Summary
Controlling robotic arms worn on the body using muscle signals is complicated because the system must first figure out what you want to do, then shape the robot’s movement, and finally control the motors to act accordingly. This paper reviews existing methods by breaking them into three parts: decoding user intention from muscle signals, shaping how the robot assists, and controlling the motors. The authors found that help for shaping assistance is less developed than other parts and that methods often don’t test well in varied real-life situations. They offer design tips and suggest future work to make wearable robot control more accurate, safer, and adaptable.
What this means in practice
- •For wearable robotics designers: Design and tune modular myoelectric control systems with clear separation of intention decoding, assistance shaping, and actuator control.
- •For rehabilitation device developers: Improve validation methods for upper-limb exoskeletons to ensure safe and effective assistance across diverse patients and tasks.
A survey. It maps existing work.
Authors
Davide Costanzi, Eldison Dimo, Emanuele Feola, Mario Sedilesu, Francesco Pascucci, Andrea Calanca
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.