Utility and usability of a wearable system and progressive-challenge cued exercise program for encouraging use of the more involved arm at-home after stroke-a feasibility study with case reports.

BACKGROUND: Understanding the role of adherence to home exercise programs for survivors of stroke is critical to ensure patients perform prescribed exercises and maximize effectiveness of recovery. METHODS: Survivors of hemiparetic stroke with impaired motor function were recruited into a 7-day study designed to test the utility and usability of a low-cost wearable system and progressive-challenge cued exercise program for encouraging graded-challenge exercise at-home. The wearable system comprised two wrist-worn MetaMotionR+ activity monitors and a custom smartphone app. The progressive-challenge cued exercise program included high-intensity activities (one repetition every 30 s) dosed at 1.5 h per day, embedded within 8 h of passive activity monitoring per day. Utility was assessed using measures of system uptime and cue response rate. Usability and user experience were assessed using well-validated quantitative surveys of system usability and user experience. Self-efficacy was assessed at the end of each day on a visual analog scale that ranged from 0 to 100. RESULTS: The system and exercise program had objective utility: system uptime was 92 ± 6.9% of intended hours and the rate of successful cue delivery was 99 ± 2.7%. The system and program also were effective in motivating cued exercise: activity was detected within 5-s of the cue 98 ± 3.1% of the time. As shown via two case studies, accelerometry data can accurately reflect graded-challenge exercise instructions and reveal differentiable activity levels across exercise stages. User experience surveys indicated positive overall usability in the home settings, strong levels of personal motivation to use the system, and high degrees of satisfaction with the devices and provided training. Self-efficacy assessments indicated a strong perception of proficiency across participants (95 ± 5.0). CONCLUSIONS: This study demonstrates that a low-cost wearable system providing frequent haptic cues to encourage graded-challenge exercise after stroke can have utility and can provide an overall positive user experience in home settings. The study also demonstrates how combining a graded exercise program with all-day activity monitoring can provide insight into the potential for wearable systems to assess adherence to-and effectiveness of-home-based exercise programs on an individualized basis.

Soft Robotics to Enhance Upper Limb Endurance in Individuals with Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic autoimmune disorder that affects the central nervous system and can result in various symptoms, including muscle weakness, spasticity, and fatigue, ultimately leading to the deterioration of the musculoskeletal system. However, in recent years, exosuits have emerged as a game-changing solution to assist individuals with MS during their daily activities. These lightweight and affordable wearable robotic devices have gained immense popularity. In our study, we assessed the performance of an elbow exosuit on eight individuals with MS using high-density electromyography to measure biceps muscle activity. The results demonstrated that our prototype significantly reduced muscle effort during both dynamic and isometric tasks while increasing the elbow range of motion. In addition, the exosuit effectively delayed the onset of muscle fatigue, enhancing endurance for people with MS and enabling them to perform heavy duty tasks for a longer period.

RiNeo MR: A mixed reality simulator for newborn life support training.

Neonatal resuscitation is an uncommon, albeit critical task that is more likely to succeed if performed properly and promptly. In this context, simulation is an appropriate way for training and assessing the abilities of all medical staff involved in delivery room care. Recent studies have shown that learning is enhanced if the simulation experience is realistic and engaging. Hence, Virtual Reality can be beneficial for newborn resuscitation training. However, the difficulty of providing realistic haptic interaction limits its use. To overcome this constraint, we have designed RiNeo MR, a simulator for newborn life support training, combining a sensorized manikin to monitor in real time resuscitation skills, with a Virtual Reality application. The system includes a Virtual Reality headset, Leap Motion to track the user's hands, sensorized bag valve mask, and manikin to monitor head and mask positioning, ventilation, and chest compression. RiNeo MR can be used in two modalities: 2D to let the trainee practice resuscitation manoeuvres on the physical manikin, while receiving real time feedback; 3D that allows the user to be immersed in a virtual environment and practice in an hospital-like setting. In the 3D mode, virtual and real manikins are overlapped and communicate in real time. Tests on 16 subjects (11 controls without medical expertise and 5 paediatric residents) demonstrated that the simulator is well tolerated in terms of discomfort. Moreover, the simulator is high rated for user experience and system usability, suggesting that RiNeo MR can be a promising tool to improve newborn life support training. RiNeo MR is a proof of concept of a mixed-reality newborn life support simulator that can be a promising tool to spread newborn resuscitation high-quality training among healthcare providers involved in perinatal medicine.

