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' orthonormality the variance explained by successive components drops sharply after the first.
Methods: 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' proficiency practicing a 3D reaching task by operating the robot with the validated AE.
Results: 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.
Conclusion: 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's residual movements.
Significance: We consider these findings as supporting a future implementation of our interface as an assistive tool for people with motor impairments.