Context-Dependent Upper Limb Prosthesis Control for Natural and Robust Use

Abstract

Pattern recognition and regression methods applied to the surface EMG have been used for estimating the user intended motor tasks across multiple degrees of freedom (DOF), for prosthetic control. While these methods are effective in several conditions, they are still characterized by some shortcomings. In this study we propose a methodology that combines these two approaches for mutually alleviating their limitations. This resulted in a control method capable of context-dependent movement estimation that switched automatically between sequential (one DOF at a time) or simultaneous (multiple DOF) prosthesis control, based on an online estimation of signal dimensionality. The proposed method was evaluated in scenarios close to real-life situations, with the control of a physical prosthesis in applied tasks of varying difficulties. Test prostheses were individually manufactured for both able-bodied and transradial amputee subjects. With these prostheses, two amputees performed the Southampton Hand Assessment Procedure test with scores of 58 and 71 points. The five able-bodied individuals performed standardized tests, such as the box&block and clothes pin test, reducing the completion times by up to 30%, with respect to using a state-of-the-art pure sequential control algorithm. Apart from facilitating fast simultaneous movements, the proposed control scheme was also more intuitive to use, since human movements are predominated by simultaneous activations across joints. The proposed method thus represents a significant step towards intelligent, intuitive and natural control of upper limb prostheses.

Assessment of a Wearable Force- and Electromyography Device and Comparison of the Related Signals for Myocontrol

“…The choice of this set of movements was motivated by the well-known importance of controlling at least the wrist pronation/supination (see e.g., Jiang et al, 2012b) together with grasping; for instance, pronation and supination of the wrist are operated by deep muscles (Biryukova and Yourovskaya, 1994), meaning that they are usually hard to detect using sEMG. It is worth mentioning that, to the best of our knowledge, there is so far no commercially available 2-active-DOFs prosthetic wrist, but a few prototypes are being studied [see e.g., the device embedded in the DEKA arm, https://www.youtube.com/watch?v=KCUwoxuAdYQ, and the prototype by Ottobock which appears for instance in Amsuess et al (2016)].…”

Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes

“…Amsuess et al . (Amsuess et al, 2016) used eight electrodes and combined sequential-simultaneous control to improve activity of daily living performance tasks in five able-bodied subjects and two amputees.…”

Combining Electromyography and Tactile Myography to Improve Hand and Wrist Activity Detection in Prostheses

“…Surface electromyography (sEMG) sensors allow for capturing the electrical activity of the muscles of the forearm, which is used to estimate the user intent using machine learning methods [1]. Most research efforts in this area have focused on myoelectric prostheses, by exploring the control of one, two or multiple degrees of freedom (DOFs) [2][3][4] or by exploiting hand synergies [5,6]. Even by means of this technology, simultaneous and proportional control of multiple DOFs remains a major challenge [7]; the problem is that even when state-of-the-art machine learning algorithms are used to interpret sEMG data, robust (reliable) hand activity detection is not yet possible in daily living activities with a small number of sEMG sensors.…”