A Computer Interface Controlled by Upper Limb Muscles: Effects of a Two Weeks Training on Younger and Older Adults.

As the population worldwide ages, there is a growing need for assistive technology and effective human-machine interfaces to address the wider range of motor disabilities that older adults may experience. Motor disabilities can make it difficult for individuals to perform basic daily tasks, such as getting dressed, preparing meals, or using a computer. The goal of this study was to investigate the effect of two weeks of training with a myoelectric computer interface (MCI) on motor functions in younger and older adults. Twenty people were recruited in the study: thirteen younger (range: 22-35 years old) and seven older (range: 61-78 years old) adults. Participants completed six training sessions of about 2 hours each, during which the activity of right and left biceps and trapezius were mapped into a control signal for the cursor of a computer. Results highlighted significant improvements in cursor control, and therefore in muscle coordination, in both groups. All participants with training became faster and more accurate, although people in different age range learned with a different dynamic. Results of the questionnaire on system usability and quality highlighted a general consensus about easiness of use and intuitiveness. These findings suggest that the proposed MCI training can be a powerful tool in the framework of assistive technologies for both younger and older adults. Further research is needed to determine the optimal duration and intensity of MCI training for different age groups and to investigate long-term effects of training on physical and cognitive function.

Clinical, Kinematic and Muscle Assessment of Bilateral Coordinated Upper-Limb Movements Following Cervical Spinal Cord Injury.

Cervical spinal cord injury (cSCI) often results in bilateral impairment of the arms, leading to difficulties in performing daily activities. However, little is known about the neuromotor alterations that affect the ability of individuals with cSCI to perform coordinated movements with both arms. To address this issue, we developed and tested a functional assessment that integrates clinical, kinematic, and muscle activity measures, including the evaluation of bilateral arm movements. Twelve subjects with a C5-C7 spinal lesion and six unimpaired subjects underwent an evaluation that included three tests: the Manual Muscle Test, Range Of Motion test and Arm stabilisation test, a subsection of the "Van Lieshout arm/hand function test". During the latter, we recorded kinematic and muscle activity data from the upper-body during the execution of a set of movements that required participants to stabilize both arms against gravity at different configurations. Analytical methods, including muscle synergies, spinal maps, and Principal Component Analysis, were used to analyse the data. Clinical tests detected limitations in shoulder abduction-flexion of cSCI participants and alterations in elbows-wrists motor function. The instrumented assessment provided insight into how these limitations impacted the ability of cSCI participants to perform bilateral movements. They exhibited severe difficulty in performing movements involving over-the-shoulder motion and shoulder internal rotation due to altered patterns of activity of the scapular stabilizer muscles, latissimus dorsi, pectoralis, and triceps. Our findings shed light on the bilateral neuromotor changes that occur post-cSCI addressing not only motor deficits, but also the underlying abnormal, weak, or silent muscle activations.

Bimanual Motor Strategies and Handedness Role in Human-Robot Haptic Interaction.

