The impact of decision tools during oncological consultation with lung cancer patients: A systematic review within the I3LUNG project

INTRODUCTION

To date, lung cancer is one of the most lethal diagnoses worldwide. A variety of lung cancer treatments and modalities are available, which are generally presented during the patient and doctor consultation. The implementation of decision tools to facilitate patient's decision-making and the management of their healthcare process during medical consultation is fundamental. Studies have demonstrated that decision tools are helpful to promote health management and decision-making of lung cancer patients during consultations. The main aim of the present work within the I3LUNG project is to systematically review the implementation of decision tools to facilitate medical consultation about oncological treatments for lung cancer patients.

METHODS

In the present study, we conducted a systematic review following the PRISMA guidelines. We used an electronic computer-based search involving three databases, as follows: Embase, PubMed, and Scopus. 10 articles met the inclusion criteria and were included. They explicitly refer to decision tools in the oncological context, with lung cancer patients.

RESULTS

The discussion highlights the most encouraging results about the positive role of decision aids during medical consultations about oncological treatments, especially regarding anxiety, decision-making, and patient knowledge. However, no one main decision aid tool emerged as essential. Opting for a more recent timeframe to select eligible articles might shed light on the current array of decision aid tools available.

CONCLUSION

Future review efforts could utilize alternative search strategies to explore other lung cancer-specific outcomes during medical consultations for treatment decisions and the implementation of decision aid tools. Engaging with experts in the fields of oncology, patient decision-making, or health communication could provide valuable insights and recommendations for relevant literature or research directions that may not be readily accessible through traditional search methods. The development of guidelines for future research were provided with the aim to promote decision aids focused on patients' needs.

Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning

According to the motor learning theory by Albus and Ito, synaptic depression at the parallel fibre to Purkinje cells synapse (pf-PC) is the main substrate responsible for learning sensorimotor contingencies under climbing fibre control. However, recent experimental evidence challenges this relatively monopolistic view of cerebellar learning. Bidirectional plasticity appears crucial for learning, in which different microzones can undergo opposite changes of synaptic strength (e.g. downbound microzones-more likely depression, upbound microzones-more likely potentiation), and multiple forms of plasticity have been identified, distributed over different cerebellar circuit synapses. Here, we have simulated classical eyeblink conditioning (CEBC) using an advanced spiking cerebellar model embedding downbound and upbound modules that are subject to multiple plasticity rules. Simulations indicate that synaptic plasticity regulates the cascade of precise spiking patterns spreading throughout the cerebellar cortex and cerebellar nuclei. CEBC was supported by plasticity at the pf-PC synapses as well as at the synapses of the molecular layer interneurons (MLIs), but only the combined switch-off of both sites of plasticity compromised learning significantly. By differentially engaging climbing fibre information and related forms of synaptic plasticity, both microzones contributed to generate a well-timed conditioned response, but it was the downbound module that played the major role in this process. The outcomes of our simulations closely align with the behavioural and electrophysiological phenotypes of mutant mice suffering from cell-specific mutations that affect processing of their PC and/or MLI synapses. Our data highlight that a synergy of bidirectional plasticity rules distributed across the cerebellum can facilitate finetuning of adaptive associative behaviours at a high spatiotemporal resolution.

Brain-computer interface for robot control with eye artifacts for assistive applications

Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user's eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors' knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot.

Improvement of speed-accuracy tradeoff during practice of a point-to-point task in children with acquired dystonia

The tradeoff between speed and accuracy is a well-known constraint for human movement, but previous work has shown that this tradeoff can be modified by practice, and the quantitative relationship between speed and accuracy may be an indicator of skill in some tasks. We have previously shown that children with dystonia are able to adapt their movement strategy in a ballistic throwing game to compensate for increased variability of movement. Here, we test whether children with dystonia can adapt and improve skills learned on a trajectory task. We use a novel task in which children move a spoon with a marble between two targets. Difficulty is modified by changing the depth of the spoon. Our results show that both healthy children and children with acquired dystonia move more slowly with the more difficult spoons, and both groups improve the relationship between speed and spoon difficulty following 1 wk of practice. By tracking the marble position in the spoon, we show that children with dystonia use a larger fraction of the available variability, whereas healthy children adopt a much safer strategy and remain farther from the margins, as well as learning to adapt and have more control over the marble's utilized area by practice. Together, our results show that both healthy children and children with dystonia choose trajectories that compensate for risk and inherent variability, and that the increased variability in dystonia can be modified with continued practice.NEW & NOTEWORTHY This study provides insights into the adaptability of children with dystonia in learning a point-to-point task. We show that these children adjust their strategies to account for increased difficulty in the task. Our findings underscore the potential of task-specific practice in improving motor skills and show higher level of signal-dependent noise can be controlled through repetition and learned strategies, which provides an avenue for the quantitative evaluation of rehabilitation strategies in this challenging group.

Instrumented Upper Limb Functional Assessment Using a Robotic Exoskeleton: Normative References Intervals

Upper-limb rehabilitation exoskeletons offer a valuable solution to support and enhance the rehabilitation path of neural-injured patients. Such devices are usually equipped with a network of sensors that can be exploited to evaluate and monitor the performances of the users. In this work, we assess the normality ranges of different motor-performance indicators on a group of 15 healthy participants, computed with the benchmark toolbox of AGREE, an upper limb motorized exoskeleton. The toolbox implements a benchmarking scheme for the evaluation of the upper limb, used to test anterior reaching at rest position height and hand-to-mouth motor skills. We selected kinematic and electromyography performance indicators to assess the different motor abilities. We performed a pilot evaluation on three neurological patients, to verify if the AGREE benchmark toolbox was able to distinguish patients from healthy subjects on the basis of the selected performance indicators. Through a comparison between results obtained by the healthy and the small group of motor-impaired users, we successfully calculated the normality ranges for the selected performance indicators, and we pilot-showed how data gathered from AGREE can be used to evaluate the current status of the patients.

IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off

The microgravity exposure that astronauts undergo during space missions lasting up to 6 months induces biochemical and physiological changes potentially impacting on their health. As a countermeasure, astronauts perform an in-flight training program consisting in different resistive exercises. To train optimally and safely, astronauts need guidance by on-ground specialists via a real-time audio/video system that, however, is subject to a communication delay that increases in proportion to the distance between sender and receiver. The aim of this work was to develop and validate a wearable IMU-based biofeedback system to monitor astronauts in-flight training displaying real-time feedback on exercises execution. Such a system has potential spin-offs also on personalized home/remote training for fitness and rehabilitation. 29 subjects were recruited according to their physical shape and performance criteria to collect kinematics data under ethical committee approval. Tests were conducted to (i) compare the signals acquired with our system to those obtained with the current state-of-the-art inertial sensors and (ii) to assess the exercises classification performance. The magnitude square coherence between the signals collected with the two different systems shows good agreement between the data. Multiple classification algorithms were tested and the best accuracy was obtained using a Multi-Layer Perceptron (MLP). MLP was also able to identify mixed errors during the exercise execution, a scenario that is quite common during training. The resulting system represents a novel low-cost training monitor tool that has space application, but also potential use on Earth for individuals working-out at home or remotely thanks to its ease of use and portability.

