intensity in APB. RTs and ADM MEP amplitudes correlated positively in specific time and intensity assessments. We conclude that the proposed PAS protocol facilitated RT performance in a complex task. This phenomenon might be useful to develop neurorehabilitation strategies with complex movements, similar to activities of daily living.

A unified scheme for the benchmarking of upper limb functions in neurological disorders

BACKGROUND

In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary.

METHODS

This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols.

RESULTS

We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles.

CONCLUSIONS

This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.

Peripheral electrical stimulation to reduce pathological tremor: a review

Interventions to reduce tremor in essential tremor (ET) and Parkinson’s disease (PD) clinical populations often utilize pharmacological or surgical therapies. However, there can be significant side effects, decline in effectiveness over time, or clinical contraindications for these interventions. Therefore, alternative approaches must be considered and developed. Some non-pharmacological strategies include assistive devices, orthoses and mechanical loading of the tremorgenic limb, while others propose peripheral electrical stimulation. Specifically, peripheral electrical stimulation encompasses strategies that activate motor and sensory pathways to evoke muscle contractions and impact sensorimotor function. Numerous studies report the efficacy of peripheral electrical stimulation to alter tremor generation, thereby opening new perspectives for both short- and long-term tremor reduction. Therefore, it is timely to explore this promising modality in a comprehensive review. In this review, we analyzed 27 studies that reported the use of peripheral electrical stimulation to reduce tremor and discuss various considerations regarding peripheral electrical stimulation: the stimulation strategies and parameters, electrodes, experimental designs, results, and mechanisms hypothesized to reduce tremor. From our review, we identified a high degree of disparity across studies with regard to stimulation patterns, experimental designs and methods of assessing tremor. Having standardized experimental methodology is a critical step in the field and is needed in order to accurately compare results across studies. With this review, we explore peripheral electrical stimulation as an intervention for tremor reduction, identify the limitations and benefits of the current state-of-the-art studies, and provide ideas to guide the development of novel approaches based on the neural circuitries and mechanical properties implied in tremor generation.

Individual differences in the neural strategies to control the lateral and medial head of the quadriceps during a mechanically constrained task

We observed that the distribution of the strength of neural drive between the vastus lateralis and vastus medialis during a single-joint isometric task varied across participants. Also, we observed that the proportion of neural drive that was shared within and between these muscles also varied across participants. These results provide evidence that the neural strategies to control the vastus lateralis and vastus medialis muscles widely vary across individuals, even during a mechanically constrained task.

[Robot therapy with the H2 exoskeleton for gait rehabilitation in patients with incomplete spinal cord injry. A clinical experience]

BACKGROUND AND OBJECTIVE

Robotic exoskeletons have emerged as a promising tool in gait rehabilitation in patients with a spinal cord injury. The aim of this study was to assess the clinical applicability of a new robotic exoskeleton model (Exo H2) in the rehabilitation of people with incomplete spinal cord injury.

MATERIAL AND METHODS

Exo H2 exoskeleton training was performed for 15 sessions in patients with incomplete subacute spinal cord injury. We analysed the appearance of undesirable events and the patient's perception of pain, fatigue and comfort. In addition, a pilot test was carried out on the possible effectiveness of the device by analysing gait characteristics before and after treatment measured by the 10mWT, the 6mWT, the TUG, the WISCI-II, and the impact on the SCIM III scale.

RESULTS

Of a group of 8 patients recruited, we were able to analyse data from 4. No undesirable effects were reported. The VAS value was 2.28±1.55 for pain, 3.75±1.55 for fatigue and 4.17±1.68 for comfort. All values improved on the WISCI-I and the TUG and almost all in the 10MWT and in the 6MWT.

CONCLUSIONS

The performance of the Exo H2 exoskeleton was robust during a clinical protocol for gait rehabilitation. The treatment was safe, without undesirable effects and with good patient tolerance. These results might justify the performance of clinical trials with an adequate sample size.

Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback

Background

Add-on robot-mediated therapy has proven to be more effective than conventional therapy alone in post-stroke gait rehabilitation. Such robot-mediated interventions routinely use also visual biofeedback tools. A better understanding of biofeedback content effects when used for robotic locomotor training may improve the rehabilitation process and outcomes.

Methods

This randomized cross-over pilot trial aimed to address the possible impact of different biofeedback contents on patients’ performance and experience during Lokomat training, by comparing a novel biofeedback based on online biological electromyographic information (EMGb) versus the commercial joint torque biofeedback (Rb) in sub-acute non ambulatory patients.

12 patients were randomized into two treatment groups, A and B, based on two different biofeedback training. For both groups, study protocol consisted of 12 Lokomat sessions, 6 for each biofeedback condition, 40 min each, 3 sessions per week of frequency. All patients performed Lokomat trainings as an add-on therapy to the conventional one that was the same for both groups and consisted of 40 min per day, 5 days per week. The primary outcome was the Modified Ashworth Spasticity Scale, and secondary outcomes included clinical, neurological, mechanical, and personal experience variables collected before and after each biofeedback training.

Results

Lokomat training significantly improved gait/daily living activity independence and trunk control, nevertheless, different effects due to biofeedback content were remarked. EMGb was more effective to reduce spasticity and improve muscle force at the ankle, knee and hip joints. Robot data suggest that Rb induces more adaptation to robotic movements than EMGb. Furthermore, Rb was perceived less demanding than EMGb, even though patient motivation was higher for EMGb. Robot was perceived to be effective, easy to use, reliable and safe: acceptability was rated as very high by all patients.

