TY - JOUR
T1 - Robust Payload Recognition based on Sensor-Over-Muscle-Independence Deep Learning for the Control of Exoskeletons
AU - Tahir, Abdullah
AU - An, Zeliang
AU - Bai, Shaoping
AU - Shen, Ming
N1 - Publisher Copyright:
IEEE
PY - 2023/9/1
Y1 - 2023/9/1
N2 - Force myography (FMG) can detect changes in the muscle volume which can be interpreted to recognize human intention. FMG data, however, is highly dependent on the placement of sensors over muscles, and interpretation becomes challenging if the sensors get displaced. This brief presents a robust sensor over muscle independence (SOMI) preprocessing algorithm combined with lightweight deep neural network (DNN) which shows high classification accuracy of the FMG data. SOMI organizes the irregular sensory data into regular patterns. Proposed algorithm makes the DNN insensitive to not only position and rotation shift but also to the flip of the sensors arrangement. A custom designed FMG band is used for payload recognition to experimentally validate the proposed method with five payload statuses and eight subjects. A five-fold cross validation comparative study demonstrated that the proposed method is 19.8% and 7.1% more accurate than support vector machine (SVM) and DNN without SOMI, respectively, and showed superior performance against k-nearest neighbors (KNN) and decision tree (DT). SOMI empowered a lightweight DNN to maintain the accuracy over 98% for different arbitrary wearing schemes of the FMG band over both left and right upper arms.
AB - Force myography (FMG) can detect changes in the muscle volume which can be interpreted to recognize human intention. FMG data, however, is highly dependent on the placement of sensors over muscles, and interpretation becomes challenging if the sensors get displaced. This brief presents a robust sensor over muscle independence (SOMI) preprocessing algorithm combined with lightweight deep neural network (DNN) which shows high classification accuracy of the FMG data. SOMI organizes the irregular sensory data into regular patterns. Proposed algorithm makes the DNN insensitive to not only position and rotation shift but also to the flip of the sensors arrangement. A custom designed FMG band is used for payload recognition to experimentally validate the proposed method with five payload statuses and eight subjects. A five-fold cross validation comparative study demonstrated that the proposed method is 19.8% and 7.1% more accurate than support vector machine (SVM) and DNN without SOMI, respectively, and showed superior performance against k-nearest neighbors (KNN) and decision tree (DT). SOMI empowered a lightweight DNN to maintain the accuracy over 98% for different arbitrary wearing schemes of the FMG band over both left and right upper arms.
KW - Classification algorithms
KW - deep neural network (DNN)
KW - Exoskeletons
KW - force mayography (FMG)
KW - Muscles
KW - Neural networks
KW - payload classification
KW - Payloads
KW - robotic exoskeleton and prosthesis
KW - Sensor over muscle independence (SOMI)
KW - Sensors
KW - Training
UR - http://www.scopus.com/inward/record.url?scp=85153526432&partnerID=8YFLogxK
U2 - 10.1109/TCSII.2023.3266827
DO - 10.1109/TCSII.2023.3266827
M3 - Journal article
AN - SCOPUS:85153526432
SN - 1549-7747
VL - 70
SP - 3699
EP - 3703
JO - IEEE Transactions on Circuits and Systems II: Express Briefs
JF - IEEE Transactions on Circuits and Systems II: Express Briefs
IS - 9
ER -