Improvement of speed-accuracy tradeoff during practice of a point to point task in children with secondary dystonia

The tradeoff between speed and accuracy is a well-known constraint for human movement, but previous work has shown that this tradeoff can be modified by practice, and the quantitative relationship between speed and accuracy may be an indicator of skill in some tasks. We have previously shown that children with dystonia are able to adapt their movement strategy in a ballistic throwing game to compensate for increased variability of movement. Here we test whether children with dystonia can adapt and improve skill learnt on a trajectory task. We use a novel task in which children move a spoon with a marble between two targets. Difficulty is modified by changing the depth of the spoon. Our results show that both healthy children and children with secondary dystonia move more slowly with the more difficult spoons, and both groups improve the relationship between speed and spoon difficulty following one week of practice. By tracking the marble position in the spoon, we show that children with dystonia use a larger fraction of the available variability, whereas healthy children adopt a much safer strategy and remain farther from the margins, as well as learning to adopt and have more control over the marble's utilized area by practice. Together, our results show that both healthy children and children with dystonia choose trajectories that compensate for risk and inherent variability, and that the increased variability in dystonia can be modified with continued practice.

Motor and Cognitive Modulation of a Single Session of Transcutaneous Auricular Vagus Nerve Stimulation in Post Stroke Patients: A Pilot Study

Objective: The aim of the present study is to explore whether a single session of transcutaneous Vagus Nerve Stimulation (tVNS) can enhance the ipsilesional, and contralesional upper limb motor functions as well as cognitive functions in stroke patients. The effects of the stimulation were evaluated through two different tasks: the box and blocks test (BB), indexing manual dexterity, and the Go/No-go task, a visuomotor paradigm used to assess both motor readiness and response inhibition. Tests were administered without tVNS, during tVNS and during sham tVNS. Results: The BB showed a statistical difference for both contralesional side (p = 0.05) between Basal-Real condition (p = 0.042) and ipsilesional side (p = 0.001) between Basal-Real (p = 0.008) and for Real-Sham (p = 0.005). Any statistical difference was found for the mean latencies in the three conditions of the Go/No-go test. Conclusion: A single session of tVNS seems to improve upper limb motor functions but not cognitive functions in post-stroke patients, despite a positive trend was detected.

Reshaping the full body illusion through visuo-electro-tactile sensations

The physical boundaries of our body do not define what we perceive as self. This malleable representation arises from the neural integration of sensory information coming from the environment. Manipulating the visual and haptic cues produces changes in body perception, inducing the Full Body Illusion (FBI), a vastly used approach to exploring humans' perception. After pioneering FBI demonstrations, issues arose regarding its setup, using experimenter-based touch and pre-recorded videos. Moreover, its outcome measures are based mainly on subjective reports, leading to biased results, or on heterogeneous objective ones giving poor consensus on their validity. To address these limitations, we developed and tested a multisensory platform allowing highly controlled experimental conditions, thanks to the leveraged use of innovative technologies: Virtual Reality (VR) and Transcutaneous Electrical Nerve Stimulation (TENS). This enabled a high spatial and temporal precision of the visual and haptic cues, efficiently eliciting FBI. While it matched the classic approach in subjective measures, our setup resulted also in significant results for all objective measurements. Importantly, FBI was elicited when all 4 limbs were multimodally stimulated but also in a single limb condition. Our results behoove the adoption of a comprehensive set of measures, introducing a new neuroscientific platform to investigate body representations.

IMU-based human activity recognition and payload classification for low-back exoskeletons

Nowadays, work-related musculoskeletal disorders have a drastic impact on a large part of the world population. In particular, low-back pain counts as the leading cause of absence from work in the industrial sector. Robotic exoskeletons have great potential to improve industrial workers' health and life quality. Nonetheless, current solutions are often limited by sub-optimal control systems. Due to the dynamic environment in which they are used, failure to adapt to the wearer and the task may be limiting exoskeleton adoption in occupational scenarios. In this scope, we present a deep-learning-based approach exploiting inertial sensors to provide industrial exoskeletons with human activity recognition and adaptive payload compensation. Inertial measurement units are easily wearable or embeddable in any industrial exoskeleton. We exploited Long-Short Term Memory networks both to perform human activity recognition and to classify the weight of lifted objects up to 15 kg. We found a median F1 score of [Formula: see text] (activity recognition) and [Formula: see text] (payload estimation) with subject-specific models trained and tested on 12 (6M-6F) young healthy volunteers. We also succeeded in evaluating the applicability of this approach with an in-lab real-time test in a simulated target scenario. These high-level algorithms may be useful to fully exploit the potential of powered exoskeletons to achieve symbiotic human-robot interaction.

Transfer learning in hand movement intention detection based on surface electromyography signals

Over the past several years, electromyography (EMG) signals have been used as a natural interface to interact with computers and machines. Recently, deep learning algorithms such as Convolutional Neural Networks (CNNs) have gained interest for decoding the hand movement intention from EMG signals. However, deep networks require a large dataset to train appropriately. Creating such a database for a single subject could be very time-consuming. In this study, we addressed this issue from two perspectives: (i) we proposed a subject-transfer framework to use the knowledge learned from other subjects to compensate for a target subject’s limited data; (ii) we proposed a task-transfer framework in which the knowledge learned from a set of basic hand movements is used to classify more complex movements, which include a combination of mentioned basic movements. We introduced two CNN-based architectures for hand movement intention detection and a subject-transfer learning approach. Classifiers are tested on the Nearlab dataset, a sEMG hand/wrist movement dataset including 8 movements and 11 subjects, along with their combination, and on open-source hand sEMG dataset “NinaPro DataBase 2 (DB2).” For the Nearlab database, the subject-transfer learning approach improved the average classification accuracy of the proposed deep classifier from 92.60 to 93.30% when classifier was utilizing 10 other subjects’ data via our proposed framework. For Ninapro DB2 exercise B (17 hand movement classes), this improvement was from 81.43 to 82.87%. Moreover, three stages of analysis in task-transfer approach proved that it is possible to classify combination hand movements using the knowledge learned from a set of basic hand movements with zero, few samples and few seconds of data from the target movement classes. First stage takes advantage of shared muscle synergies to classify combined movements, while second and third stages take advantage of novel algorithms using few-shot learning and fine-tuning to use samples from target domain to further train the classifier trained on the source database. The use of information learned from basic hand movements improved classification accuracy of combined hand movements by 10%.

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.

Reproducing a decision-making network in a virtual visual discrimination task

We reproduced a decision-making network model using the neural simulator software neural simulation tool (NEST), and we embedded the spiking neural network in a virtual robotic agent performing a simulated behavioral task. The present work builds upon the concept of replicability in neuroscience, preserving most of the computational properties in the initial model although employing a different software tool. The proposed implementation successfully obtains equivalent results from the original study, reproducing the salient features of the neural processes underlying a binary decision. Furthermore, the resulting network is able to control a robot performing an in silico visual discrimination task, the implementation of which is openly available on the EBRAINS infrastructure through the neuro robotics platform (NRP).