Conclusions

Specific effects can be related to biofeedback content: when muscular-based information is used, a more direct effect on lower limb spasticity and muscle activity is evidenced. In a similar manner, when biofeedback treatment is based on joint torque data, a higher patient compliance effect in terms of force exerted is achieved. Subjects who underwent EMGb seemed to be more motivated than those treated with Rb.

Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms

Background: Traditional studies on the neural mechanisms of tremor use coherence analysis to investigate the relationship between cortical and muscle activity, measured by electroencephalograms (EEG) and electromyograms (EMG). This methodology is limited by the need of relatively long signal recordings, and it is sensitive to EEG artifacts. Here, we analytically derive and experimentally validate a new method for automatic extraction of the tremor-related EEG component in pathological tremor patients that aims to overcome these limitations. Methods: We exploit the coupling between the tremor-related cortical activity and motor unit population firings to build a linear minimum mean square error estimator of the tremor component in EEG. We estimated the motor unit population activity by decomposing surface EMG signals into constituent motor unit spike trains, which we summed up into a cumulative spike train (CST). We used this CST to initialize our tremor-related EEG component estimate, which we optimized using a novel approach proposed here. Results: Tests on simulated signals demonstrate that our new method is robust to both noise and motor unit firing variability, and that it performs well across a wide range of spectral characteristics of the tremor. Results on 9 essential (ET) and 9 Parkinson's disease (PD) patients show a ~2-fold increase in amplitude of the coherence between the estimated EEG component and the CST, compared to the classical EEG-EMG coherence analysis. Conclusions: We have developed a novel method that allows for more precise and robust estimation of the tremor-related EEG component. This method does not require artifact removal, provides reliable results in relatively short datasets, and tracks changes in the tremor-related cortical activity over time.

Synthesis of aminophenylhydroxamate and aminobenzylhydroxamate derivatives and in vitro screening for antiparasitic and histone deacetylase inhibitory activity

A series of aminophenylhydroxamates and aminobenzylhydroxamates were synthesized and screened for their antiparasitic activity against Leishmania, Trypanosoma, and Toxoplasma. Their anti-histone deacetylase (HDAC) potency was determined. Moderate to no antileishmanial or antitrypanosomal activity was found (IC50 > 10 μM) that contrast with the highly efficient anti-Toxoplasma activity (IC50 < 1.0 μM) of these compounds. The antiparasitic activity of the synthetized compounds correlates well with their HDAC inhibitory activity. The best-performing compound (named 363) express a high anti-HDAC6 inhibitory activity (IC50 of 0.045 ± 0.015 μM) a moderate cytotoxicity and a high anti-Toxoplasma activity in the range of known anti-Toxoplasma compounds (IC50 of 0.35-2.25 μM). The calculated selectivity index (10-300 using different human cell lines) of the compound 363 makes it a lead compound for the future development of anti-Toxoplasma molecules.

Adaptive hybrid robotic system for rehabilitation of reaching movement after a brain injury: a usability study

Background

Brain injury survivors often present upper-limb motor impairment affecting the execution of functional activities such as reaching. A currently active research line seeking to maximize upper-limb motor recovery after a brain injury, deals with the combined use of functional electrical stimulation (FES) and mechanical supporting devices, in what has been previously termed hybrid robotic systems. This study evaluates from the technical and clinical perspectives the usability of an integrated hybrid robotic system for the rehabilitation of upper-limb reaching movements after a brain lesion affecting the motor function.

Methods

The presented system is comprised of four main components. The hybrid assistance is given by a passive exoskeleton to support the arm weight against gravity and a functional electrical stimulation device to assist the execution of the reaching task. The feedback error learning (FEL) controller was implemented to adjust the intensity of the electrical stimuli delivered on target muscles according to the performance of the users. This control strategy is based on a proportional-integral-derivative feedback controller and an artificial neural network as the feedforward controller. Two experiments were carried out in this evaluation. First, the technical viability and the performance of the implemented FEL controller was evaluated in healthy subjects (N = 12). Second, a small cohort of patients with a brain injury (N = 4) participated in two experimental session to evaluate the system performance. Also, the overall satisfaction and emotional response of the users after they used the system was assessed.

Results

In the experiment with healthy subjects, a significant reduction of the tracking error was found during the execution of reaching movements. In the experiment with patients, a decreasing trend of the error trajectory was found together with an increasing trend in the task performance as the movement was repeated. Brain injury patients expressed a great acceptance in using the system as a rehabilitation tool.

Conclusions

The study demonstrates the technical feasibility of using the hybrid robotic system for reaching rehabilitation. Patients’ reports on the received intervention reveal a great satisfaction and acceptance of the hybrid robotic system.

Trial registration

Retrospective trial registration in ISRCTN Register with study ID ISRCTN12843006.

