Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control

Abstract

Myoelectric prosthetic devices are commonly used to help upper limb amputees perform activities of daily living, however amputees still lack the sensory feedback required to facilitate reliable and precise control. Augmented feedback may play an important role in affecting both short-term performance, through real-time regulation, and long-term performance, through the development of stronger internal models. In this work, we investigate the potential tradeoff between controllers that enable better short-term performance and those that provide sufficient feedback to develop a strong internal model. We hypothesize that augmented feedback may be used to mitigate this tradeoff, ultimately improving both short and long-term control. We used psychometric measures to assess the internal model developed while using a filtered myoelectric controller with augmented audio feedback, imitating classification-based control but with augmented regression-based feedback. In addition, we evaluated the short-term performance using a multi degree-of-freedom constrained-time target acquisition task. Results obtained from 24 able-bodied subjects show that an augmented feedback control strategy using audio cues enables the development of a stronger internal model than the filtered control with filtered feedback, and significantly better path efficiency than both raw and filtered control strategies. These results suggest that the use of augmented feedback control strategies may improve both short-term and long-term performance.

A Review of Sensory Feedback in Upper-Limb Prostheses From the Perspective of Human Motor Control

“…Nevertheless, it has been recently proposed to use a low-bandwidth discrete feedback communicating only contact events (Clemente et al, 2016). On the other side, some researchers tested approaches that increase the communication bandwidth, e.g., through the use of visual interfaces [e.g., augmented reality glasses (Clemente et al, 2017;Markovic et al, 2017)] or acoustic signals (Gonzalez et al, 2012;Shehata et al, 2018b). This can be also done through the tactile sense by employing electrodes that integrate a matrix of stimulating pads (Štrbac et al, 2016).…”

Joint Speed Discrimination and Augmentation For Prosthesis Feedback

“…However, it is unknown how well users will understand feedback presented simultaneously for multiple degrees of freedom. Subjective feedback during a previous study revealed subjects found it difficult to understand amplitude-modulated audio feedback for a two-degree-of-freedom virtual limb 34 , though other studies have demonstrated subjects are capable of understanding frequency-modulated audio feedback for two degrees of freedom 18 . Another option is to provide feedback through a different modality, such as vibrotactile, and encode active degree of freedom via stimulus location.…”

“…Audio feedback has been shown to strengthen a user’s internal model and improve their myoelectric prosthesis control performance 18 , however audio feedback may not be viable for daily use. Many myoelectric prosthesis users exploit sound and vibrations from the motors as a proxy for proprioceptive information 20,35 , but this motor noise may also diminish the perceived cosmesis of the limb.…”

“…However, a more common approach uses sensory substitution, in which information from a missing sensory channel is provided indirectly via a separate, intact sensory channel 10 . Numerous substitution methods have been proposed over the past decades, including vibrotactile 11–15 , electrotactile 12 , skin stretch 11 , and audio 16–18 modalities 19 . What is often not considered is that prosthesis users are already using a form of sensory substitution: vision.…”

Tactile Feedback in Closed-Loop Control of Myoelectric Hand Grasping: Conveying Information of Multiple Sensors Simultaneously via a Single Feedback Channel

“…To date, prosthetic hand users rely on visual and incidental feedback for the closed-loop control of hand prosthesis (Markovic et al, 2018), as explicit feedback mechanisms are not prevalent in commercial prostheses (Cordella et al, 2016). Incidental feedback can be obtained from vibrations transmitted through the socket (Svensson et al, 2017), proprioceptive information from the muscles (Antfolk et al, 2013), sound from the motor (Markovic et al, 2018), or the reaction forces transmitted by the actuating cable in bodypowered prostheses (Shehata et al, 2018). Visual feedback has been the baseline feedback mechanism in prosthetic grasping exercises as it is the only feedback available naturally to all commercial hand prostheses (Saunders and Vijayakumar, 2011;Ninu et al, 2014).…”