Bimanual object manipulation involves using both hands to interact with objects in the environment, and the process requires the central nervous system to process sensory feedback and translate it into motor commands. Although there have been significant advancements in haptics and robotics, the kinematic strategies involved in bimanual coupled tasks are still not fully understood. This study aimed to investigate the dynamic interaction between hands during the manipulation of a shared object using two impedance-controlled exoskeletons programmed to simulate bimanual coupled manipulation of virtual objects. Twenty-six participants (right-handed and left-handed) were asked to use both hands to grab and place simulated objects in specific locations. The virtual objects were rendered with four different dynamic properties, affecting the manipulation strategies used to complete the tasks. The results showed that force asymmetries were related to movement direction and handedness preference, with right-handers exhibiting asymmetries related to movement direction and left-handers showing better control of the force applied between their hands. This is possibly due to their constant exposure to objects designed for right-handed use. Additionally, the haptic properties of the virtual objects influenced task performance in terms of timing and failure for all participants. This study demonstrates the potential of advanced technologies to provide realistic simulations of multi-joint movements involving the entire upper extremities. The findings have implications for the development of training programs for bimanual object manipulation tasks and the design of virtual environments that can enhance the learning process.

Learning to operate a high-dimensional hand via a low-dimensional controller.

Dimensionality reduction techniques have proven useful in simplifying complex hand kinematics. They may allow for a low-dimensional kinematic or myoelectric interface to be used to control a high-dimensional hand. Controlling a high-dimensional hand, however, is difficult to learn since the relationship between the low-dimensional controls and the high-dimensional system can be hard to perceive. In this manuscript, we explore how training practices that make this relationship more explicit can aid learning. We outline three studies that explore different factors which affect learning of an autoencoder-based controller, in which a user is able to operate a high-dimensional virtual hand via a low-dimensional control space. We compare computer mouse and myoelectric control as one factor contributing to learning difficulty. We also compare training paradigms in which the dimensionality of the training task matched or did not match the true dimensionality of the low-dimensional controller (both 2D). The training paradigms were a) a full-dimensional task, in which the user was unaware of the underlying controller dimensionality, b) an implicit 2D training, which allowed the user to practice on a simple 2D reaching task before attempting the full-dimensional one, without establishing an explicit connection between the two, and c) an explicit 2D training, during which the user was able to observe the relationship between their 2D movements and the higher-dimensional hand. We found that operating a myoelectric interface did not pose a big challenge to learning the low-dimensional controller and was not the main reason for the poor performance. Implicit 2D training was found to be as good, but not better, as training directly on the high-dimensional hand. What truly aided the user's ability to learn the controller was the 2D training that established an explicit connection between the low-dimensional control space and the high-dimensional hand movements.

A Non-Linear Body Machine Interface for Controlling Assistive Robotic Arms.

OBJECTIVE: Body machine interfaces (BoMIs) enable individuals with paralysis to achieve a greater measure of independence in daily activities by assisting the control of devices such as robotic manipulators. The first BoMIs relied on Principal Component Analysis (PCA) to extract a lower dimensional control space from information in voluntary movement signals. Despite its widespread use, PCA might not be suited for controlling devices with a large number of degrees of freedom, as because of PCs' orthonormality the variance explained by successive components drops sharply after the first. METHODS: Here, we propose an alternative BoMI based on non-linear autoencoder (AE) networks that mapped arm kinematic signals into joint angles of a 4D virtual robotic manipulator. First, we performed a validation procedure that aimed at selecting an AE structure that would allow to distribute the input variance uniformly across the dimensions of the control space. Then, we assessed the users' proficiency practicing a 3D reaching task by operating the robot with the validated AE. RESULTS: All participants managed to acquire an adequate level of skill when operating the 4D robot. Moreover, they retained the performance across two non-consecutive days of training. CONCLUSION: While providing users with a fully continuous control of the robot, the entirely unsupervised nature of our approach makes it ideal for applications in a clinical context since it can be tailored to each user's residual movements. SIGNIFICANCE: We consider these findings as supporting a future implementation of our interface as an assistive tool for people with motor impairments.

Automatic video analysis and classification of sleep-related hypermotor seizures and disorders of arousal.

