TY  - JOUR
AB  - Research on machine learning approaches for upper limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient's everyday lives remains a challenge, because advanced control schemes tend to break down under everyday disturbances, such as electrode shifts. Recently, it has been suggested to apply adaptive transfer learning to counteract electrode shifts using as little newly recorded training data as possible.<br /><br />

In this paper, we present a novel, simple version of transfer learning and provide the first user study demonstrating the effectiveness of transfer learning to counteract electrode shifts. For this purpose, we introduce the novel Box and Beans test to evaluate prosthesis proficiency and compare user performance with an initial simple pattern recognition system, the system under electrode shifts, and the system after transfer learning.  Our results show that transfer learning could significantly alleviate the impact of electrode shifts on user performance in the Box and Beans test.
DA  - 2019
DO  - 10.1109/TNSRE.2019.2907200
KW  - transfer learning
KW  - upper-limb prostheses
KW  - box and beans test
KW  - electromyography
LA  - eng
IS  - 5
M2  - 956
PY  - 2019
SN  - 1534-4320
SP  - 956-962
T2  - IEEE Transactions on Neural Systems and Rehabilitation Engineering
TI  - Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29344586
Y2  - 2024-11-22T10:51:11
ER  -