Electromyography Based Human-Robot Interfaces for the Control of Artificial Hands and Wearable Devices

The design of robotic systems is currently facing human-inspired solutions as a road to replicate the human ability and flexibility in performing motor tasks. Especially for control and teleoperation purposes, the human-in-the-loop approach is a key element within the framework know as Human-Robot Interface. This thesis reports the research activity carried out for the design of Human-Robot Interfaces based on the detection of human motion intentions from surface electromyography. The main goal was to investigate intuitive and natural control solutions for the teleoperation of both robotic hands during grasping tasks and wearable devices during elbow assistive applications. The design solutions are based on the human motor control principles and surface electromyography interpretation, which are reviewed with emphasis on the concept of synergies. The electromyography based control strategies for the robotic hand grasping and the wearable device assistance are also reviewed. The contribution of this research for the control of artificial hands rely on the integration of different levels of the motor control synergistic organization, and on the combination of proportional control and machine learning approaches under the guideline of user-centred intuitiveness in the Human-Robot Interface design specifications. From the side of the wearable devices, the control of a novel upper limb assistive device based on the Twisted String Actuation concept is faced. The contribution regards the assistance of the elbow during load lifting tasks, exploring a simplification in the use of the surface electromyography within the design of the Human-Robot Interface. The aim is to work around complex subject-dependent algorithm calibrations required by joint torque estimation methods

Human to robot hand motion mapping methods: review and classification

In this article, the variety of approaches proposed in literature to address the problem of mapping human to robot hand motions are summarized and discussed. We particularly attempt to organize under macro-categories the great quantity of presented methods, that are often difficult to be seen from a general point of view due to different fields of application, specific use of algorithms, terminology and declared goals of the mappings. Firstly, a brief historical overview is reported, in order to provide a look on the emergence of the human to robot hand mapping problem as a both conceptual and analytical challenge that is still open nowadays. Thereafter, the survey mainly focuses on a classification of modern mapping methods under six categories: direct joint, direct Cartesian, taskoriented, dimensionality reduction based, pose recognition based and hybrid mappings. For each of these categories, the general view that associates the related reported studies is provided, and representative references are highlighted. Finally, a concluding discussion along with the authors’ point of view regarding future desirable trends are reported.This work was supported in part by the European Commission’s Horizon 2020 Framework Programme with the project REMODEL under Grant 870133 and in part by the Spanish Government under Grant PID2020-114819GB-I00.Peer ReviewedPostprint (published version

A Control Architecture for Grasp Strength Regulation in Myocontrolled Robotic Hands Using Vibrotactile Feedback: Preliminary Results

Nowadays, electric-powered hand prostheses do not provide adequate sensory instrumentation and artificial feedback to allow users voluntarily and finely modulate the grasp strength applied to the objects. In this work, the design of a control architecture for a myocontrol-based regulation of the grasp strength for a robotic hand equipped with contact force sensors is presented. The goal of the study was to provide the user with the capability of modulating the grasping force according to target required levels by exploiting a vibrotactile feedback. In particular, the whole human-robot control system is concerned (i.e. myocontrol, robotic hand controller, vibrotactile feedback.) In order to evaluate the intuitiveness and force tracking performance provided by the proposed control architecture, an experiment was carried out involving four naïve able-bodied subjects in a grasping strength regulation task with a myocontrolled robotic hand (the University of Bologna Hand), requiring for grasping different objects with specific target force levels. The reported results show that the control architecture successfully allowed all subjects to achieve all grasping strength levels exploiting the vibrotactile feedback information. This preliminary demonstrates that, potentially, the proposed control interface can be profitably exploited in upper-limb prosthetic applications, as well as for non-rehabilitation uses, e.g. in ultra-light teleoperation for grasping devices

Mapping human hand fingertips motion to an anthropomorphic robotic hand

The mapping of the human intention to a dexterous anthropomorphic robotic hand is still an open issue among researchers. The complexity behind this problems comes mainly from three factors: the kinematics differences between the users and the robotic hand(s); the differences in size and motion capabilities among different users hands; and the high number of degrees of freedom present in an anthropomorphic hand. In this work, we present a procedure for the determination of a linear transformation capable to interface the user and the robot kinematics and therefore to allow a precise and natural control of the mechanical device. The main assumption that we make is that different human hand kinematics differ -with a good approximation- for a scaling factor only, whereas the proportions between the phalanges lengths and the relative orientation of the fingers are kept almost constant in healthy people [1]. We also assume that, being the considered robotic hand highly anthropomorphic, this condition holds also between the user and the robotic hand. In addition, while for a robotic hand the definition of a reference frame fixed to the palm is a free choice, for the human hand tracked with some external system it is completely software dependent. Therefore additional rotational and translational corrective terms have to be introduced to compensate for the different placement of the palm reference frame with respect to the fingers. We have applied this approach to control the UB-Hand IV using a commercial device called Leap Motion, able to track with a good accuracy the pose of the palm and the positions of the key points of the human hand, i.e. the end points of the hand bones [2]