Combining muscle synergies and biomechanical analysis to assess gait in stroke patients

The understanding of biomechanical deficits and impaired neural control of gait after stroke is crucial to prescribe effective customized treatments aimed at improving walking function. Instrumented gait analysis has been increasingly integrated into the clinical practice to enhance precision and inter-rater reliability for the assessment of pathological gait. On the other hand, the analysis of muscle synergies has gained relevance as a novel tool to describe the neural control of walking. Since muscle synergies and gait analysis capture different but equally important aspects of walking, we hypothesized that their combination can improve the current clinical tools for the assessment of walking performance. To test this hypothesis, we performed a complete bilateral, lower limb biomechanical and muscle synergies analysis on nine poststroke hemiparetic patients during overground walking. Using stepwise multiple regression, we identified a number of kinematic, kinetic, spatiotemporal and synergy-related features from the paretic and non-paretic side that, combined together, allow to predict impaired walking function better than the Fugl-Meyer Assessment score. These variables were time of peak knee flexion, VAF_total values, duration of stance phase, peak of paretic propulsion and range of hip flexion. Since these five variables describe important biomechanical and neural control features underlying walking deficits poststroke, they may be feasible to drive customized rehabilitation therapies aimed to improve walking function. This paper demonstrates the feasibility of combining biomechanical and neural-related measures to assess locomotion performance in neurologically injured individuals.

Simultaneous estimation of human and exoskeleton motion: A simplified protocol

Adequate benchmarking procedures in the area of wearable robots is gaining importance in order to compare different devices on a quantitative basis, improve them and support the standardization and regulation procedures. Performance assessment usually focuses on the execution of locomotion tasks, and is mostly based on kinematic-related measures. Typical drawbacks of marker-based motion capture systems, gold standard for measure of human limb motion, become challenging when measuring limb kinematics, due to the concomitant presence of the robot. This work answers the question of how to reliably assess the subject's body motion by placing markers over the exoskeleton. Focusing on the ankle joint, the proposed methodology showed that it is possible to reconstruct the trajectory of the subject's joint by placing markers on the exoskeleton, although foot flexibility during walking can impact the reconstruction accuracy. More experiments are needed to confirm this hypothesis, and more subjects and walking conditions are needed to better characterize the errors of the proposed methodology, although our results are promising, indicating small errors.

Towards an ankle neuroprosthesis for hybrid robotics: Concepts and current sources for functional electrical stimulation

Hybrid rehabilitation robotics combine neuro-prosthetic devices (close-loop functional electrical stimulation systems) and traditional robotic structures and actuators to explore better therapies and promote a more efficient motor function recovery or compensation. Although hybrid robotics and ankle neuroprostheses (NPs) have been widely developed over the last years, there are just few studies on the use of NPs to electrically control both ankle flexion and extension to promote ankle recovery and improved gait patterns in paretic limbs. The aim of this work is to develop an ankle NP specifically designed to work in the field of hybrid robotics. This article presents early steps towards this goal and makes a brief review about motor NPs and Functional Electrical Stimulation (FES) principles and most common devices used to aid the ankle functioning during the gait cycle. It also shows a current sources analysis done in this framework, in order to choose the best one for this intended application.

Boosting the traditional physiotherapist approach for stroke spasticity using a sensorized ankle foot orthosis: a pilot study

BACKGROUND

Spasticity is a motor disorder that is commonly treated manually by a physical therapist (PhT) stretching the muscles. Recent data on learning have demonstrated the importance of human-to-human interaction in improving rehabilitation: cooperative motor behavior engages specific areas of the motor system compared with execution of a task alone.

OBJECTIVES

We hypothesize that PhT-guided therapy that involves active collaboration with the patient (Pt) through shared biomechanical visual biofeedback (vBFB) positively impacts learning and performance by the Pt during ankle spasticity treatment. A sensorized ankle foot orthosis (AFO) was developed to provide online quantitative data of joint range of motion (ROM), angular velocity, and electromyographic activity to the PhT and Pt during the treatment of ankle spasticity.

METHODS

Randomized controlled clinical trial. Ten subacute stroke inpatients, randomized into experimental (EXP) and control (CTRL) groups, underwent six weeks of daily treatment. The EXP group was treated with an active AFO, and the CTRL group was given an inactive AFO. Spasticity, ankle ROM, ankle active and passive joint speed, and coactivation index (CI) were assessed at enrollment and after 15-30 sessions.

RESULTS

Spasticity and CI (p < 0.005) decreased significantly after training only in the EXP group, in association with a significant rise in active joint speed and active ROM (p < 0.05). Improvements in spasticity (p < 0.05), active joint speed (p < 0.001), and CI (p < 0.001) after treatment differed between the EXP and CTRL groups.

CONCLUSIONS

PhT-Pt sharing of exercise information, provided by joint sensorization and vBFB, improved the efficacy of the conventional approach for treating ankle spasticity in subacute stroke Pts.

Electrical Stimulation of Afferent Pathways for the Suppression of Pathological Tremor

Pathological tremors are involuntary oscillatory movements which cannot be fully attenuated using conventional treatments. For this reason, several studies have investigated the use of neuromuscular electrical stimulation for tremor suppression. In a recent study, however, we found that electrical stimulation below the motor threshold also suppressed tremor, indicating involvement of afferent pathways. In this study, we further explored this possibility by systematically investigating how tremor suppression by afferent stimulation depends on the stimulation settings. In this way, we aimed at identifying the optimal stimulation strategy, as well as to elucidate the underlying physiological mechanisms of tremor suppression. Stimulation strategies varying the stimulation intensity and pulse timing were tested in nine tremor patients using either intramuscular or surface stimulation. Significant tremor suppression was observed in six patients (tremor suppression > 75% was observed in three patients) and the average optimal suppression level observed across all subjects was 52%. The efficiency for each stimulation setting, however, varied substantially across patients and it was not possible to identify a single set of stimulation parameters that yielded positive results in all patients. For example, tremor suppression was achieved both with stimulation delivered in an out-of-phase pattern with respect to the tremor, and with random timing of the stimulation. Overall, these results indicate that low-current stimulation of afferent fibers is a promising approach for tremor suppression, but that further research is required to identify how the effect can be maximized in the individual patient.