The effects of robotic assistance on upper limb spatial muscle synergies in healthy people during planar upper-limb training

BACKGROUND

Robotic rehabilitation is a commonly adopted technique used to restore motor functionality of neurological patients. However, despite promising results were achieved, the effects of human-robot interaction on human motor control and the recovery mechanisms induced with robot assistance can be further investigated even on healthy subjects before translating to clinical practice. In this study, we adopt a standard paradigm for upper-limb rehabilitation (a planar device with assistive control) with linear and challenging curvilinear trajectories to investigate the effect of the assistance in human-robot interaction in healthy people.

METHODS

Ten healthy subjects were instructed to perform a large set of radial and curvilinear movements in two interaction modes: 1) free movement (subjects hold the robot handle with no assistance) and 2) assisted movement (with a force tunnel assistance paradigm). Kinematics and EMGs from representative upper-limb muscles were recorded to extract phasic muscle synergies. The free and assisted interaction modes were compared assessing the level of assistance, error, and muscle synergy comparison between the two interaction modes.

RESULTS

It was found that in free movement error magnitude is higher than with assistance, proving that task complexity required assistance also on healthy controls. Moreover, curvilinear tasks require more assistance than standard radial paths and error is higher. Interestingly, while assistance improved task performance, we found only a slight modification of phasic synergies when comparing assisted and free movement.

CONCLUSIONS

We found that on healthy people, the effect of assistance was significant on task performance, but limited on muscle synergies. The findings of this study can find applications for assessing human-robot interaction and to design training to maximize motor recovery.

Bayesian Integration in a Spiking Neural System for Sensorimotor Control

The brain continuously estimates the state of body and environment, with specific regions that are thought to act as Bayesian estimator, optimally integrating noisy and delayed sensory feedback with sensory predictions generated by the cerebellum. In control theory, Bayesian estimators are usually implemented using high-level representations. In this work, we designed a new spike-based computational model of a Bayesian estimator. The state estimator receives spiking activity from two neural populations encoding the sensory feedback and the cerebellar prediction, and it continuously computes the spike variability within each population as a reliability index of the signal these populations encode. The state estimator output encodes the current state estimate. We simulated a reaching task at different stages of cerebellar learning. The activity of the sensory feedback neurons encoded a noisy version of the trajectory after actual movement, with an almost constant intrapopulation spiking variability. Conversely, the activity of the cerebellar output neurons depended on the phase of the learning process. Before learning, they fired at their baseline not encoding any relevant information, and the variability was set to be higher than that of the sensory feedback (more reliable, albeit delayed). When learning was complete, their activity encoded the trajectory before the actual execution, providing an accurate sensory prediction; in this case, the variability was set to be lower than that of the sensory feedback. The state estimator model optimally integrated the neural activities of the afferent populations, so that the output state estimate was primarily driven by sensory feedback in prelearning and by the cerebellar prediction in postlearning. It was able to deal even with more complex scenarios, for example, by shifting the dominant source during the movement execution if information availability suddenly changed. The proposed tool will be a critical block within integrated spiking, brain-inspired control systems for simulations of sensorimotor tasks.

A force-based human machine interface to drive a motorized upper limb exoskeleton. a pilot study

Muscular dystrophy is a strongly invalidating disease that causes the progressive loss of motor skills. The use of assistive devices, especially those in support of the upper limb, can increase the ability to perform daily-life activities and foster a partial recovery of the lost motor functionalities. However, for the use of these devices to be truly effective and accepted by patients, their activation must coincide with the user's intention to move. This work describes a new human-machine interface based on the integration of a six-axis force sensor to drive an upper limb motorized exoskeleton. This novel system can detect the patient's intention to move and produce displacements of the robotic device that are of magnitude and direction consistent with the user's wishes. The integration of the force-sensor interface in the BRIDGE/EMPATIA exoskeletal system was successful, and tests performed on both healthy and dystrophic subjects showed promising results, especially for the execution of planar movements.

A Backbone-Tracking Passive Exoskeleton to Reduce the Stress on the Low-Back: Proof of Concept Study

Exoskeletons for the low-back have great potential as tools to both prevent low-back pain for healthy subjects and limit its impact for chronic patients. Here, we show a proof-of-concept evaluation of our low-back exoskeleton. Its peculiar feature is the backbone-tracking kinematic structure that allows tracking the motion of the human spine while bending the trunk. This mechanism is implemented with a rigid-yet-elongating structure that does not hinder nor constrain the motion of the wearer while providing assistance. In this work, we show the first prototype we manufactured. It is equipped with a traction spring to assist the wearer during trunk flexion/extension. Then, we report the results of a preliminary test with healthy subjects. We measured a reduction of the mean absolute value for some target muscles - including the erector spinae - when using the exoskeleton for payload manipulation tasks. This was achieved without affecting task performance, measured as task time and joints range of motion. We believe these preliminary results are encouraging, paving the way for a broader experimental campaign to evaluate our exoskeleton.

AGREE: an upper-limb robotic platform for personalized rehabilitation, concept and clinical study design

Rehabilitation exoskeletons can supplement therapist-based training allowing post-stroke patients to perform functional, high-dosage, repetitive exercises. The use of robotic devices allows providing intense rehabilitation sessions and permits clinicians to personalize the therapy according to the patient's need. In this work, we propose an upper-limb rehabilitation system developed within the AGREE project. The platform relies on a four degrees-of-freedom arm exoskeleton, capable of assisting state-of-the-art rehabilitation exercises under different training modalities while behaving transparently to user-generated and therapist-applied forces. The system is provided with a LEDs-matrix mat to guide patients during reaching tasks with visual feedback, an EMG reader to evaluate the patient's involvement during the therapy, and several software tools to help clinicians customize the treatment and monitor the patient's progress. A randomized controlled pilot study aimed at evaluating the usability and the effectiveness of the AGREE rehabilitation platform to improve arm impairment after stroke is currently ongoing.

Adaptive Hybrid FES-Force Controller for Arm Exosuit

Patients suffering from neuromuscular diseases experience motor disabilities which hinder their independence during activities of daily living (ADLs). For such impaired subjects, robotic devices and Functional Electrical Stimulation (FES) are technologies commonly used to rehabilitate lost functions. Nevertheless, both systems present some limitations, and merging FES and robots in Hybrid Robotic Rehabilitation Systems allows to overcome these boundaries. Here we propose for the first time a hybrid cooperative controller involving FES and a soft wearable upper arm exosuit to rehabilitate elbow movements. We tested the designed hybrid controller on six healthy participants. The results showed how the proposed hybrid controller allowed the wearers to perform flexion movements with no significant decrease in accuracy and precision with respect to the exosuit alone, while significantly decreasing the fatigue level by about 63% and delaying its onset with respect to the FES action alone.

Upper limb exosuit cable routing optimization

Exosuits are emerging as promising in assisting with activities of daily living. In the design phase of an exosuit, it is fundamental to maximize its portability. The goal of this work was to identify the best cable routing configuration for an upper limb cable-driven exosuit to assist elbow flexion. Simulations were run in OpenSim. Different cable configurations were evaluated. The goal was to minimize the overall tension of the cables to reduce the device's power consumption and torque requirements. The optimal configuration was evaluated in simulation for different percentages of assistance to study its effects in terms of muscle activation and joint reaction forces. We then tested three different configurations on a test bench to both evaluate the motor current and their effect on the pronation/supination of the elbow. Simulation results suggested that a double cable configuration might help to lower the motor torque and power consumption. This conclusion was supported by the experimental results, in which the motor current was reduced by 12.5% with respect to the single cable configuration. Simulation results also showed that the optimal configuration lowered muscle activation without greatly affecting joint reactions at the elbow, even though it might cause unwanted pronation/supination, as experimental results confirmed. However, since a double configuration results in greater complexity and reduced efficiency, single-cable solutions still represent a good option.

