A Non-Linear Body Machine Interface For Controlling Assistive Robotic Arms

Fabio Rizzoglio, Marco Giordano, Ferdinando A. Mussa-Ivaldi, Maura Casadio

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Objective: Body machine interfaces (BoMIs) enable individuals with paralysis to achieve a greater measure of independence in daily activities by assisting the control of devices such as robotic manipulators. The first BoMIs relied on Principal Component Analysis (PCA) to extract a lower dimensional control space from information in voluntary movement signals. Despite its widespread use, PCA might not be suited for controlling devices with a large number of degrees of freedom, as because of PCs&#x0027; orthonormality the variance explained by successive components drops sharply after the first. <italic>Methods:</italic> Here, we propose an alternative BoMI based on non-linear autoencoder (AE) networks that mapped arm kinematic signals into joint angles of a 4D virtual robotic manipulator. First, we performed a validation procedure that aimed at selecting an AE structure that would allow to distribute the input variance uniformly across the dimensions of the control space. Then, we assessed the users&#x0027; proficiency practicing a 3D reaching task by operating the robot with the validated AE. <italic>Results:</italic> All participants managed to acquire an adequate level of skill when operating the 4D robot. Moreover, they retained the performance across two non-consecutive days of training. <italic>Conclusion:</italic> While providing users with a fully continuous control of the robot, the entirely unsupervised nature of our approach makes it ideal for applications in a clinical context since it can be tailored to each user&#x0027;s residual movements. <italic>Significance</italic>: We consider these findings as supporting a future implementation of our interface as an assistive tool for people with motor impairments.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
StateAccepted/In press - 2023


  • Assistive manipulator
  • autoencoders
  • Calibration
  • Codes
  • human-machine interface
  • Manipulators
  • motor learning
  • Principal component analysis
  • Robot kinematics
  • Robots
  • Training

ASJC Scopus subject areas

  • Biomedical Engineering


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