Modular control of gait after incomplete spinal cord injury: differences between sides

STUDY DESIGN

This is an analytical descriptive study.

OBJECTIVES

The main goal of this study was to compare the modular organization of bilateral lower limb control in incomplete spinal cord injury (iSCI) patients during overground walking, using muscle synergies analysis. The secondary goal was to determine whether the similarity between the patients and control group correlate with clinical indicators of walking performance.

SETTING

This study was conducted in National Hospital for Spinal Cord Injury (Toledo, Spain).

METHODS

Eight iSCI patients and eight healthy subjects completed 10 walking trials at matched speed. For each trial, three-dimensional motion analysis and surface electromyography (sEMG) analysis of seven leg muscles from both limbs were performed. Muscle synergies were extracted from sEMG signals using a non-negative matrix factorization algorithm. The optimal number of synergies has been defined as the minimum number needed to obtain variability accounted for (VAF) ⩾90%.

RESULTS

When compared with healthy references, iSCI patients showed fewer muscle synergies in the most affected side and, in both sides, significant differences in the composition of synergy 2. The degree of similarity of these variables with the healthy reference, together with the composition of synergy 3 of the most affected side, presented significant correlations (P<0.05) with walking performance.

CONCLUSION

The analysis of muscle synergies shows potential to detect differences between the two sides in patients with iSCI. Specifically, the VAF may constitute a new neurophysiological metric to assess and monitor patients' condition throughout the gait recovery process.

Advances in selective activation of muscles for non-invasive motor neuroprostheses

Non-invasive neuroprosthetic (NP) technologies for movement compensation and rehabilitation remain with challenges for their clinical application. Two of those major challenges are selective activation of muscles and fatigue management. This review discusses how electrode arrays improve the efficiency and selectivity of functional electrical stimulation (FES) applied via transcutaneous electrodes. In this paper we review the principles and achievements during the last decade on techniques for artificial motor unit recruitment to improve the selective activation of muscles. We review the key factors affecting the outcome of muscle force production via multi-pad transcutaneous electrical stimulation and discuss how stimulation parameters can be set to optimize external activation of body segments. A detailed review of existing electrode array systems proposed by different research teams is also provided. Furthermore, a review of the targeted applications of existing electrode arrays for control of upper and lower limb NPs is provided. Eventually, last section demonstrates the potential of electrode arrays to overcome the major challenges of NPs for compensation and rehabilitation of patient-specific impairments.

Muscle Synergies in Cycling after Incomplete Spinal Cord Injury: Correlation with Clinical Measures of Motor Function and Spasticity

Background: After incomplete spinal cord injury (iSCI), patients suffer important sensorimotor impairments, such as abnormal locomotion patterns and spasticity. Complementary to current clinical diagnostic procedures, the analysis of muscle synergies has emerged as a promising tool to study muscle coordination, which plays a major role in the control of multi-limb functional movements.

Objective: Based on recent findings suggesting that walking and cycling share similar synergistic control, the analysis of muscle synergies during cycling might be explored as an early descriptor of gait-related impaired control. This idea was split into the following two hypotheses: (a) iSCI patients present a synergistic control of muscles during cycling; (b) muscle synergies outcomes extracted during cycling correlate with clinical measurements of gait performance and/or spasticity.

Methods: Electromyographic (EMG) activity of 13 unilateral lower limb muscles was recorded in a group of 10 healthy individuals and 10 iSCI subjects during cycling at four different cadences. A non-negative matrix factorization (NNMF) algorithm was applied to identify synergistic components (i.e., activation coefficients and muscle synergy vectors). Reconstruction goodness scores (VAF and r²) were used to evaluate the ability of a given number of synergies to reconstruct the EMG signals.

A set of metrics based on the similarity between pathologic and healthy synergies were correlated with clinical scales of gait performance and spasticity.

Results: iSCI patients preserved a synergistic control of muscles during cycling. The similarity with the healthy reference was consistent with the degree of the impairment, i.e., less impaired patients showed higher similarities with the healthy reference. There was a strong correlation between reconstruction goodness scores at 42 rpm and motor performance scales (TUG, 10-m test and WISCI II). On the other hand, the similarity between the healthy and affected synergies presented correlation with some spasticity symptoms measured by Penn, Modified Ashworth and SCATS scales.

Conclusion: Overall, the results of this study support the hypothesis that the analysis of muscle synergies during cycling can provide detailed quantitative assessment of functional motor impairments and symptoms of spasticity caused by abnormal spatiotemporal muscle co-activation following iSCI.

Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions

Background

As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes.

Methods

In this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user’s brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection.

Results

Three healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively).

Conclusions

The accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.