Sharing Worlds: Design of a Real-Time Attention Classifier for Robotic Therapy of ASD Children. IEEE Int Conf Rehabil Robot 2022;2022:1-6. [PMID: 36176158 DOI: 10.1109/icorr55369.2022.9896506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Joint attention is the capacity of sharing attention between two agents and an aspect of the environment, through the use of different cues, namely gaze. This capacity is of paramount importance for social skills. People with Autism Spectrum Disorder (ASD) present certain deficits in joint attention. Therefore, there is an increasing interest in finding therapies to improve this skill. Some of these therapies include robots since they are known to be attractive to people with autism due to their motivation ability and predictability when compared with humans. In this line, we have designed a real-time attention classifier for a triadic robotic therapy, using Gaze360 and geometrical considerations of the scene. We were able to classify the gaze of the therapist and the one of the child during the whole session, even in a highly unconstrained scenario with a single camera, achieving a mean accuracy of 59%. This classifier can be used for the measurement of joint attention, an important metric for the development of adaptive robotic therapies, where increasing levels of difficulty and engagement are provided dependent on the ASD children, who are characterised by high heterogeneity. Future work will pass by the calculation of this metric and integration on a robotic platform for ASD therapy to understand the impact of these robotic therapies in improving ASD symptoms, specifically on how ASD children share their attention with other people present in the rehabilitation scenarios.

Development of an Interactive Total Body Robot Enhanced Imitation Therapy for ASD children

Autism is a neurodevelopmental disorder in which the available therapies target the improvement of social skills, in order to ensure a high quality of life for the child. The use of Social Assistive Robots offers new therapeutic possibilities in which robots can act as therapy enhancers. IOGIOCO project emerges in this framework: it aims at the development of a Robot- Assisted Therapy protocol for the treatment of Autism Spectrum Disorder, through gesture training. The definition of these gestures and their recognition by the robot are parameters that directly affect the engagement of the children. However, the design of a protocol becomes harder in a highly unconstrained environment. Therefore, the current work aims at expanding the gesture set and improving the gesture recognition algorithm available in the IOGIOCO platform. More specifically, total body gestures have been added to the available upper limbs movements, and a custom Activity Detection method has been developed, which allows the identification of the time window in which a gesture is performed. The insertion of this method on a recognition algorithm based on a ResNet, a particular kind of Convolutional Neural Network, improved its F1-score from 57% obtained with the previously-available version, in a dataset of ASD children, to 76%, demonstrating the effectiveness of the Activity Detection method. Furthermore, the expansion of the interaction possibilities to total body movements was positively evaluated by the clinical staff, increasing the engagement of patients and the set of possible trained skills. Therefore, the results of the current work are encouraging. To reinforce the conclusions drawn, the proposed algorithm should be tested in real time on several autistic children within a complete Randomized Clinical Trial, also to study the effectiveness of this type of treatment. From the technical point of view, further improvements of the developed methodology should tackle the remained issues, such as further increasing the recognition capability, especially in the transitions from sitting to standing, that proved to be a hard task for the developed method.

Hand grip support for rehabilitation and assistance: from patent to TRL5

In the last decades, the continuous increase in the number of the vast cohort of chronic patients that constantly need medical assistance and supervision, and the widespread lack of therapist has brought to an increased interest in the role of medical technologies in rehabilitative programs and assistive scenarios. Current clinical evidence in rehabilitation demonstrates that there is an important and increasing demand for innovative therapeutic solutions to recover the hand functions to prevent patients to need assistance in performing daily life activities. This works describes the pathway from patent to TRL5 of a device to support hand grip actions and interaction with daily life objects. E-KIRO is based on the use of electromagnets, which are able to attach/detach interactive objects equipped with a ferromagnetic plate. Five end-users used the device and scored it with excellent usability based on the System Usability Scale.

Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model for studying active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modeling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Human Brain Project's Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was adequately connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behavior experimentally recorded in mice.

Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces

Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from electroencephalography (EEG) recordings. P300 wave is an event-related potential with a latency of 300 ms after the onset of a rare stimulus. In this paper, we used deep learning architectures, namely convolutional neural networks (CNNs), to improve P300-based BCIs. We propose a novel BCI classifier, called P3CNET, that improved P300 classification accuracy performances of the best state-of-the-art classifier. In addition, we explored pre-processing and training choices that improved the usability of BCI systems. For the pre-processing of EEG data, we explored the optimal signal interval that would improve classification accuracies. Then, we explored the minimum number of calibration sessions to balance higher accuracy and shorter calibration time. To improve the explainability of deep learning architectures, we analyzed the saliency maps of the input EEG signal leading to a correct P300 classification, and we observed that the elimination of less informative electrode channels from the data did not result in better accuracy. All the methodologies and explorations were performed and validated on two different CNN classifiers, demonstrating the generalizability of the obtained results. Finally, we showed the advantages given by transfer learning when using the proposed novel architecture on other P300 datasets. The presented architectures and practical suggestions can be used by BCI practitioners to improve its effectiveness.

Development and Electromyographic Validation of a Compliant Human-Robot Interaction Controller for Cooperative and Personalized Neurorehabilitation

BACKGROUND

Appropriate training modalities for post-stroke upper-limb rehabilitation are key features for effective recovery after the acute event. This study presents a cooperative control framework that promotes compliant motion and implements a variety of high-level rehabilitation modalities with a unified low-level explicit impedance control law. The core idea is that we can change the haptic behavior perceived by a human when interacting with the rehabilitation robot by tuning three impedance control parameters.

METHODS

The presented control law is based on an impedance controller with direct torque measurement, provided with positive-feedback compensation terms for disturbances rejection and gravity compensation. We developed an elbow flexion-extension experimental setup as a platform to validate the performance of the proposed controller to promote the desired high-level behavior. The controller was first characterized through experimental trials regarding joint transparency, torque, and impedance tracking accuracy. Then, to validate if the controller could effectively render different physical human-robot interaction according to the selected rehabilitation modalities, we conducted tests on 14 healthy volunteers and measured their muscular voluntary effort through surface electromyography (sEMG). The experiments consisted of one degree-of-freedom elbow flexion/extension movements, executed under six high-level modalities, characterized by different levels of (i) corrective assistance, (ii) weight counterbalance assistance, and (iii) resistance.

RESULTS

The unified controller demonstrated suitability to promote good transparency and render both compliant and stiff behavior at the joint. We demonstrated through electromyographic monitoring that a proper combination of stiffness, damping, and weight assistance could induce different user participation levels, render different physical human-robot interaction, and potentially promote different rehabilitation training modalities.

CONCLUSION

We proved that the proposed control framework could render a wide variety of physical human-robot interaction, helping the user to accomplish the task while exploiting physiological muscular activation patterns. The reported results confirmed that the control scheme could induce different levels of the subject's participation, potentially applicable to the clinical practice to adapt the rehabilitation treatment to the subject's progress. Further investigation is needed to validate the presented approach to neurological patients.

