cs.RONov 2, 2026

Myoelectric Control for Upper-Limb Exoskeletons: A Decoder-Shaper-Controller Framework, Design Guidelines, and Future Directions

Authors: Davide Costanzi, Eldison Dimo, Emanuele Feola, Mario Sedilesu, Francesco Pascucci, Andrea Calanca

Organizations: Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy · Department of Biomechanical Engineering, University of Twente, 7522 NB Enschede, The Netherlands

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Abstract

Myoelectric Control Systems (MCSs) enable voluntary control of wearable robots through muscle activity. However, intention decoding, assistance shaping, and low-level actuator control are often not explicitly distinguished, making their contributions difficult to evaluate and compare. This review introduces a functional decoder-shaper-controller framework for continuous myoelectric control of upper-limb exoskeletons. The decoder estimates an intention-related variable from surface electromyography (sEMG), the shaper maps it to a control reference according to an assistance policy, and the controller realizes that reference through actuator control. This decomposition separates intention estimation from assistance-policy design and enables comparison of heterogeneous architectures. Representative approaches are critically examined in terms of module design, calibration, and evaluation. The analysis highlights that the shaper, despite determining the magnitude and shape of assistance, lacks established design and tuning methodologies and is often embedded within the decoder or controller. Key open issues include inconsistent reporting of assistance levels, limited use of interaction-aware stability and passivity tools, and insufficient validation across degrees of freedom, users, and tasks. Building on these findings, this review provides design guidelines for systematic module-wise tuning and identifies future research priorities to bridge the gap between accurate intention decoding and safe, effective, and generalizable assistance under real-world operating conditions.

Figures & tables

Ref.YearArchitectureTopics
[ [ 16 ] ]2015Continuous/discrete decoderRecommended practices and future research on myoelectric interfaces
[ [ 17 ] ]2015Decoder, controllerSensor fusion methods for wearable robots
[ [ 18 ] ]2020Decoder, controllerKey technologies for human-robot interaction based on sEMG
[ [ 19 ] ]2022DecoderMachine-learning and deep-learning methods for robust, adaptive, and reliable upper limb motion estimation for myoelectric control
[ [ 5 ] ]2022Decoder, controllerMCSs for upper limb wearable robotic exoskeletons and exosuits
[ [ 20 ] ]2023DecoderNeuromusculoskeletal biomechanics: neural control, muscle–tendon dynamics, musculoskeletal modeling, and digital-twin applications
Table I: Representative recent reviews on topics related to continuous myoelectric control.
Figure 1: Overview of the proposed three-module architecture for myoelectric control of upper-limb exoskeletons, comprising the decoder, shaper, and controller, together with the myoelectric interface and human–exoskeleton feedback pathways.
Figure 2: Human motor control loop for a subject wearing an exoskeleton. Blue boxes denote physical components, green boxes indicate information transmitted between components, and red boxes represent roles in the MCS taxonomy.
Decoder ParadigmEmpiricalLinear RegressionNon-linear RegressionNMS ModelsML ModelsPhysics-Informed ML Models
Typical TargetNo explicit targetd∗(t)d^{*}(t) : force, joint torque, kinematicsd∗(t)d^{*}(t) : force, joint torque, kinematicsd∗(t)d^{*}(t) : force, joint torque; kinematics possibled∗(t)d^{*}(t) : kinematics; force, joint torque possiblePotential d∗(t)d^{*}(t) : force, joint torque, stiffness, muscle states, kinematics
Physiological StructureNoneNoneOptionalExplicitNo explicit physiological structurePhysical constraints through loss functions or model architecture
GeneralizationVery lowImproved when fitting force/torque rather than kinematicsImproved when fitting force/torque rather than kinematicsPotential robustness across payloads; model- and calibration-dependentStrongly dependent on training data distributionPotential improvement over conventional ML; not yet demonstrated in complete MCSs
EMD HandlingGenerally ignoredGenerally ignoredPossible through explicit temporal or activation dynamicsCan be explicitly modeledImplicitly handled through the temporal analysis window/forecasting horizonPotentially incorporable through physical constraints
Calibration BurdenNo explicit decoder calibrationVery LowLow-moderateModel-dependentData- and model-dependentData- and model-dependent
Computational BurdenVery LowVery LowLow-moderateImplementation-dependentModel-dependentModel-dependent
Table II: Overview of the continuous decoder paradigms considered in this review. Generalization, calibration burden, and computational burden are qualitative considerations that depend on the prediction target, model complexity, available data, and implementation.
Shaper TypeAssistance Level η\etaStrengthsLimitations
Proportional (force →\to force)η≈β1+β\eta\approx\frac{\beta}{1+\beta}Simple and interpretable; η\eta explicitly computable; widely validated on humansInstability/performance degradation reported above η≈0.5−0.6\eta\approx 0.5-0.6 [ 40 , 39 ]
Proportional (force →\to velocity)Not directly computable; variable, η=1\eta=1 in static conditions, decreases with velocityPayload-independent behaviorNo established η\eta guidelines; no established calibration guidelines
Integral (force →\to force)η→1\eta\to 1 achievable for quasi-static tasksReduces effort in static conditionsAssistance level not adjustable; not suitable for fast tasks; not robust to arm kinematics, since no gravity-compensation law is applied
AdmittanceNot directly computablePhysiologically motivated force →\to velocity mapping; particularly suitable for users with no residual movement, e.g., pathological populations [ 30 ]Requires careful tuning of virtual dynamics, often selected empirically or according to user preferences; inappropriate parameters may affect responsiveness, stability, comfort, and interaction compliance
Adaptive gravity compensationη→1\eta\to 1 achievable for quasi-static tasks; partial gravity compensation possibleRobust to arm kinematics through the gravity-compensation model; robust to subject anthropometry; relatively robust performance under dynamic conditionsPartial gravity compensation has been shown to be preferred over full compensation [ 124 , 125 ] ; optimal partial compensation level has not yet been identified
Learning-basedNot directly computable; output of the learning algorithmCan encode complex nonlinear mappings; can optimize assistance policiesη\eta not directly tunable; shaper/decoder boundary may collapse
Table III: Comparative summary of shaper types in terms of achievable assistance level, strengths, and limitations.
Figure 3: Summary of the main open research questions identified through the critical analysis of the reviewed literature.
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