Predictive classification of self-paced upper-limb analytical movements with EEG

The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from intervals preceding self-initiated movement tasks. Features selected for the classification are subject specific and associated with the movement tasks. Further tests are performed to reject the hypothesis that other information different from the task-related cortical activity is being used by the classifiers. Six healthy subjects were measured performing self-initiated upper-limb analytical movements. A Bayesian classifier was used to classify among seven different kinds of movements. Features considered covered the alpha and beta bands. A genetic algorithm was used to optimally select a subset of features for the classification. An average accuracy of 62.9 ± 7.5% was reached, which was above the baseline level observed with the proposed methodology (30.2 ± 4.3%). The study shows how the electroencephalography carries information about the type of analytical movement performed with the upper limb and how it can be decoded before the movement begins. In neurorehabilitation environments, this information could be used for monitoring and assisting purposes.

Task independent identification of sensor location on upper limb from orientation data

In this paper we describe a novel method for sensor placement identification, and demonstrate the effectiveness of this method on an upper limb neuroprothesis for tremor suppression under a variety of tasks. Our objective is to facilitate long-term tremor monitoring; tremor is the most prevalent movement disorder. Two assumptions are made: 1) movement and tremor demonstrate an additive effect further down the kinematic chain; 2) most applications have chained or fixed sensor locations. These assumptions justify obtaining absolute location through identifying relative location and thus to allow us to simplify the classification algorithm. Seventeen tasks were performed by patients suffering from essential tremor or Parkinson's disease. Ten features were found that resulted in 98.30% average accuracy (min: 92.31%; max: 100%) for the best configuration, irrespective of the task being performed. The method presented here is an important step towards more user-friendly and context-aware neuroprostheses for tremor suppression and monitoring, and facilitates the use of wearable sensors by non-trained personnel.

Influence of common synaptic input to motor neurons on the neural drive to muscle in essential tremor

Tremor in essential tremor (ET) is generated by pathological oscillations at 4-12 Hz, likely originating at cerebello-thalamo-cortical pathways. However, the way in which tremor is represented in the output of the spinal cord circuitries is largely unknown because of the difficulties in identifying the behavior of individual motor units from tremulous muscles. By using novel methods for the decomposition of multichannel surface EMG, we provide a systematic analysis of the discharge properties of motor units in nine ET patients, with concurrent recordings of EEG activity. This analysis allowed us to infer the contribution of common synaptic inputs to motor neurons in ET. Motor unit short-term synchronization was significantly greater in ET patients than in healthy subjects. Furthermore, the strong association between the degree of synchronization and the peak in coherence between motor unit spike trains at the tremor frequency indicated that the high synchronization levels were generated mainly by common synaptic inputs specifically at the tremor frequency. The coherence between EEG and motor unit spike trains demonstrated the presence of common cortical input to the motor neurons at the tremor frequency. Nonetheless, the strength of this input was uncorrelated to the net common synaptic input at the tremor frequency, suggesting a contribution of spinal afferents or secondary supraspinal pathways in projecting common input at the tremor frequency. These results provide the first systematic analysis of the neural drive to the muscle in ET and elucidate some of its characteristics that determine pathological tremulous muscle activity.

Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials

OBJECTIVE

Characterizing the intention to move by means of electroencephalographic activity can be used in rehabilitation protocols with patients' cortical activity taking an active role during the intervention. In such applications, the reliability of the intention estimation is critical both in terms of specificity 'number of misclassifications' and temporal accuracy. Here, a detector of the onset of voluntary upper-limb reaching movements based on the cortical rhythms and the slow cortical potentials is proposed. The improvement in detections due to the combination of these two cortical patterns is also studied.

APPROACH

Upper-limb movements and cortical activity were recorded in healthy subjects and stroke patients performing self-paced reaching movements. A logistic regression combined the output of two classifiers: (i) a naïve Bayes classifier trained to detect the event-related desynchronization preceding the movement onset and (ii) a matched filter detecting the bereitschaftspotential. The proposed detector was compared with the detectors by using each one of these cortical patterns separately. In addition, differences between the patients and healthy subjects were analysed.

MAIN RESULTS

On average, 74.5 ± 13.8% and 82.2 ± 10.4% of the movements were detected with 1.32 ± 0.87 and 1.50 ± 1.09 false detections generated per minute in the healthy subjects and the patients, respectively. A significantly better performance was achieved by the combined detector (as compared to the detectors of the two cortical patterns separately) in terms of true detections (p = 0.099) and false positives (p = 0.0083).

SIGNIFICANCE

A rationale is provided for combining information from cortical rhythms and slow cortical potentials to detect the onsets of voluntary upper-limb movements. It is demonstrated that the two cortical processes supply complementary information that can be summed up to boost the performance of the detector. Successful results have been also obtained with stroke patients, which supports the use of the proposed system in brain-computer interface applications with this group of patients.

Shared muscle synergies in human walking and cycling

The motor system may rely on a modular organization (muscle synergies activated in time) to execute different tasks. We investigated the common control features of walking and cycling in healthy humans from the perspective of muscle synergies. Three hypotheses were tested: 1) muscle synergies extracted from walking trials are similar to those extracted during cycling; 2) muscle synergies extracted from one of these motor tasks can be used to mathematically reconstruct the electromyographic (EMG) patterns of the other task; 3) muscle synergies of cycling can result from merging synergies of walking. A secondary objective was to identify the speed (and cadence) at which higher similarities emerged. EMG activity from eight muscles of the dominant leg was recorded in eight healthy subjects during walking and cycling at four matched cadences. A factorization technique [nonnegative matrix factorization (NNMF)] was applied to extract individual muscle synergy vectors and the respective activation coefficients behind the global muscular activity of each condition. Results corroborated hypotheses 2 and 3, showing that 1) four synergies from walking and cycling can successfully explain most of the EMG variability of cycling and walking, respectively, and 2) two of four synergies from walking appear to merge together to reconstruct one individual synergy of cycling, with best reconstruction values found for higher speeds. Direct comparison of the muscle synergy vectors of walking and the muscle synergy vectors of cycling (hypothesis 1) produced moderated values of similarity. This study provides supporting evidence for the hypothesis that cycling and walking share common neuromuscular mechanisms.