OBJECTIVE: Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy with seizures occurring mostly during sleep. SHE seizures present different motor characteristics ranging from dystonic posturing to hyperkinetic motor patterns, sometimes associated with affective symptoms and complex behaviors. Disorders of arousal (DOA) are sleep disorders with paroxysmal episodes that may present analogies with SHE seizures. Accurate interpretation of the different SHE patterns and their differentiation from DOA manifestations can be difficult and expensive, and can require highly skilled personnel not always available. Furthermore, it is operator dependent. METHODS: Common techniques for human motion analysis, such as wearable sensors (e.g., accelerometers) and motion capture systems, have been considered to overcome these problems. Unfortunately, these systems are cumbersome and they require trained personnel for marker and sensor positioning, limiting their use in the epilepsy domain. To overcome these problems, recently significant effort has been spent in studying automatic methods based on video analysis for the characterization of human motion. Systems based on computer vision and deep learning have been exploited in many fields, but epilepsy has received limited attention. RESULTS: In this paper, we present a pipeline composed of a set of three-dimensional convolutional neural networks that, starting from video recordings, reached an overall accuracy of 80% in the classification of different SHE semiology patterns and DOA. SIGNIFICANCE: The preliminary results obtained in this study highlight that our deep learning pipeline could be used by physicians as a tool to support them in the differential diagnosis of the different patterns of SHE and DOA, and encourage further investigation.

Extended training improves the accuracy and efficiency of goal-directed reaching guided by supplemental kinesthetic vibrotactile feedback.

Prior studies have shown that the accuracy and efficiency of reaching can be improved using novel sensory interfaces to apply task-specific vibrotactile feedback (VTF) during movement. However, those studies have typically evaluated performance after less than 1 h of training using VTF. Here, we tested the effects of extended training using a specific form of vibrotactile cues-supplemental kinesthetic VTF-on the accuracy and temporal efficiency of goal-directed reaching. Healthy young adults performed planar reaching with VTF encoding of the moving hand's instantaneous position, applied to the non-moving arm. We compared target capture errors and movement times before, during, and after approximately 10 h (20 sessions) of training on the VTF-guided reaching task. Initial performance of VTF-guided reaching showed that people were able to use supplemental VTF to improve reaching accuracy. Performance improvements were retained from one training session to the next. After 20 sessions of training, the accuracy and temporal efficiency of VTF-guided reaching were equivalent to or better than reaches performed with only proprioception. However, hand paths during VTF-guided reaching exhibited a persistent strategy where movements were decomposed into discrete sub-movements along the cardinal axes of the VTF display. We also used a dual-task condition to assess the extent to which performance gains in VTF-guided reaching resist dual-task interference. Dual-tasking capability improved over the 20 sessions, such that the primary VTF-guided reaching and a secondary choice reaction time task were performed with increasing concurrency. Thus, VTF-guided reaching is a learnable skill in young adults, who can achieve levels of accuracy and temporal efficiency equaling or exceeding those observed during movements guided only by proprioception. Future studies are warranted to explore learnability in older adults and patients with proprioceptive deficits, who might benefit from using wearable sensory augmentation technologies to enhance control of arm movements.

A markerless pipeline to analyze spontaneous movements of preterm infants.

BACKGROUND AND OBJECTIVE: The analysis of spontaneous movements of preterm infants is important because anomalous motion patterns can be a sign of neurological disorders caused by lesions in the developing brain. A diagnosis in the first weeks of child's life is crucial to plan timely and appropriate rehabilitative interventions. An accurate visual assessment of infants' spontaneous movements requires highly specialized personnel, not always available, and it is operator dependent. Motion capture systems, markers and wearable sensors are commonly used for human motion analysis, but they can be cumbersome, limiting their use in the study of infants' movements. METHODS: In this paper we propose a computer-aided pipeline to characterize and classify infants' motion from 2D video recordings. The final goal is detecting anomalous motion patterns. The implemented pipeline is based on computer vision and machine learning algorithms and includes a specific step to increase the interpretability of the results. Specifically, it can be summarized by the following steps: (i) body keypoints detection: we rely on a deep learning-based semantic features detector to localize the positions of meaningful landmark points on infants' bodies; (ii) parameters extraction: starting from the trajectories of the detected landmark points, we extract quantitative parameters describing infants motion patterns; (iii) classification: we implement different classifiers (Support Vector Machines, Random Forest, fully connected Neural Network, Long Short Term Memory) that, starting from the motion parameters, classify between normal or abnormal motion patterns. RESULTS: We tested the proposed pipeline on a dataset, recorded at the 40th gestational week, of 142 infants, 59 with evidence of neuromotor disorders according to a medical assessment carried out a posteriori. Our procedure successfully discriminates normal and anomalous motion patterns with a maximum accuracy of 85.7%. CONCLUSIONS: In conclusion, our pipeline has the potential to be adopted as a tool to support the early detection of abnormal motion patterns in preterm infants.