Review on Patient-Cooperative Control Strategies for Upper-Limb Rehabilitation Exoskeletons

Technology-supported rehabilitation therapy for neurological patients has gained increasing interest since the last decades. The literature agrees that the goal of robots should be to induce motor plasticity in subjects undergoing rehabilitation treatment by providing the patients with repetitive, intensive, and task-oriented treatment. As a key element, robot controllers should adapt to patients’ status and recovery stage. Thus, the design of effective training modalities and their hardware implementation play a crucial role in robot-assisted rehabilitation and strongly influence the treatment outcome. The objective of this paper is to provide a multi-disciplinary vision of patient-cooperative control strategies for upper-limb rehabilitation exoskeletons to help researchers bridge the gap between human motor control aspects, desired rehabilitation training modalities, and their hardware implementations. To this aim, we propose a three-level classification based on 1) “high-level” training modalities, 2) “low-level” control strategies, and 3) “hardware-level” implementation. Then, we provide examples of literature upper-limb exoskeletons to show how the three levels of implementation have been combined to obtain a given high-level behavior, which is specifically designed to promote motor relearning during the rehabilitation treatment. Finally, we emphasize the need for the development of compliant control strategies, based on the collaboration between the exoskeleton and the wearer, we report the key findings to promote the desired physical human-robot interaction for neurorehabilitation, and we provide insights and suggestions for future works.

A modulated template-matching approach to improve spike sorting of bursting neurons

In extracellular neural electrophysiology, individual spikes have to be assigned to their cell of origin in a procedure called "spike sorting". Spike sorting is an unsupervised problem, since no ground-truth information is generally available. Here, we focus on improving spike sorting performance, particularly during periods of high synchronous activity or so-called "bursting". Bursting entails systematic changes in spike shapes and amplitudes and remains a challenge for current spike sorting schemes. We use realistic simulated bursting recordings of high-density micro-electrode arrays (HD-MEAs) and we present a fully automated algorithm based on template matching with a focus on recovering missed spikes during bursts. To compare and benchmark spike-sorting performance after applying our method, we used ground-truth information of simulated recordings. We show that our approach can be effective in improving spike sorting performance during bursting. Further validation with experimental recordings is necessary.

Robotic Exoskeleton Gait Training in Stroke: An Electromyography-Based Evaluation

The recovery of symmetric and efficient walking is one of the key goals of a rehabilitation program in patients with stroke. The use of overground exoskeletons alongside conventional gait training might help foster rhythmic muscle activation in the gait cycle toward a more efficient gait. About twenty-nine patients with subacute stroke have been recruited and underwent either conventional gait training or experimental training, including overground gait training using a wearable powered exoskeleton alongside conventional therapy. Before and after the rehabilitation treatment, we assessed: (i) gait functionality by means of clinical scales combined to obtain a Capacity Score, and (ii) gait neuromuscular lower limbs pattern using superficial EMG signals. Both groups improved their ability to walk in terms of functional gait, as detected by the Capacity Score. However, only the group treated with the robotic exoskeleton regained a controlled rhythmic neuromuscular pattern in the proximal lower limb muscles, as observed by the muscular activation analysis. Coherence analysis suggested that the control group (CG) improvement was mediated mainly by spinal cord control, while experimental group improvements were mediated by cortical-driven control. In subacute stroke patients, we hypothesize that exoskeleton multijoint powered fine control overground gait training, alongside conventional care, may lead to a more fine-tuned and efficient gait pattern.

Adaptive Cooperative Control for Hybrid FES-Robotic Upper Limb Devices: a Simulation Study

Robotic systems and Functional Electrical Stimulation (FES) are common technologies exploited in motor rehabilitation. However, they present some limits. To overcome the weaknesses of both approaches, hybrid cooperative devices have been developed, which combine the action of the robot and that of the electrically stimulated muscles on the same joint. In this work, we present a novel adaptive cooperative controller for the rehabilitation of the upper limb. The controller comprises an allocator - which breaks down the reference torque between the motor and the FES a-priori contributions based on muscle fatigue estimation - an FES closed-loop controller, and an impedance control loop on the motor to correct trajectory tracking errors. The controller was tested in simulation environment reproducing elbow flexion/extension movements. Results showed that the controller could reduce motor torque requirements with respect to the motor-only case, at the expense of trajectory tracking performance. Moreover, it could improve fatigue management with respect to the FES-only case. In conclusion, the proposed control strategy provides a good trade-off between motor torque consumption and trajectory tracking performance, while the allocator manages fatigue-related phenomena.Clinical relevance-The use of allocation proves to be effective in both reducing motor torque and FES-induced muscle fatigue and might be an effective solution for hybrid FES-robotic systems.

Comparing Fatigue Reducing Stimulation Strategies During Cycling Induced by Functional Electrical Stimulation: a Case Study with one Spinal Cord Injured Subject

This case study was designed starting from our experience at CYBATHLON 2020. The specific aim of this work was to compare the effectiveness of different fatigue reducing stimulation strategies during cycling induced by Functional Electrical Stimulation (FES). The compared stimulation strategies were: traditional constant frequency trains (CFTs) at 30 and 40Hz, doublet frequency trains (DFTs) and spatially distributed sequential stimulation (SDSS) on the quadriceps muscles. One Spinal Cord Injured (SCI) subject (39 years, T5-T6, male, ASIA A) was involved in 12 experimental sessions during which the four strategies were tested in a randomized order during FES-induced cycling performed on a passive trike at a constant cadence of 35 RPM. FES was delivered to four muscle groups (quadriceps, gluteal muscles, hamstrings and gastrocnemius) for each leg. The performance was evaluated in terms of saturation time (i.e., the time elapsed from the beginning of the stimulation until the predetermined maximum value of current amplitude is reached) and root mean square error (RMSE) of the actual cadence with respect to the target value. SDSS achieved a statistical lower saturation time and a qualitative higher RMSE of the cadence with respect to CFTs both at 30 and 40Hz.Clinical relevance- Conversely to previous literature, SDSS seems to be ineffective to reduce muscle fatigue during FES-induced cycling. Further experiments are needed to confirm this result.

Design of a Robotic Coach for Motor, Social and Cognitive Skills Training Toward Applications With ASD Children

Socially assistive robots may help the treatment of autism spectrum disorder(ASD), through games using dyadic interactions to train social skills. Existing systems are mainly based on simplified protocols which qualitatively evaluate subject performance. We propose a robotic coaching platform for training social, motor and cognitive capabilities, with two main contributions: (i) using triadic interactions(adult, robot and child), with robotic mirroring, and (ii) providing quantitative performance indicators. The key system features were accurately designed, including type of protocols, feedback systems and evaluation metrics, contemplating the requirements for applications with ASD children. We implemented two protocols, Robot-Master and Adult-Master, where children performed different gestures guided by the robot or the adult respectively, eventually receiving feedback about movement execution. In both, the robot mirrors the subject during the movement. To assess system functionalities, with a homogeneous group of subjects, tests were carried out with 28 healthy subjects; one preliminary acquisition was done with an ASD child. Data analysis was customized to design protocol-specific parameters for movement characterization. Our tests show that robotic mirroring execution depends on the complexity and standardization of movements, as well as on the robot technical features. The feedback system evaluated movement phases and successfully estimated the completion of the exercises. Future work includes improving platform flexibility and adaptability, and clinical trials with ASD children to test the impact of the robotic coach on reducing symptoms. We trust that the proposed quantitative performance indicators extend the current state-of-the-art towards clinical usage of robotic-based coaching systems.