Hybrid gait training with an overground robot for people with incomplete spinal cord injury: a pilot study

Locomotor training has proved to provide beneficial effect in terms of mobility in incomplete paraplegic patients. Neuroprosthetic technology can contribute to increase the efficacy of a training paradigm in the promotion of a locomotor pattern. Robotic exoskeletons can be used to manage the unavoidable loss of performance of artificially driven muscles. Hybrid exoskeletons blend complementary robotic and neuro-prosthetic technologies. The aim of this pilot study was to determine the effects of hybrid gait training in three case studies with persons with incomplete spinal cord injury (iSCI) in terms of locomotion performance during assisted gait, patient-robot adaptations, impact on ambulation and assessment of lower limb muscle strength and spasticity. Participants with iSCI received interventions with a hybrid bilateral exoskeleton for 4 days. Assessment of gait function revealed that patients improved the 6 min and 10 m walking tests after the intervention, and further improvements were observed 1 week after the intervention. Muscle examination revealed improvements in knee and hip sagittal muscle balance scores and decreased score in ankle extensor balance. It is concluded that improvements in biomechanical function of the knee joint after the tested overground hybrid gait trainer are coherent with improvements in gait performance.

Hybrid FES-robot cooperative control of ambulatory gait rehabilitation exoskeleton

Robotic and functional electrical stimulation (FES) approaches are used for rehabilitation of walking impairment of spinal cord injured individuals. Although devices are commercially available, there are still issues that remain to be solved. Control of hybrid exoskeletons aims at blending robotic exoskeletons and electrical stimulation to overcome the drawbacks of each approach while preserving their advantages. Hybrid actuation and control have a considerable potential for walking rehabilitation but there is a need of novel control strategies of hybrid systems that adequately manage the balance between FES and robotic controllers. Combination of FES and robotic control is a challenging issue, due to the non-linear behavior of muscle under stimulation and the lack of developments in the field of hybrid control. In this article, a cooperative control strategy of a hybrid exoskeleton is presented. This strategy is designed to overcome the main disadvantages of muscular stimulation: electromechanical delay and change in muscle performance over time, and to balance muscular and robotic actuation during walking.

Experimental results in healthy subjects show the ability of the hybrid FES-robot cooperative control to balance power contribution between exoskeleton and muscle stimulation. The robotic exoskeleton decreases assistance while adequate knee kinematics are guaranteed. A new technique to monitor muscle performance is employed, which allows to estimate muscle fatigue and implement muscle fatigue management strategies. Kinesis is therefore the first ambulatory hybrid exoskeleton that can effectively balance robotic and FES actuation during walking. This represents a new opportunity to implement new rehabilitation interventions to induce locomotor activity in patients with paraplegia.

Acronym list: 10mWT: ten meters walking test; 6MWT: six minutes walking test; FSM: finite-state machine; t-FSM: time-domain FSM; c-FSM: cycle-domain FSM; FES: functional electrical stimulation; HKAFO: hip-knee-ankle-foot orthosis; ILC: iterative error-based learning control; MFE: muscle fatigue estimator; NILC: Normalized stimulation output from ILC controller; PID: Proportional-Integral-derivative Control; PW: Stimulation pulse width; QUEST: Quebec User Evaluation of Satisfaction with assistive Technology; SCI: Spinal cord injury; TTI: torque-time integral; VAS: Visual Analog Scale.

Tibialis Anterior muscle coherence during controlled voluntary activation in patients with spinal cord injury: diagnostic potential for muscle strength, gait and spasticity

Background

Coherence estimation has been used as an indirect measure of voluntary neurocontrol of residual motor activity following spinal cord injury (SCI). Here intramuscular Tibialis Anterior (TA) coherence estimation was performed within specific frequency bands for the 10-60 Hz bandwidth during controlled ankle dorsiflexion in subjects with incomplete SCI with and without spasticity.

Methods

In the first cohort study 15 non-injured and 14 motor incomplete SCI subjects were recruited to evaluate TA coherence during controlled movement. Specifically 15-30 Hz EMG was recorded during dorsiflexion with: i) isometric activation at 50, 75 and 100% of maximal voluntary torque (MVT), ii) isokinetic activation at 60 and 120°/s and iii) isotonic dorsiflexion at 50% MVT. Following identification of the motor tasks necessary for measurement of optimal TA coherence a second cohort was analyzed within the 10-16 Hz, 15-30 Hz, 24-40 Hz and 40-60 Hz bandwidths from 22 incomplete SCI subjects, with and without spasticity.