Upper Limb Sensory-Motor Control During Exposure to Different Mechanical Environments in Multiple Sclerosis Subjects With No Clinical Disability.

Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease resulting in motor impairments associated with muscle weakness and lack of movement coordination. The goal of this work was to quantify upper limb motor deficits in asymptomatic MS subjects with a robot-based assessment including performance and muscle synergies analysis. A total of 7 subjects (MS: 3 M-4 F; 42 ± 10 years) with clinically definite MS according to McDonald criteria, but with no clinical disability, and 7 age- and sex-matched subjects without a history of neurological disorders participated in the study. All subjects controlled a cursor on the computer screen by moving their hand or applying forces in 8 coplanar directions at their self-selected speed. They grasped the handle of a robotic planar manipulandum that generated four different environments: null, assistive or resistive forces, and rigid constraint. Simultaneously, the activity of 15 upper body muscles was recorded. Asymptomatic MS subjects generated less smooth and less accurate cursor trajectories than control subjects in controlling a force profile, while the end-point error was significantly different also in the other environments. The EMG analysis revealed different muscle activation patterns in MS subjects when exerting isometric forces or when moving in presence of external forces generated by a robot. While the two populations had the same number and similar structure of muscle synergies, they had different activation profiles. These results suggested that a task requiring to control forces against a rigid environment allows better than movement tasks to detect early sensory-motor signs related to the onset of symptoms of multiple sclerosis and to differentiate between stages of the disease.

Viewpoint: Virtual and Augmented Reality in Basic and Advanced Life Support Training.

The use of augmented reality (AR) and virtual reality (VR) for life support training is increasing. These technologies provide an immersive experience that supports learning in a safe and controlled environment. This review focuses on the use of AR and VR for emergency care training for health care providers, medical students, and nonprofessionals. In particular, we analyzed (1) serious games, nonimmersive games, both single-player and multiplayer; (2) VR tools ranging from semi-immersive to immersive virtual and mixed reality; and (3) AR applications. All the toolkits have been investigated in terms of application goals (training, assessment, or both), simulated procedures, and skills. The main goal of this work is to summarize and organize the findings of studies coming from multiple research areas in order to make them accessible to all the professionals involved in medical simulation. The analysis of the state-of-the-art technologies reveals that tools and studies related to the multiplayer experience, haptic feedback, and evaluation of user's manual skills in the foregoing health care-related environments are still limited and require further investigation. Also, there is an additional need to conduct studies aimed at assessing whether AR/VR-based systems are superior or, at the minimum, comparable to traditional training methods.

A Lifespan Approach to Balance in Static and Dynamic Conditions: The Effect of Age on Balance Abilities.

