A direct spinal cord-computer interface enables the control of the paralysed hand in spinal cord injury.

The paralysis of the muscles controlling the hand dramatically limits the quality of life of individuals living with spinal cord injury (SCI). Here, with a non-invasive neural interface, we demonstrate that eight motor complete SCI individuals (C5-C6) are still able to task-modulate in real-time the activity of populations of spinal motor neurons with residual neural pathways. In all SCI participants tested, we identified groups of motor units under voluntary control that encoded various hand movements. The motor unit discharges were mapped into more than 10 degrees of freedom, ranging from grasping to individual hand-digit flexion and extension. We then mapped the neural dynamics into a real-time controlled virtual hand. The SCI participants were able to match the cue hand posture by proportionally controlling four degrees of freedom (opening and closing the hand and index flexion/extension). These results demonstrate that wearable muscle sensors provide access to spared motor neurons that are fully under voluntary control in complete cervical SCI individuals. This non-invasive neural interface allows the investigation of motor neuron changes after the injury and has the potential to promote movement restoration when integrated with assistive devices.

Human-in-the-Loop Assessment of an Ultralight, Low-Cost Body Posture Tracking Device.

In rehabilitation, assistive and space robotics, the capability to track the body posture of a user in real time is highly desirable. In more specific cases, such as teleoperated extra-vehicular activity, prosthetics and home service robotics, the ideal posture-tracking device must also be wearable, light and low-power, while still enforcing the best possible accuracy. Additionally, the device must be targeted at effective human-machine interaction. In this paper, we present and test such a device based upon commercial inertial measurement units: it weighs 575 grams in total, lasts up to 10.5 hours of continual operation, can be donned and doffed in under a minute and costs less than 290 EUR. We assess the attainable performance in terms of error in an online trajectory-tracking task in Virtual Reality using the device through an experiment involving 10 subjects, showing that an average user can attain a precision of 0.66 cm during a static precision task and 6.33 cm while tracking a moving trajectory, when tested in the full peri-personal space of a user.

Robot-Inspired Human Impedance Control Through Functional Electrical Stimulation.

Functional Electrical Stimulation is an effective tool to foster rehabilitation of neurological patients suffering from impaired motor functions. It can also serve as an assistive device to compensate for compromised motor functions in the chronic phase occurring after a disease or trauma. In all cases, the dominant paradigm in FES applications is that of aiding specialized, task-specific movements, such as reaching or grasping. Usually this is achieved by targeting specific muscle groups which are associated to the targeted motion by experts. A general purpose, FES-based control theory capable of enabling neurological patients to achieve a wide range of positional goals in their peri-personal space is still missing. In this paper, we present an early analysis of the performance achievable through a muscular impedance control loop employing FES to actuate force and movement. The control is evaluated in a test where the user's upper limb is moved by means of an exonerve to a series of target positions on a plane without providing visual feedback nor requiring volitional effort. The results allow to characterize the performance of such a setup over time and to assess how well can it generalize over different target positions in the user's peri-personal space. The current study population also allows to evaluate the effects of user's experience with FES systems on the overall performance during the test. The results indicate that the proposed control loop can generalize well over different arm poses.

Omnidirectional endpoint force control through Functional Electrical Stimulation.

OBJECTIVE: in recent years, Functional Electrical Stimulation has found many applications both within and outside the medical field. However, most available wearable FES devices are not easily adaptable to different users, and most setups rely on task-specific control schemes. APPROACH: in this article, we present a peripheral stimulation prototype featuring a compressive jacket which allows to easily modify the electrode arrangement to better fit any body frame. Coupled with a suitable control system, this device can induce the output of arbitrary forces at the end-effector, which is the basis to facilitate universal, task-independent impedance control of the human limbs. Here, the device is validated by having it provide stimulation currents that should induce a desired force output. The forces exerted by the user as a result of stimulation are measured through a 6-axis force-torque sensor, and compared to the desired forces. Furthermore, here we present the offline analysis of a regression algorithm, trained on the data acquired during the aforementioned validation, which is able to reliably predict the force output based on the stimulation currents. MAIN RESULTS: open-loop control of the output force is possible with correlation coefficients between commanded and measured force output direction up to 0.88. A twitch-based calibration procedure shows significant reduction of the RMS error in the online control. The regression algorithm trained offline is able to predict the force output given the injected stimulation with correlations up to 0.94, and average normalized errors of 0.12 RMS. SIGNIFICANCE: A reliable force output control through FES is the first basis towards higher-level FES force controls. This could eventually provide full, general-purpose control of the human neuromuscular system, which would allow to induce any desired movement in the peri-personal space in individuals affected by e.g. spinal cord injury.

Adaptive Filter for Biosignal-Driven Force Controls Preserves Predictive Powers of sEMG.

Electromyographic controls based on machine learning rely on the stability and repeatability of signals related to muscular activity. However, such algorithms are prone to several issues, making them non-viable in certain applications with low tolerances for delays and signal instability, such as exoskeleton control or teleimpedance. These issues can become dramatic whenever, e.g., muscular activity is present not only when the user is trying to move but also for mere gravity compensation, which generally becomes more prominent the more proximal a muscle is. A substantial part of this instability is attributed to electromyography's inherent heteroscedasticity. In this study, we introduce and characterize an adaptive filter for sEMG features in such applications, which automatically adjusts its own cutoff frequency to suit the current movement intention. The adaptive filter is tested offline and online on a regression-based joint torque predictor. Both the offline and the online test show that the adaptive filter leads to more accurate prediction in terms of root mean square error when compared to the unfiltered prediction and higher responsiveness of the signal in terms of lag when compared to the output of a conventional low-pass filter.

