A surface neuromuscular electrical stimulation device for universal cartesian force control in humans

In recent years, neuromuscular electrical stim-ulation (NMES) has found many applications both within the medical field and outside. While this technology has been widely recognized as a valid tool for rehabilitative and assistive applications, most solutions presented in the literature seem to focus on highly specific cases and facili-tate very selective movements. In this article, we present a novel surface stimulation- based prototype which, cou-pled with an internally designed musculoskeletal model, allows to induce the output of generalized forces at the human end- effector in Cartesian coordinates. The control has been validated here through a 6- axis force- torque sen-sor coupled with a robotic manipulator. Thus, the meas-ured forces at the user's end- effector were compared to the commanded forces. The results confirm that open- loop control of the output force is possible with an average cor-relation coefficient between commanded and measured force output direction >0.7. This could eventually provide full, general- purpose impedance control of the human neuromuscular system, which would allow to induce ar-bitrary movements in the peri- personal space

Deflection-Domain Passivity Control of Variable Stiffnesses Based on Potential Energy Reference

With emerging capabilities, robots will advance gradually into human environments in the near future. Thereby, safety and robustness is currently tackled through intrinsically soft robotics or variable impedances, mainly stiffnesses. In tele-operation, for instance, the control stiffness can be adapted to a measured arm impedance of the operator to stiffen the robot only when required for a manipulation task. Thus, humans or moving objects in the robot's environment are protected from hard collisions. Independent from its realization through hardware or software, the stability of the variation needs to be ensured through control strategies since energy is potentially introduced into the robotic system. This work presents a novel gradient-based passivity control concept for variable stiffnesses. In contrast to state-of-the-art methods, the approach is based on a potential energy storage reference and prevents phases of zero stiffness through deflection-domain control. I.e., according to the energy storage, the stiffness variation over the spring deflection is controlled to ensure passivity. Experiments confirm the functionality of the approach and its robustness against delayed communication and active environments

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

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

Fusion of IMU and Muscular Information in Order to Solve the Limb Position Effect

The main goal of the work hereafter presented is to integrate data about a subject’s body pose with data regarding the muscular activity in the forearm in order to improve the prediction of hand movement intention, particularly with respect to the limb position effect. The efforts to this end were articulated in three fundamental phases. Firstly, a portable and lightweight upper body tracking system was designed, implemented and characterized, particularly with respect to drift. Secondly, offline and online analyses about possible approaches to integrate the data from the body tracking system with different kinds of data measuring muscular activity in the forearm were conducted. Finally, a user study involving telemanipulation using a humanoid platform was conducted with the main goal of evaluating the learning curve of a subject using the upper body tracking system and in order to test some of the offline test results in terms of performance in the execution of daily tasks. Furthermore, this user study allowed to test an alternative control scheme especially suited for transradial amputees, with the main goal to estimate wrist rotation intention without the need to rely on muscular activity information

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 g in total, lasts up to 10.5 h 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