Postural control is a complex sensorimotor skill that is fundamental to our daily life. The abilities to maintain and recover balance degrade with age. However, the time decay of balance performance with age is not well understood. In this study, we aim at quantifying the age-dependent changes in standing balance under static and dynamic conditions. We tested 272 healthy subjects with ages ranging from 20 to 90. Subjects maintained the upright posture while standing on the robotic platform hunova®. In the evaluation of static balance, subjects stood on the fixed platform both with eyes open (EO) and eyes closed (EC). In the dynamic condition, subjects stood with eyes open on the moving foot platform that provided three different perturbations: (i) an inclination proportional to the center of pressure displacements, (ii) a pre-defined predictable motion, and (iii) an unpredictable and unexpected tilt. During all these tests, hunova® measured the inclination of the platform and the displacement of the center of pressure, while the trunk movements were recorded with an accelerometer placed on the sternum. To quantify balance performance, we computed spatio-temporal parameters typically used in clinical environments from the acceleration measures: mean velocity, variability of trunk motion, and trunk sway area. All subjects successfully completed all the proposed exercises. Their motor performance in the dynamic balance tasks quadratically changed with age. Also, we found that the reliance on visual feedback is not age-dependent in static conditions. All subjects well-tolerated the proposed protocol independently of their age without experiencing fatigue as we chose the timing of the evaluations based on clinical needs and routines. Thus, this study is a starting point for the definition of robot-based assessment protocols aiming at detecting the onset of age-related standing balance deficits and allowing the planning of tailored rehabilitation protocols to prevent falls in older adults.

Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study.

The analysis of human gait is an important tool in medicine and rehabilitation to evaluate the effects and the progression of neurological diseases resulting in neuromotor disorders. In these fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems and markers. However, these systems present drawbacks: they are expensive, time consuming and they can affect the naturalness of the motion. For these reasons, in the last few years, considerable effort has been spent to study and implement markerless systems based on videography for gait analysis. Unfortunately, only few studies quantitatively compare the differences between markerless and marker-based systems in 3D settings. This work presented a new RGB video-based markerless system leveraging computer vision and deep learning to perform 3D gait analysis. These results were compared with those obtained by a marker-based motion capture system. To this end, we acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking in an indoor environment. With the two methods we obtained similar spatio-temporal parameters. The joint angles were comparable, except for a slight underestimation of the maximum flexion for ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless technique for gait analysis.

RiNeo MR: A mixed-reality tool for newborn life support training.

The first minute of life, the Golden Minute, has been defined as a critical window in which fundamental physiological processes occur for establishing spontaneous ventilation in a newborn. Resuscitation is more likely to succeed if it is performed properly and at the right time. In this scenario, simulation is an appropriate tool for training and evaluating the abilities of all staff working in the delivery room, as well as students. As simulations require a high degree of immersivity in order to be effective, the use of technologies like Virtual (VR) and mixed reality (MR) have garnered more interest in training. Currently, some VR and MR applications have been developed for adult life support training, but neonatal tools are still missing. To overcome this limitation, we present RiNeo MR, a prototype of a MR simulator for neonatal resuscitation training. The simulator consists of (i) a sensorized physical model of the newborn that allows monitoring chest compressions; (ii) a VR head mounted display that allows visualizing a virtual 3D model of the manikin and scenarios of the delivery and operating rooms. This enables students, and healthcare providers to be immersed in realistic hospital settings while performing life support procedures on the newborn manikin. Clinical Relevance-The newborn life support training (NLS) in facilities reduces term intrapartum-related deaths by 30%.

Motor Memory Consolidation after Augmented Variability Depends on the Space in which Variability is Introduced.

Motor memories undergo a period of consolidation before they become resistant to the practice of another task. Although movement variability is important in motor memory consolidation, its role is not fully understood in redundant tasks where variability can exist along two orthogonal subspaces (the 'task space' and the 'null space') that have different effects on task performance. Here, we used haptic perturbations to augment variability in these different spaces and examined their effect on motor memory consolidation. Participants learned a shuffleboard task, where they held a bimanual manipulandum and made a discrete throwing motion to slide a virtual puck towards a target. The task was redundant because the distance travelled by the puck was determined by the sum of the left and right hand speeds at release. After participants practiced the task, we used haptic perturbations to introduce motor variability in the task space or null space and examined consolidation of the original task on the next day. We found that regardless of the amplitude, augmenting variability in the task space resulted in significantly better consolidation relative to augmenting variability in the null space, but was not different from a control group that practiced with no variability. This benefit of increasing task space variability relative to increasing null space variability was likely due to the fact that it did not disrupt the pre-existing coordination strategy. These results suggest that the effects of variability on motor memory consolidation depend on the interplay between the induced variability and the pre-existing coordination strategy.

