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

Davide Costanzi; Eldison Dimo; Emanuele Feola; Mario Sedilesu; Francesco Pascucci; Andrea Calanca

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

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.	Year	Architecture	Topics
[ [ 16 ] ]	2015	Continuous/discrete decoder	Recommended practices and future research on myoelectric interfaces
[ [ 17 ] ]	2015	Decoder, controller	Sensor fusion methods for wearable robots
[ [ 18 ] ]	2020	Decoder, controller	Key technologies for human-robot interaction based on sEMG
[ [ 19 ] ]	2022	Decoder	Machine-learning and deep-learning methods for robust, adaptive, and reliable upper limb motion estimation for myoelectric control
[ [ 5 ] ]	2022	Decoder, controller	MCSs for upper limb wearable robotic exoskeletons and exosuits
[ [ 20 ] ]	2023	Decoder	Neuromusculoskeletal 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 Paradigm	Empirical	Linear Regression	Non-linear Regression	NMS Models	ML Models	Physics-Informed ML Models
Typical Target	No explicit target	$d^{*}(t)$ : force, joint torque, kinematics	$d^{*}(t)$ : force, joint torque, kinematics	$d^{*}(t)$ : force, joint torque; kinematics possible	$d^{*}(t)$ : kinematics; force, joint torque possible	Potential $d^{*}(t)$ : force, joint torque, stiffness, muscle states, kinematics
Physiological Structure	None	None	Optional	Explicit	No explicit physiological structure	Physical constraints through loss functions or model architecture
Generalization	Very low	Improved when fitting force/torque rather than kinematics	Improved when fitting force/torque rather than kinematics	Potential robustness across payloads; model- and calibration-dependent	Strongly dependent on training data distribution	Potential improvement over conventional ML; not yet demonstrated in complete MCSs
EMD Handling	Generally ignored	Generally ignored	Possible through explicit temporal or activation dynamics	Can be explicitly modeled	Implicitly handled through the temporal analysis window/forecasting horizon	Potentially incorporable through physical constraints
Calibration Burden	No explicit decoder calibration	Very Low	Low-moderate	Model-dependent	Data- and model-dependent	Data- and model-dependent
Computational Burden	Very Low	Very Low	Low-moderate	Implementation-dependent	Model-dependent	Model-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 Type	Assistance Level $\eta$	Strengths	Limitations
Proportional (force $\to$ force)	$\eta\approx\frac{\beta}{1+\beta}$	Simple and interpretable; $\eta$ explicitly computable; widely validated on humans	Instability/performance degradation reported above $\eta\approx 0.5-0.6$ [ 40 , 39 ]
Proportional (force $\to$ velocity)	Not directly computable; variable, $\eta=1$ in static conditions, decreases with velocity	Payload-independent behavior	No established $\eta$ guidelines; no established calibration guidelines
Integral (force $\to$ force)	$\eta\to 1$ achievable for quasi-static tasks	Reduces effort in static conditions	Assistance level not adjustable; not suitable for fast tasks; not robust to arm kinematics, since no gravity-compensation law is applied
Admittance	Not directly computable	Physiologically 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 compensation	$\eta\to 1$ achievable for quasi-static tasks; partial gravity compensation possible	Robust to arm kinematics through the gravity-compensation model; robust to subject anthropometry; relatively robust performance under dynamic conditions	Partial gravity compensation has been shown to be preferred over full compensation [ 124 , 125 ] ; optimal partial compensation level has not yet been identified
Learning-based	Not directly computable; output of the learning algorithm	Can encode complex nonlinear mappings; can optimize assistance policies	$\eta$ not directly tunable; shaper/decoder boundary may collapse

Table III: Comparative summary of shaper types in terms of achievable assistance level, strengths, and limitations.

Panel of Figure 3 — Figure 3: Summary of the main open research questions identified through the critical analysis of the reviewed literature.

Assistive Robotics

Assistive robotics develops robots and wearable devices that support mobility, daily activities, rehabilitation, and care.

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Characterizing the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton
Mar 21, 2026Kanishka Mitra, Frigyes Samuel Racz, Satyam Kumar +2Brain-Computer Interfaces MI Classification
Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA. · Department of Neurology, The University of Texas at Austin, Austin, TX, USA. · Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, USA.
Real-Time Decoding of Movement Onset and Offset for Brain-Controlled Rehabilitation Exoskeleton
Mar 17, 2026Kanishka Mitra, Satyam Kumar, Frigyes Samuel Racz +3Brain-Computer Interfaces Assistive Robotics
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. · Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA. · Department of Neurology, The University of Texas at Austin, Austin, TX, USA. +3
ATP: Anatomical Torque with Passivity-based Control Framework for Safe Upper-Limb Exoskeleton Assistance
Aug 6, 2026Yu Chen, Gong Chen, Xiang LiAnkle Exoskeleton Control Feedback Control
Department of Automation, Tsinghua University, China · Shenzhen MileBot Robotics Co., Ltd, China

Mar 21, 2026J/K move · Enter open · S save

Characterizing the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton

Kanishka Mitra, Frigyes Samuel Racz, Satyam Kumar, Ashish D. Deshpande+1

Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA. · Department of Neurology, The University of Texas at Austin, Austin, TX, USA. · Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, USA.

Brain-Computer Interfaces MI Classification Assistive Robotics

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Two distinct technologies have gained attention lately due to their prospects for motor rehabilitation: robotics and brain-machine interfaces (BMIs). Harnessing their combined efforts is a largely uncharted and promising direction that has immense clinical potential. However, a significant challenge is whether motor intentions from the user can be accurately detected using non-invasive BMIs in the presence of instrumental noise and passive movements induced by the rehabilitation exoskeleton. As an alternative to the straightforward continuous control approach, this study instead aims to characterize the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton to allow for the natural control (initiation and termination) of functional movements. Ten participants were recruited to perform kinesthetic motor imagery (MI) of the right arm while attached to the robot, simultaneously cued with LEDs indicating the initiation and termination of a goal-oriented reaching task. Using electroencephalogram signals, we built a decoder to detect the transition between i) rest and beginning MI and ii) maintaining and ending MI. Offline decoder evaluation achieved group average onset accuracy of 60.7% and 66.6% for offset accuracy, revealing that the start and stop of MI could be identified while attached to the robot. Furthermore, pseudo-online evaluation could replicate this performance, forecasting reliable online exoskeleton control in the future. Our approach showed that participants could produce quality and reliable sensorimotor rhythms regardless of noise or passive arm movements induced by wearing the exoskeleton, which opens new possibilities for BMI control of assistive devices.

Abstract

Assistive Robotics

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