Robot Kinesthetic Teaching Enhanced by sEMG-based Estimation of Muscle Co-Contraction and Bio-Feedback

sEMG signals are exploited for unsupervised estimation of the co-contraction level of forearm's muscles. In this way, by also exploiting a feedback based on a vibrotactile bracelet, the ability of operators in stiffening their hand was evaluated during kinesthetic teaching, in order to regulate the estimated co-contraction level to (i) match reference levels and (ii) activate the opening/closing of a gripper, i.e. in using their myoelectric signals enhance robot kinesthetic teaching operations. Experiments were carried out. The results provide positive outcomes on the intuitiveness and effectiveness of the proposed system and approach

Experimental evaluation of a sEMG-based control for elbow wearable assistive devices during load lifting tasks

In this work, a surface skin electromyography(sEMG)-based control solution for elbow wearable assistive devices during load lifting tasks is presented. The goal of the controller consists in limiting the user's muscle activity during the task execution, in such a way that the assistive device can partially compensate the load-related biceps muscle effort. Since sEMG-driven control strategies based on the estimation of the joint torques generally requires complex task- and subject-dependent training sessions for tuning the control algorithms, here a more direct control approach is proposed, based on a muscle activity error related proportional-integral action together with an double-threshold activation logic. The controller's parameters are easily set by means of a fast, online and automatic subject calibration procedure, ensuring a simple adjustability to different users. An experimental phase has been conducted in order to evaluate the sEMG-based control performance involving four healthy subjects, using as wearable assistive device a twisted string action module, which is particularly suitable for assistive applications because of its lightness and compactness. Results show that the control strategy is able to successfully limit the EMG activity of the subjects during the lifting tasks, providing preliminary outcomes and promising possibilities for the use of twisted string-based technologies to assist human joints and muscles

REMODEL. WP3. User And System Interface. T3_3. Teaching By Demonstration Of Skills For New Assembly References And Tasks. Evaluation of physical human-robot interaction modalities. v0

The datasets contain the data related to the experiment was carried out involving four subjects – named U1, U2, U3, U4 – in a series of physical and muscle strength training tasks, related to the publication: R. Meattini, D. Chiaravalli, G. Palli and C. Melchiorri, "sEMG-Based Human-in-theLoop Control of Elbow Assistive Robots for Physical Tasks and Muscle Strength Training," in IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5795-5802, Oct. 2020. (DOI: 10.1109/LRA.2020.3010741

REMODEL. WP3. User And System Interface. T3_6. Teaching By Demonstration Of Skills For New Assembly References And Tasks. Simulative evaluation of hand motion mapping. v0

The dataset contains the data related to human to robot hand mapping, ensuring natural motions and predictability for the operator, since it requires the preservation of the Cartesian position of the fingertips and the finger shapes given by the joint values. We exploit the spatial information available in-hand, in particular, related to the thumb-finger relative position, for combining joint and Cartesian mappings. In this way, it is possible to perform a large range of both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We consider two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint–Cartesian transition is defined on the areas of the workspace in which thumb and fingertips can get in contact. The data are presented in the publication: Meattini, R., Chiaravalli, D., Palli, G., & Melchiorri, C. (2022). Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information. Frontiers in Robotics and AI, 9:878364. doi: 10.3389/frobt.2022.87836

sEMG-Based Human-in-the-Loop Control of Elbow Assistive Robots for Physical Tasks and Muscle Strength Training

In this letter we present a sEMG-driven human-in-the-loop (HITL) control designed to allow an assistive robot produce proper support forces for both muscular effort compensations , i.e. for assistance in physical tasks, and muscular effort generations , i.e. for the application in muscle strength training exercises related to the elbow joint. By employing our control strategy based on a Double Threshold Strategy (DTS) with a standard PID regulator, we report that our approach can be successfully used to achieve a target, quantifiable muscle activity assistance. In this relation, an experimental concept validation was carried out involving four healthy subjects in physical and muscle strength training tasks, reporting with single-subject and global results that the proposed sEMG-driven control strategy was able to successfully limit the elbow muscular activity to an arbitrary level for effort compensation objectives, and to impose a lower bound to the sEMG signals during effort generation goals. Also, a subjective qualitative evaluation of the robotic assistance was carried out by means of a questionnaire. The obtained results open future possibilities for a simplified usage of the sEMG measurements to obtain a target, quantitatively defined, robot assistance for human joints and muscles