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

Sensors

Cristina Piazza¹  Veronika Spieker¹  Sami Haddadin¹  Amartya Ganguly¹ 

[1] Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, 80797 Munich, Germany;

关键词: upper-limb prostheses;    myoelectric control;    pattern recognition;    limb effect;    linear discriminant analysis;    multi-modal control;   

DOI  :  10.3390/s21217404

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown