Start with a digital twin that mimics both the human and the exoskeleton
The first line of defense is a simulation that models the human musculoskeletal system and the exoskeleton together. A 2024 Nature study used a 'learning-in-simulation' framework with a musculoskeletal model and reinforcement learning to train a hip exoskeleton controller without any human experiments [1]. This approach reduced metabolic rate by 24.3% for walking, 13.1% for running, and 15.4% for stair climbing—meaning the controller was already effective before a person ever wore it. The key is that the simulation must be 'dynamics-aware,' meaning it accounts for how muscles and the device interact, not just the exoskeleton alone.
Similarly, a 2022 study on a knee exoskeleton used a differentiable musculoskeletal model to predict joint torque and angle from EMG (electromyography) signals, achieving accurate trajectory prediction and good assistance during movement [4]. This shows that simulation can be used to refine control algorithms before hardware testing, reducing the risk of unexpected behavior.
Then test the physical hardware on a dummy or replica, not a person
Once the controller works in simulation, the next step is to validate it on the actual exoskeleton hardware but without a human in the loop. A 2022 study on a finger exoskeleton built a replica of a human finger to test the assistance and grasp control [2]. The controller maintained force within 3% of the desired value in both simulated and experimental tests, showing that hardware-in-the-loop testing can catch discrepancies between simulation and reality. This is a safe way to verify that the actuators, sensors, and control algorithms work together before exposing a person.
This approach is especially important because simulation can never perfectly capture real-world friction, sensor noise, or mechanical wear. Testing on a dummy or replica allows you to tune the controller and identify mechanical issues without risking injury.
Finally, follow strict safety guidelines for human trials
When you do move to human testing, safety must be the top priority. A 2024 guideline paper outlines a comprehensive list of risks—including joint hyper-extension, sudden motion, misalignment, sensor failures, and control algorithm malfunctions—and recommends pre-operation checks, user training, emergency response, and real-time monitoring [3]. These guidelines are not optional; they are essential to prevent injury during the transition from simulation to reality.
The guideline also emphasizes the need for emergency stop mechanisms and fall risk assessment, especially for lower-limb exoskeletons. Even with a well-trained controller, unexpected human-robot interactions can occur, so having a human supervisor and a physical emergency stop is critical.
For brain-controlled exoskeletons, use a simulator first to test the interface
If the exoskeleton is controlled by brain signals (BCI), a simulator is even more valuable. A 2023 study used a digital twin of a lower-limb exoskeleton to test an EEG-based brain-computer interface (BCI) that decodes intended gait direction [5]. Six healthy participants wore an EEG cap and controlled the simulator with an average accuracy of 0.75 (75%) and low error rates. This allowed the researchers to evaluate the BCI system safely before any physical exoskeleton was involved. The study also revealed a 5-minute delay in real-time control due to signal processing, highlighting the need to test such systems in simulation to identify and address latency issues before human trials.
About These Sources
This answer is built on 5 peer-reviewed studies — published from 2022 to 2024, 2 from 2024 or later, 2 in Q1 journals, collectively cited 138 times — selected as the most relevant from 5 studies that passed quality screening, drawn from 54 papers retrieved from a database of over 500 million.
Sources used in this answer
Experiment-free exoskeleton assistance via learning in simulation
A learning-in-simulation framework with a musculoskeletal model trained a hip exoskeleton controller without human experiments, reducing metabolic rate by 24.3% (walking), 13.1% (running), and 15.4% (stair climbing) when deployed.
Control Design and Testing for a Finger Exoskeleton Mechanism
A finger exoskeleton controller was validated on a replica human finger, maintaining force within 3% of the desired value in both simulated and experimental grasp tests.
Safety in Wearable Robotic Exoskeletons: Design, Control, and Testing Guidelines
A safety guideline paper enumerates risks (e.g., joint hyper-extension, sensor failures, control malfunctions) and recommends pre-operation checks, user training, emergency response, and real-time monitoring for exoskeleton testing.
A Differentiable Dynamic Model for Musculoskeletal Simulation and Exoskeleton Control
A differentiable musculoskeletal model merged with EMG signals accurately predicted knee joint torque and angle, outperforming other models in convergence rate and execution time for exoskeleton control.
Development of a BCI-Controlled Lower Limb Exoskeleton Simulator
An EEG-based BCI for a lower-limb exoskeleton was tested on a digital twin, achieving 75% accuracy in decoding gait direction among six healthy participants, but with a 5-minute delay in real-time control.