An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition

Abstract

Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset—i.e., representing variations in limb position or external loads—to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development.

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

“…This control challenge is well documented and referred to as the "limb position effect" [7]. Several pattern recognition-based control methods have been investigated to minimize the limb position effect [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. These methods require a user to execute a training routine across multiple limb positions, prior to daily device use.…”

“…Performance-based assessments for the evaluation of real-time upper limb prosthesis control often require participants to execute on-screen virtual arm movements or on-screen cursor movements (such as the Target Achievement Control test [39] and Fitts' Law tests [40], respectively) [8,14,[41][42][43][44][45][46][47][48][49][50][51]. EMG sensors placed on participants' limbs record muscle activations in such assessments.…”

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

“…This control challenge is well documented and referred to as the "limb position effect" [7]. Several pattern recognition-based control methods have been investigated to minimize the limb position effect [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. These methods require a user to perform a training routine across multiple limb positions, prior to daily device use.…”

“…Performance-based assessments for the evaluation of real-time upper limb prosthesis control often require participants to either move a virtual arm to a target posture or a cursor to a target position, by performing appropriate muscle contractions in their residual limb [8,14,[40][41][42][43][44][45][46][47][48][49][50]. For example, a virtual arm is presented on a computer screen in the Target Achievement Control test [51], and a cursor is presented on-screen in Fitts' Law tests [52].…”