Resolvin E1 and Cytokines Environment in Skeletally Immature and Adult ACL Tears

The intra-articular synovial fluid environment in skeletally immature patients following an ACL tear is complex and remains undefined. Levels of inflammatory and anti-inflammatory cytokines change significantly in response to trauma and collectively define the inflammatory environment. Of these factors the resolvins, with their inherent anti-inflammatory, reparative, and analgesic properties, have become prominent. This study examined the levels of resolvins and other cytokines after ACL tears in skeletally immature and adult patients in order to determine if skeletal maturity affects the inflammatory pattern. Skeletally immature and adult patients with an anterior cruciate ligament injury and meniscal tears were prospectively enrolled over a 5-month period. Synovial fluid samples were obtained before surgery quantifying Resolvin E1, IL-1β, TNF-α, and IL-10 by ELISA. Comparisons between skeletally immature patients and adults, the influence of meniscal tear, growth plate maturity and time from trauma were analyzed. Skeletally immature patients had significantly greater levels of Resolvin E1 and IL-10 compared with adults with an isolated anterior cruciate ligament lesion. Among the injured skeletally immature patients Resolvin E1 levels were greater in the open growth plate group compared with those with closing growth plates. Moreover, levels of Resolvin E1 and IL-10 appeared to decrease with time. Our results suggest that skeletally immature patients have a stronger activation of the Resolvin pattern compared to adult patients and that synovial fluid Resolvins could play an antinflammatory role in the knee after anterior cruciate ligament lesion and that its activity may be synergistic with that of IL-10.

Brain Plasticity Mechanisms Underlying Motor Control Reorganization: Pilot Longitudinal Study on Post-Stroke Subjects

Functional Electrical Stimulation (FES) has demonstrated to improve walking ability and to induce the carryover effect, long-lasting persisting improvement. Functional magnetic resonance imaging has been used to investigate effective connectivity differences and longitudinal changes in a group of chronic stroke patients that attended a FES-based rehabilitation program for foot-drop correction, distinguishing between carryover effect responders and non-responders, and in comparison with a healthy control group. Bayesian hierarchical procedures were employed, involving nonlinear models at within-subject level-dynamic causal models-and linear models at between-subjects level. Selected regions of interest were primary sensorimotor cortices (M1, S1), supplementary motor area (SMA), and angular gyrus. Our results suggest the following: (i) The ability to correctly plan the movement and integrate proprioception information might be the features to update the motor control loop, towards the carryover effect, as indicated by the reduced sensitivity to proprioception input to S1 of FES non-responders; (ii) FES-related neural plasticity supports the active inference account for motor control, as indicated by the modulation of SMA and M1 connections to S1 area; (iii) SMA has a dual role of higher order motor processing unit responsible for complex movements, and a superintendence role in suppressing standard motor plans as external conditions changes.

A Robotic System with EMG-Triggered Functional Eletrical Stimulation for Restoring Arm Functions in Stroke Survivors

BACKGROUND

Robotic systems combined with Functional Electrical Stimulation (FES) showed promising results on upper-limb motor recovery after stroke, but adequately-sized randomized controlled trials (RCTs) are still missing.

OBJECTIVE

To evaluate whether arm training supported by RETRAINER, a passive exoskeleton integrated with electromyograph-triggered functional electrical stimulation, is superior to advanced conventional therapy (ACT) of equal intensity in the recovery of arm functions, dexterity, strength, activities of daily living, and quality of life after stroke.

METHODS

A single-blind RCT recruiting 72 patients was conducted. Patients, randomly allocated to 2 groups, were trained for 9 weeks, 3 times per week: the experimental group performed task-oriented exercises assisted by RETRAINER for 30 minutes plus ACT (60 minutes), whereas the control group performed only ACT (90 minutes). Patients were assessed before, soon after, and 1 month after the end of the intervention. Outcome measures were as follows: Action Research Arm Test (ARAT), Motricity Index, Motor Activity Log, Box and Blocks Test (BBT), Stroke Specific Quality of Life Scale (SSQoL), and Muscle Research Council.

RESULTS

All outcomes but SSQoL significantly improved over time in both groups (P < .001); a significant interaction effect in favor of the experimental group was found for ARAT and BBT. ARAT showed a between-group change of 11.5 points (P = .010) at the end of the intervention, which increased to 13.6 points 1 month after. Patients considered RETRAINER moderately usable (System Usability Score of 61.5 ± 22.8).

CONCLUSIONS

Hybrid robotic systems, allowing to perform personalized, intensive, and task-oriented training, with an enriched sensory feedback, was superior to ACT in improving arm functions and dexterity after stroke.

Abstract PO-065: Artificial intelligence to improve selection for NSCLC patients treated with immunotherapy

Introduction In advanced Non-Small Cell Lung Cancer (aNSCLC), Programmed Death Ligand 1 (PD-L1) remains the only used biomarker to candidate patients (pts) to immunotherapy (IO) even if its predictive accuracy is not satisfactory. Indeed, given the complex dynamics underlying the cross-talk between the tumor and its microenvironment, it is unlikely that a single biomarker could be able to profile prediction with high precision. Artificial Intelligence (AI) and machine learning (ML) are techniques able to analyze and interpret big data, which cope with this complexity. The present study aims at using AI tools to improve response and efficacy prediction in aNSCLC pts treated with IO. Methods A classification task to determine if a pt is likely to benefit from IO was formulated using complete clinical data, PD-L1, histology, molecular data, and the blood microRNA signature classifier (MSC), which include 24 different microRNAs. Pts were divided into responders (R), who obtained a partial response or stable disease as best response, and non-R, who experienced progressive disease. A forward feature selection technique based on the Akaike Information Criterion was used to extract a specific subset of the pts data, being the most informative ones for the task. To develop the final predictive model, different ML methods have been tested: K-nearest neighbors, Logistic Regression, Kernel Support Vector Machines, Feedforward Neural Network, and Random Forest. Results Of 164 enrolled pts, 73 (44.5%) were R and 91 (55.5%) non-R. At data cut-off (Nov 2020), median Overall Survival (mOS) was 10.1 (95%IC 7.0 - 13.2) months (m). mOS for R pts was 38.5 m (95%IC 23.9 - 53.1) vs 3.8 m (95%IC 2.8 - 4.7) of non-R, p&lt;0.001. Overall, the best model was the Logistic Regression and included 5 features (3 clinical, 1 tissue and 1 blood features): ECOG performance status, IO-line of therapy, the neutrophil-to-lymphocyte ratio (NLR), the MSC test and PD-L1 with the following corresponding parameters w= (0.692, 0.718, 1.058, 0.566, -0.471), respectively. The intercept of the model is w_0 = 0.467, and the model achieves a 75% accuracy, computed using a leave-one-out approach. PD-L1 alone has an accuracy of 65%. We also evaluated the accuracy of the models excluding PD-L1 (74% accuracy), MSC (73% accuracy), and excluding both PD-L1 and MSC considering only clinical features (71% accuracy). Conclusions The results suggest that the data integration provided by AI techniques is a powerful tool to improve personalized selection of pts candidates to IO. In particular, the model shows that higher ECOG, NLR value, IO-line, and MSC test level correlate negatively while higher PD-L1 correlates positively with the response. The model confirms PD-L1 and MSC as relevant biomarkers to improve the accuracy of pts response. Considering the difference in survival among R and non-R groups, these results suggest that the model can also be used to indirectly predict OS.