Results

Intramuscular 40-60 Hz, but not 15-30 Hz TA, coherence calculated in SCI subjects during isometric activation at 100% of MVT was lower than the control group. In contrast only isometric activation at 100% of MVT 15-30 Hz TA coherence was higher in subjects with less severe SCI (AIS D vs. AIS C), and correlated functionally with dorsiflexion MVT. Higher TA coherence was observed for the SCI group during 120°/s isokinetic movement. In addition 15-30 Hz TA coherence calculated during isometric activation at 100% MVT or 120°/s isokinetic movement correlated moderately with walking function and time from SCI, respectively. Spasticity symptoms correlated negatively with coherence during isometric activation at 100% of MVT in all tested frequency bands, except for 15-30 Hz. Specifically, 10-16 Hz coherence correlated inversely with passive resistive torque to ankle dorsiflexion, while clinical measures of muscle hypertonia and spasm severity correlated inversely with 40-60 Hz.

Conclusion

Analysis of intramuscular 15-30 Hz TA coherence during isometric activation at 100% of MVT is related to muscle strength and gait function following incomplete SCI. In contrast several spasticity symptoms correlated negatively with 10-16 Hz and 40-60 Hz TA coherence during isometric activation at 100% MVT. Validation of the diagnostic potential of TA coherence estimation as a reliable and comprehensive measure of muscle strength, gait and spasticity should facilitate SCI neurorehabilation.

Upper-limb muscular electrical stimulation driven by EEG-based detections of the intentions to move: a proposed intervention for patients with stroke

This study proposes an intervention for stroke patients in which electrical stimulation of muscles in the affected arm is supplied when movement intention is detected from the electroencephalographic signal. The detection relies on the combined analysis of two movement related cortical patterns: the event-related desynchronization and the bereitschaftspotential. Results with two healthy subjects and three chronic stroke patients show that reliable EEG-based estimations of the movement onsets can be generated (on average, 66.9 ± 26.4 % of the movements are detected with 0.42 ± 0.17 false activations per minute) which in turn give rise to electrical stimuli providing sensory feedback tightly associated to the movement planning (average detection latency of the onsets of the movements was 54.4 ± 287.9 ms).

A novel FES control paradigm based on muscle synergies for postural rehabilitation therapy with hybrid exoskeletons

Hybrid exoskeletons combine robotic orthoses and motor neuroprosthetic devices to compensate for motor disabilities and assist rehabilitation. The basic idea is to take benefits from the strength of each technology, primarily the power of robotic actuators and the clinical advantages of using patient's muscles, while compensating for the respective weaknesses: weight and autonomy for the former, fatigue and stability for the latter. While a wide repertory of solutions have been proposed in literature for the control of robotic orthoses and simple motor neuroprosthesis, the same problem on a complex hybrid architecture, involving a wide number of muscles distributed on multiple articulations, still waits for a practical solution. In this article we present a general algorithm for the control of the neuroprosthesis in the execution of functional coordinated movements. The method extracts muscle synergies as a mean to diagnose residual neuromotor capabilities, and adapts the rehabilitation exercise to patient requirements in a dynamic way. Fatigue effects and unexpected perturbations are compensated by monitoring functional state variables estimated from sensors in the robot. The proposed concept is applied to a case-study scenario, in which a postural balance rehabilitation therapy is presented.

Analysis of biomechanical data to determine the degree of users participation during robotic-assisted gait rehabilitation

Recent studies have shown evidence indicating that effective robotic rehabilitation is only possible when the user actively participates during training. Providing a complete effective biofeedback to the patient representing his compliance to the therapy and his performance is thought that his active participation will be enhanced significantly, thus, improving his rehabilitation. We have performed a study with the driven gait orthosis (DGO) Lokomat (Hocoma AG, Volketswil, Switzerland). The objective of the present study is the analysis of the effect of different types of participation (attention to the functional task) from subjects receiving robotic assisted gait training on the kinematic and kinetic patterns. The obtained results provide useful evidence of specific biomechanical features that can be used to design more useful, robust, focused and intuitive biomechanical biofeedback during robotic assisted gait rehabilitation in stroke survivors.

Effects of robotic guidance on the coordination of locomotion

BACKGROUND

Functional integration of motor activity patterns enables the production of coordinated movements, such as walking. The activation of muscles by weightened summation of activation signals has been demonstrated to represent the spatiotemporal components that determine motor behavior during walking. Exoskeleton robotic devices are now often used in the rehabilitation practice to assist physical therapy of individuals with neurological disorders. These devices are used to promote motor recovery by providing guidance force to the patients. The guidance should in principle lead to a muscle coordination similar to physiological human walking. However, the influence of robotic devices on locomotor patterns needs still to be characterized. The aim of this study was to analyze the effect of force guidance and gait speed on the modular organization of walking in a group of eight healthy subjects.

METHOD

A group of healthy subjects walked on a treadmill with and without robotic aiding at speeds of 1.5, 2.0 and 2.5 Km/h. The guidance force was varied between 20%, 40%, 70% and 100% level of assistance. EMG recordings were obtained from seven leg muscles of the dominant leg and kinematic and kinetic features of the knee and hip joints were extracted.

RESULTS

Four motor modules were sufficient to represent the variety of behavioral goals demanded during robotic guidance, with similar relationships between muscle patterns and biomechanical parameters across subjects, confirming that the low-dimensional and impulsive control of human walking is maintained using robotic force guidance. The conditions of guidance force and speed that maintained correct and incorrect (not natural) modular control were identified.

CONCLUSION

In neurologically intact subjects robotic-guided walking at various force guidance and speed levels does not alter the basic locomotor control and timing. This allows the design of robotic-aided rehabilitation strategies aimed at the modulation of motor modules, which are altered in stroke.