A Comprehensive Framework for the Modelling of Cartesian Force Output in Human Limbs.

Neuromuscular functional electrical stimulation represents a valid technique for functional rehabilitation or, in the form of a neuroprosthesis, for the assistance of neurological patients. However, the selected stimulation of single muscles through surface electrodes remains challenging particularly for the upper extremity. In this paper, we present the MyoCeption, a comprehensive setup, which enables intuitive modeling of the user's musculoskeletal system, as well as proportional stimulation of the muscles with 16-bit resolution through up to 10 channels. The system can be used to provide open-loop force control, which, if coupled with an adequate body tracking system, can be used to implement an impedance control where the control loop is closed around the body posture. The system is completely self-contained and can be used in a wide array of scenarios, from rehabilitation to VR to teleoperation. Here, the MyoCeption's control environment has been experimentally validated through comparison with a third-party simulation suite. The results indicate that the musculoskeletal model used for the MyoCeption provides muscle geometries that are qualitatively similar to those computed in the baseline model.

Unobtrusive, natural support control of an adaptive industrial exoskeleton using force myography.

Repetitive or tiring tasks and movements during manual work can lead to serious musculoskeletal disorders and, consequently, to monetary damage for both the worker and the employer. Among the most common of these tasks is overhead working while operating a heavy tool, such as drilling, painting, and decorating. In such scenarios, it is desirable to provide adaptive support in order to take some of the load off the shoulder joint as needed. However, even to this day, hardly any viable approaches have been tested, which could enable the user to control such assistive devices naturally and in real time. Here, we present and assess the adaptive Paexo Shoulder exoskeleton, an unobtrusive device explicitly designed for this kind of industrial scenario, which can provide a variable amount of support to the shoulders and arms of a user engaged in overhead work. The adaptive Paexo Shoulder exoskeleton is controlled through machine learning applied to force myography. The controller is able to determine the lifted mass and provide the required support in real time. Twelve subjects joined a user study comparing the Paexo driven through this adaptive control to the Paexo locked in a fixed level of support. The results showed that the machine learning algorithm can successfully adapt the level of assistance to the lifted mass. Specifically, adaptive assistance can sensibly reduce the muscle activity's sensitivity to the lifted mass, with an observed relative reduction of up to 31% of the muscular activity observed when lifting 2 kg normalized by the baseline when lifting no mass.

Learning to teleoperate an upper-limb assistive humanoid robot for bimanual daily-living tasks.

Objective.Bimanual humanoid platforms for home assistance are nowadays available, both as academic prototypes and commercially. Although they are usually thought of as daily helpers for non-disabled users, their ability to move around, together with their dexterity, makes them ideal assistive devices for upper-limb disabled persons, too. Indeed, teleoperating a bimanual robotic platform via muscle activation could revolutionize the way stroke survivors, amputees and patients with spinal injuries solve their daily home chores. Moreover, with respect to direct prosthetic control, teleoperation has the advantage of freeing the user from the burden of the prosthesis itself, overpassing several limitations regarding size, weight, or integration, and thus enables a much higher level of functionality.Approach.In this study, nine participants, two of whom suffer from severe upper-limb disabilities, teleoperated a humanoid assistive platform, performing complex bimanual tasks requiring high precision and bilateral arm/hand coordination, simulating home/office chores. A wearable body posture tracker was used for position control of the robotic torso and arms, while interactive machine learning applied to electromyography of the forearms helped the robot to build an increasingly accurate model of the participant's intent over time.Main results.All participants, irrespective of their disability, were uniformly able to perform the demanded tasks. Completion times, subjective evaluation scores, as well as energy- and time- efficiency show improvement over time on short and long term.Significance.This is the first time a hybrid setup, involving myoeletric and inertial measurements, is used by disabled people to teleoperate a bimanual humanoid robot. The proposed setup, taking advantage of interactive machine learning, is simple, non-invasive, and offers a new assistive solution for disabled people in their home environment. Additionnally, it has the potential of being used in several other applications in which fine humanoid robot control is required.

Model-Augmented Haptic Telemanipulation: Concept, Retrospective Overview, and Current Use Cases.

Certain telerobotic applications, including telerobotics in space, pose particularly demanding challenges to both technology and humans. Traditional bilateral telemanipulation approaches often cannot be used in such applications due to technical and physical limitations such as long and varying delays, packet loss, and limited bandwidth, as well as high reliability, precision, and task duration requirements. In order to close this gap, we research model-augmented haptic telemanipulation (MATM) that uses two kinds of models: a remote model that enables shared autonomous functionality of the teleoperated robot, and a local model that aims to generate assistive augmented haptic feedback for the human operator. Several technological methods that form the backbone of the MATM approach have already been successfully demonstrated in accomplished telerobotic space missions. On this basis, we have applied our approach in more recent research to applications in the fields of orbital robotics, telesurgery, caregiving, and telenavigation. In the course of this work, we have advanced specific aspects of the approach that were of particular importance for each respective application, especially shared autonomy, and haptic augmentation. This overview paper discusses the MATM approach in detail, presents the latest research results of the various technologies encompassed within this approach, provides a retrospective of DLR's telerobotic space missions, demonstrates the broad application potential of MATM based on the aforementioned use cases, and outlines lessons learned and open challenges.