Citation Format: Arsela Prelaj, Mattia Boeri, Alessandro Robuschi, Claudia Proto, Giuseppe Lo Russo, Roberto Ferrara, Giulia Galli, Alessandro De Toma, Marta Brambilla, Mario Occhipinti, Sara Manglaviti, Alice Labianca, Teresa Beninato, Marta Bini, Mavis Mensah, Monica Ganzinelli, Nicoletta Zilembo, Filippo de Braud, Gabriella Sozzi, Marcello Restelli, Alessandra Pedrocchi, Marina Chiara Garassino, Francesco Trovo. Artificial intelligence to improve selection for NSCLC patients treated with immunotherapy [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-065.

User-centred assistive SystEm for arm Functions in neUromuscuLar subjects (USEFUL): a randomized controlled study

Background

Upper limb assistive devices can compensate for muscular weakness and empower the user in the execution of daily activities. Multiple devices have been recently proposed but there is still a lack in the scientific comparison of their efficacy.

Methods

We conducted a cross-over multi-centric randomized controlled trial to assess the functional improvement at the upper limb level of two arms supports on 36 patients with muscular dystrophy. Participants tested a passive device (i.e., Wrex by Jaeco) and a semi-active solution for gravity compensation (i.e., Armon Ayura). We evaluated devices’ effectiveness with an externally-assessed scale (i.e., Performance of the Upper Limb-PUL-module), a self-perceived scale (i.e., Abilhand questionnaire), and a usability scale (i.e., System Usability Scale). Friedman’s test was used to assess significant functional gain for PUL module and Abilhand questionnaire. Moreover, PUL changes were compared by means of the Friedman’s test.

Results

Most of the patients improved upper limb function with the use of arm supports (median PUL scores increase of 1–3 points). However, the effectiveness of each device was related to the level of residual ability of the end-user. Slightly impaired patients maintained the same independence without and with assistive devices, even if they reported reduced muscular fatigue for both devices. Moderately impaired patients enhanced their arm functionality with both devices, and they obtained higher improvements with the semi-active one (median PUL scores increase of 9 points). Finally, severely impaired subjects benefited only from the semi-active device (median PUL scores increase of 12 points). Inadequate strength was recognized as a barrier to passive devices. The usability, measured by the System Usability Scale, was evaluated by end-users “good” (70/100 points) for the passive, and “excellent” (80/100 points) for the semi-active device.

Conclusions

This study demonstrated that assistive devices can improve the quality of life of people suffering from muscular dystrophy. The use of passive devices, despite being low cost and easy to use, shows limitations in the efficacy of the assistance to daily tasks, limiting the assistance to a predefined horizontal plane. The addition of one active degree of freedom improves efficacy and usability especially for medium to severe patients. Further investigations are needed to increase the evidence on the effect of arm supports on quality of life and diseases’ progression in subjects with degenerative disorders.

Trial registration clinicaltrials.gov, NCT03127241, Registered 25th April 2017. The clinical trial was also registered as a post-market study at the Italian Ministry of Health.

IoT ink pen for ecological monitoring of daily life handwriting

The analysis of the writing gesture has been successfully investigated in the diagnosis of age-related diseases, but the current technologies and methods still do not allow the ecological daily monitoring of handwriting, mostly because they rely on standardized writing protocols. In this study, we first designed and validated a novel electronic ink pen, equipped with motion and writing force sensing, for the ecological daily-life monitoring of handwriting in uncontrolled environments. We used the pen to acquire writing activities from healthy adults, from which we computed useful handwriting and tremor indicators. We evaluated the reliability of our measurements by computing the intraclass correlation coefficients (ICC) and the minimal detectable changes (MDC). Moderate to excellent reliability were obtained for all the handwriting indicators computed in two different writing tasks. MDC values can be used as reference to discriminate a real change in the handwriting parameters from a measurement error in longitudinal studies. These results pave the way towards the use of the pen for daily life handwriting monitoring.

Test-retest reliability of the Performance of Upper Limb (PUL) module for muscular dystrophy patients

The Performance of the Upper Limb (PUL) module is an externally-assessed clinical scale, initially designed for the Duchenne muscular dystrophy population. It provides an upper extremity functional score suitable for both weaker ambulatory and non-ambulatory phases up to the severely impaired patients. It is capable of characterizing overall progression and severity of disease and of tracking the stereotypical proximal-to-distal progressive loss of upper limb function in muscular dystrophy. Since the PUL module has been validated only with Duchenne patients, its use also for Becker and Limb-Girdle muscular dystrophy patients has been here evaluated, to verify its reliability and extend its use. In particular, two different assessors performed this scale on 32 dystrophic subjects in two consecutive days. The results showed that the PUL module has high reliability, both absolute and relative, based on the calculation of Pearson's r (0.9942), Intraclass Correlation Coefficient (0.9943), Standard Error of Measurement (1.36), Minimum Detectable Change (3.77), and Coefficient of Variation (3%). The Minimum Detectable Change, in particular, can be used in clinical trials to perform a comprehensive longitudinal evaluation of the effects of interventions with the lapse of time. According to this analysis, an intervention is effective if the difference in the PUL score between subsequent evaluation points is equal or higher than 4 points; otherwise, the observed effect is not relevant. Inter-rater reliability with ten different assessors was evaluated, and it has been demonstrated that deviation from the mean is lower than calculated Minimum Detectable Change. The present work provides evidence that the PUL module is a reliable and valid instrument for measuring upper limb ability in people with different forms of muscular dystrophy. Therefore, the PUL module might be extended to other pathologies and reliably used in multicenter settings.

Does cycling induced by functional electrical stimulation enhance motor recovery in the subacute phase after stroke? A systematic review and meta-analysis

OBJECTIVE

To investigate the effects of cycling with functional electrical stimulation on walking, muscle power and tone, balance and activities of daily living in subacute stroke survivors.

DATA SOURCES

Ten electronic databases were searched from inception to February 2020.

REVIEW METHODS

Inclusion criteria were: subacute stroke survivors (<6 months since stroke), an experimental group performing any type of cycling training with electrical stimulation, alone or in addition to usual care, and a control group performing usual care alone. Two reviewers assessed eligibility, extracted data and analyzed the risks of bias. Standardized Mean Difference (SMD) or Mean Difference (MD) with 95% Confidence Intervals (CI) were estimated using fixed- or random-effects models to evaluate the training effect.