Stroke impairs the control of isometric forces and muscle activations in the ipsilesional arm.

Stroke often impairs the control of the contralesional arm, thus most survivors rely on the ipsilesional arm to perform daily living activities that require an efficient control of movements and forces. Whereas the ipsilesional arm is often called 'unaffected' or 'unimpaired', several studies suggested that during dynamic tasks its kinematics and joint torques are altered. Is stroke also affecting the ability of the ipsilesional arm to produce isometric force, as when pushing or pulling a handle? Here, we address this question by analyzing behavioral performance and muscles' activity when subjects applied an isometric force of 10 N in eight coplanar directions. We found that stroke affected the ability to apply well-controlled isometric forces with the ipsilesional arm, although to a minor extent compared to the contralesional arm. The spinal maps, the analysis of single muscle activities and the organization of muscle synergies highlighted that this effect was mainly associated with abnormal activity of proximal muscles with respect to matched controls, especially when pushing or pulling in lateral directions.

Effects of Hemispheric Stroke Localization on the Reorganization of Arm Movements within Different Mechanical Environments.

This study investigated how stroke's hemispheric localization affects motor performance, spinal maps and muscle synergies while performing planar reaching with and without assistive or resistive forces. A lesion of the right hemisphere affected performance, reducing average speed and smoothness and augmenting lateral deviation in both arms. Instead, a lesion of the left hemisphere affected the aiming error, impairing the feedforward control of the ipsilesional arm. The structure of the muscle synergies had alterations dependent on the lesion side in both arms. The applied force fields reduced the differences in performance and in muscle activations between arms and among populations. These results support the hypotheses of hemispheric specialization in movement control and identify potential significant biomarkers for the design of more effective and personalized rehabilitation protocols.

Recovery of Distal Arm Movements in Spinal Cord Injured Patients with a Body-Machine Interface: A Proof-of-Concept Study.

BACKGROUND: The recovery of upper limb mobility and functions is essential for people with cervical spinal cord injuries (cSCI) to maximize independence in daily activities and ensure a successful return to normality. The rehabilitative path should include a thorough neuromotor evaluation and personalized treatments aimed at recovering motor functions. Body-machine interfaces (BoMI) have been proven to be capable of harnessing residual joint motions to control objects like computer cursors and virtual or physical wheelchairs and to promote motor recovery. However, their therapeutic application has still been limited to shoulder movements. Here, we expanded the use of BoMI to promote the whole arm's mobility, with a special focus on elbow movements. We also developed an instrumented evaluation test and a set of kinematic indicators for assessing residual abilities and recovery. METHODS: Five inpatient cSCI subjects (four acute, one chronic) participated in a BoMI treatment complementary to their standard rehabilitative routine. The subjects wore a BoMI with sensors placed on both proximal and distal arm districts and practiced for 5 weeks. The BoMI was programmed to promote symmetry between right and left arms use and the forearms' mobility while playing games. To evaluate the effectiveness of the treatment, the subjects' kinematics were recorded while performing an evaluation test that involved functional bilateral arms movements, before, at the end, and three months after training. RESULTS: At the end of the training, all subjects learned to efficiently use the interface despite being compelled by it to engage their most impaired movements. The subjects completed the training with bilateral symmetry in body recruitment, already present at the end of the familiarization, and they increased the forearm activity. The instrumental evaluation confirmed this. The elbow motion's angular amplitude improved for all subjects, and other kinematic parameters showed a trend towards the normality range. CONCLUSION: The outcomes are preliminary evidence supporting the efficacy of the proposed BoMI as a rehabilitation tool to be considered for clinical practice. It also suggests an instrumental evaluation protocol and a set of indicators to assess and evaluate motor impairment and recovery in cSCI.