Design and validation of a neuroprosthesis for the treatment of upper limb tremor

Pathological tremor is the most prevalent movement disorder. In spite of the existence of various treatments for it, tremor poses a functional problem to a large proportion of patients. This paper presents the design and implementation of a novel neuroprosthesis for tremor management. The paper starts by reviewing a series of design criteria that were established after analyzing users needs and the expected functionality of the system. Then, it summarizes the design of the neuroprosthesis, which was built to meet the criteria defined previously. Experimental results with a representative group of 12 patients show that the neuroprosthesis provided significant (p < 0.001) and systematic tremor attenuation (in average 52.33 ± 25.48 %), and encourage its functional evaluation as a potential new treatment for tremor in a large cohort of patients.

Similarity of muscle synergies in human walking and cycling: preliminary results

Recent investigations on how the Motor System coordinates different tasks in humans have indicated that a low-dimensional structure of muscle synergies is sufficient to explain specific spatiotemporal components underlying such behaviors. In this work, we tested the hypothesis that pedaling and walking share common modular features by using the muscle synergies paradigm. Seven healthy subjects walked on a treadmill at their maximum speed and also cycled in an ergometer, set at the same walking cadence. EMG activity was recorded from 10 muscles of the most dominant leg. A Non-Negative Matrix Factorization algorithm was applied to extract synergies. Four synergies were sufficient to explain 90% of the EMG variability during walking and cycling. There were statistically significant correlations (higher than 71%) across similar synergies for each task (walking and pedaling). These preliminary results support the hypothesis of modular control across different human motor tasks and may indicate that some synergies are shared amongst different rhythmic movements.

Principles of human locomotion: a review

In this article the principles of human locomotion are revisited and reviewed. This has been done in the framework of two European projects, where the elicitation of these mechanisms inform, on the one hand, the design of artificial bipedal walkers (H2R), and on the other hand the design of lower limb exoskeletons (BETTER) for rehabilitation of gait in post-stroke patients. Passive dynamics emerging from the morphology of the human musculoskeletal system, reflexes as stabilization mechanisms, modular control of movement as well as supra-spinal control of gait are reviewed to get insight on how these mechanisms can be used to explain human locomotion.

Motor modules in robot-aided walking

BACKGROUND

It is hypothesized that locomotion is achieved by means of rhythm generating networks (central pattern generators) and muscle activation generating networks. This modular organization can be partly identified from the analysis of the muscular activity by means of factorization algorithms. The activity of rhythm generating networks is described by activation signals whilst the muscle intervention generating network is represented by motor modules (muscle synergies). In this study, we extend the analysis of modular organization of walking to the case of robot-aided locomotion, at varying speed and body weight support level.

METHODS

Non Negative Matrix Factorization was applied on surface electromyographic signals of 8 lower limb muscles of healthy subjects walking in gait robotic trainer at different walking velocities (1 to 3 km/h) and levels of body weight support (0 to 30%).

RESULTS

The muscular activity of volunteers could be described by low dimensionality (4 modules), as for overground walking. Moreover, the activation signals during robot-aided walking were bursts of activation timed at specific phases of the gait cycle, underlying an impulsive controller, as also observed in overground walking. This modular organization was consistent across the investigated speeds, body weight support level, and subjects.

CONCLUSIONS

These results indicate that walking in a Lokomat robotic trainer is achieved by similar motor modules and activation signals as overground walking and thus supports the use of robotic training for re-establishing natural walking patterns.

Review of hybrid exoskeletons to restore gait following spinal cord injury

Different approaches are available to compensate gait in persons with spinal cord injury, including passive orthoses, functional electrical stimulation (FES), and robotic exoskeletons. However, several drawbacks arise from each specific approach. Orthotic gait is energy-demanding for the user and functionally ineffective. FES uses the muscles as natural actuators to generate gait, providing not only functional but also psychological benefits to the users. However, disadvantages are also related to the early appearance of muscle fatigue and the control of joint trajectories. Robotic exoskeletons that provide joint moment compensation or substitution to the body during walking have been developed in recent years. Significant advances have been achieved, but the technology itself is not mature yet because of many limitations related to both physical and cognitive interaction as well as portability and energy-management issues. Meanwhile, the combination of FES technology and exoskeletons has emerged as a promising approach to both gait compensation and rehabilitation, bringing together technologies, methods, and rehabilitation principles that can overcome the drawbacks of each individual approach. This article presents an overview of hybrid lower-limb exoskeletons, related technologies, and advances in actuation and control systems. Also, we highlight the functional assessment of individuals with spinal cord injury.

Enhancing functional electrical stimulation for emerging rehabilitation robotics in the framework of HYPER project

This paper presents the development of a novel functional electrical stimulation (FES) system. New approaches in emerging rehabilitation robotics propose the use of residual muscular activity or limbs movements during the rehabilitation process of neuromotor. More ambitious projects propose the use of FES systems to restore or compensate motor capabilities by controlling existing muscles or subject limbs. These emerging approaches require more sophisticated FES devices in terms of channels, signals controls and portability. In the framework of HYPER project, such devices are being developed to support the main objective of the project: the development of neurorobots and neuroprosthetics to restore functional motor capabilities in patients who suffered cerebrovascular accidents or spinal cord injury. The presented portable FES system includes novel elec-trostimulator circuits and improved channel switching capacities to enable emerging approaches in rehabilitation robotics.