TY - JOUR
T1 - Effective Multi-Mode Grasping Assistance Control of a Soft Hand Exoskeleton Using Force Myography
AU - Islam, Muhammad Raza Ul
AU - Bai, Shaoping
N1 - Funding Information:
This work was supported by Innovation Fund Denmark for project EXO-AIDER (https://www.exo-aider.dk).
Publisher Copyright:
© Copyright © 2020 Islam and Bai.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Human intention detection is fundamental to the control of robotic devices in order to assist humans according to their needs. This paper presents a novel approach for detecting hand motion intention, i.e., rest, open, close, and grasp, and grasping force estimation using force myography (FMG). The output is further used to control a soft hand exoskeleton called an SEM Glove. In this method, two sensor bands constructed using force sensing resistor (FSR) sensors are utilized to detect hand motion states and muscle activities. Upon placing both bands on an arm, the sensors can measure normal forces caused by muscle contraction/relaxation. Afterwards, the sensor data is processed, and hand motions are identified through a threshold-based classification method. The developed method has been tested on human subjects for object-grasping tasks. The results show that the developed method can detect hand motions accurately and to provide assistance w.r.t to the task requirement.
AB - Human intention detection is fundamental to the control of robotic devices in order to assist humans according to their needs. This paper presents a novel approach for detecting hand motion intention, i.e., rest, open, close, and grasp, and grasping force estimation using force myography (FMG). The output is further used to control a soft hand exoskeleton called an SEM Glove. In this method, two sensor bands constructed using force sensing resistor (FSR) sensors are utilized to detect hand motion states and muscle activities. Upon placing both bands on an arm, the sensors can measure normal forces caused by muscle contraction/relaxation. Afterwards, the sensor data is processed, and hand motions are identified through a threshold-based classification method. The developed method has been tested on human subjects for object-grasping tasks. The results show that the developed method can detect hand motions accurately and to provide assistance w.r.t to the task requirement.
KW - exoskeleton control
KW - FSR sensor band
KW - grasping assistance
KW - human intention detection
KW - soft hand exoskeletons
UR - http://www.scopus.com/inward/record.url?scp=85096940448&partnerID=8YFLogxK
U2 - 10.3389/frobt.2020.567491
DO - 10.3389/frobt.2020.567491
M3 - Journal article
AN - SCOPUS:85096940448
SN - 2296-9144
VL - 7
JO - Frontiers in Robotics and AI
JF - Frontiers in Robotics and AI
M1 - 567491
ER -