RESULTS

Seven randomized controlled trials recruiting a total of 273 stroke survivors were included in the meta-analyses. There was a statistically significant, but not clinically relevant, effect of cycling with electrical stimulation compared to usual care on walking (six studies, SMD [95% CI] = 0.40 [0.13, 0.67]; P = 0.004), capability to maintain a sitting position (three studies, MD [95% CI] = 7.92 [1.01, 14.82]; P = 0.02) and work produced by the paretic leg during pedaling (2 studies, MD [95% CI] = 8.13 [1.03, 15.25]; P = 0.02). No significant between-group differences were found for muscular power, tone, standing balance, and activities of daily living.

CONCLUSIONS

Cycling training with functional electrical stimulation cannot be recommended in terms of being better than usual care in subacute stroke survivors. Further investigations are required to confirm these results, to determine the optimal training parameters and to evaluate long-term effects.

Upper-limb actuated exoskeleton for muscular dystrophy patients: preliminary results

Being able to perform a lost movement is an important experience towards increased independence and self-esteem, particularly for neuromuscular patients, who see their muscles weaken day after day. In this pilot study, preliminary results on the testing of a motorized upper-limb exoskeleton for muscular dystrophy patients are presented. The mechatronic system is a five Degrees of Freedom exoskeleton, which acts at shoulder, elbow, and wrist levels. It is designed to help severely impaired people to regain independence during daily-life activities. While wearing the exoskeleton, the user has the direct control of the system by actively piloting the position of end-effector by means of joystick or vocal control. The usability of the system and a quantitative assessment of arm functionality with and without the exoskeleton are evaluated on five muscular dystrophy patients. According to the objective functional benefit evaluation performed through the PUL scale, all participants strongly increased their range of motion and they were able to perform activities that were not possible without the exoskeleton, such as such as feeding, playing activities at the table, combing hair or using a keyboard. As for the evaluation of self-perceived functional benefit, four patients reflected the effective measured functional improvement. System usability has been evaluated to be good.

Advanced Neurotechnologies for the Restoration of Motor Function

Stroke is one of the leading causes of long-term disability. Advanced technological solutions ("neurotechnologies") exploiting robotic systems and electrodes that stimulate the nervous system can increase the efficacy of stroke rehabilitation. Recent studies on these approaches have shown promising results. However, a paradigm shift in the development of new approaches must be made to significantly improve the clinical outcomes of neurotechnologies compared with those of traditional therapies. An "evolutionary" change can occur only by understanding in great detail the basic mechanisms of natural stroke recovery and technology-assisted neurorehabilitation. In this review, we first describe the results achieved by existing neurotechnologies and highlight their current limitations. In parallel, we summarize the data available on the mechanisms of recovery from electrophysiological, behavioral, and anatomical studies in humans and rodent models. Finally, we propose new approaches for the effective use of neurotechnologies in stroke survivors, as well as in people with other neurological disorders.

Changes in leg cycling muscle synergies after training augmented by functional electrical stimulation in subacute stroke survivors: a pilot study

Background

Muscle synergies analysis can provide a deep understanding of motor impairment after stroke and of changes after rehabilitation. In this study, the neuro-mechanical analysis of leg cycling was used to longitudinally investigate the motor recovery process coupled with cycling training augmented by Functional Electrical Stimulation (FES) in subacute stroke survivors.

Methods

Subjects with ischemic subacute stroke participated in a 3-week training of FES-cycling with visual biofeedback plus usual care. Participants were evaluated before and after the intervention through clinical scales, gait spatio-temporal parameters derived from an instrumented mat, and a voluntary pedaling test. Biomechanical metrics (work produced by the two legs, mechanical effectiveness and symmetry indexes) and bilateral electromyography from 9 leg muscles were acquired during the voluntary pedaling test. To extract muscles synergies, the Weighted Nonnegative Matrix Factorization algorithm was applied to the normalized EMG envelopes. Synergy complexity was measured by the number of synergies required to explain more than 90% of the total variance of the normalized EMG envelopes and variance accounted for by one synergy. Regardless the inter-subject differences in the number of extracted synergies, 4 synergies were extracted from each patient and the cosine-similarity between patients and healthy weight vectors was computed.

Results

Nine patients (median age of 75 years and median time post-stroke of 2 weeks) were recruited. Significant improvements in terms of clinical scales, gait parameters and work produced by the affected leg were obtained after training. Synergy complexity well correlated to the level of motor impairment at baseline, but it did not change after training. We found a significant improvement in the similarity of the synergy responsible of the knee flexion during the pulling phase of the pedaling cycle, which was the mostly compromised at baseline. This improvement may indicate the re-learning of a more physiological motor strategy.

Conclusions

Our findings support the use of the neuro-mechanical analysis of cycling as a method to assess motor recovery after stroke, mainly in an early phase, when gait evaluation is not yet possible. The improvement in the modular coordination of pedaling correlated with the improvement in motor functions and walking ability achieved at the end of the intervention support the role of FES-cycling in enhancing motor re-learning after stroke but need to be confirmed in a controlled study with a larger sample size.

Trial registration

ClinicalTrial.gov, NCT02439515. Registered on May 8, 2015, .

The Effectiveness of Wearable Upper Limb Assistive Devices in Degenerative Neuromuscular Diseases: A Systematic Review and Meta-Analysis

Background: This systematic review summarizes the current evidence about the effectiveness of wearable assistive technologies for upper limbs support during activities of daily living for individuals with neuromuscular diseases. Methods: Fourteen studies have been included in the meta-analysis, involving 184 participants. All included studies compared patients ability to perform functional tasks with and without assistive devices. Results: An overall effect size of 1.06 (95% CI = 0.76-1.36, p < 0.00001) was obtained, demonstrating that upper limbs assistive devices significantly improve the performance in activities of daily living in people with neuromuscular diseases. A significant interaction between studies evaluating functional improvement with externally-assessed outcome measures or self-perceived outcome measures has been detected. In particular, the effect size of the sub-group considering self-perceived scales was 1.38 (95% CI = 1.08-1.68), while the effect size of the other group was 0.77 (95% CI = 0.41-1.11), meaning that patients' perceived functional gain is often higher than the functional gain detectable through clinical scales. Conclusion: Overall, the quality of the evidence ranged from low to moderate, due to low number of studies and participants, limitations in the selection of participants and in the blindness of outcome assessors, and risk of publication bias. Significance: A large magnitude effect and a clear dose-response gradient were found, therefore, a strong recommendation, in favor of the use of assistive devices could be suggested.

Implementation of an Advanced Frequency-Based Hebbian Spike Timing Dependent Plasticity

The brain is provided with an enormous computing capability and exploits neural plasticity to store and elaborate complex information. One of the multiple mechanisms that neural circuits express is the Spike-timing-dependent plasticity (STDP), a form of long-term synaptic plasticity exploiting the time relationship between pre- and post-synaptic action potentials (i.e., neuron spikes). It has been found that in certain cases, for instance at the input stage of the cerebellum, between mossy fibers and granular neurons, the plasticity is not only driven by the timing of the spikes, but also by the oscillation frequency of the inputs. This complex behaviour has been implemented in this work, where we developed a novel form of advanced synaptic plasticity model to be used in a well-established neural network simulator (NEST). The subsequent tests proved the proper functioning of the plasticity and its range of applicability, demonstrating the possibility to adopt it in noisy and variable conditions, similar to the biological settings.