Mapping of EMG signal to hand grip force at varying wrist angles
Limb loss is a growing problem in Malaysia and the rest of the world due to the increasing number of industrial accidents, diseases and armed conflicts. After a tragic incident resulting in an amputation or paralysis, the disabled individual needs to be assisted with all possible technological m...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/69466/1/Mapping_EMG_2012.pdf http://irep.iium.edu.my/69466/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Limb loss is a growing problem in Malaysia and the
rest of the world due to the increasing number of industrial
accidents, diseases and armed conflicts. After a tragic incident
resulting in an amputation or paralysis, the disabled individual
needs to be assisted with all possible technological means to
improve his quality of life. A cybernetic prosthesis is a device
which can greatly assist individuals with hand disabilities by
enabling them to have some of the hand capabilities of an able
bodied individual. The central nervous system which consists of
the brain and spine governs hand grip force and hand movement
in the human body by spatial and temporal motor unit
recruitments. Electromyogram (EMG) is an electrical biological
signal that can be measured from the skin surface and consists of
the summation of Motor Unit Action Potentials (MUAP). Hand
grip strength, wrist extension and wrist flexion are hand
functions which result from the forearm muscle activity and are
used in a wide range of daily tasks. Extracting hand grip force
and wrist angle information from forearm EMG signals is useful
to be used as inputs for the control of cybernetic prostheses. By
establishing the relationship between forearm EMG and hand
grip force/wrist angles, the prosthetic hand can be controlled in a
manner that is customized to an amputee’s intent. In this
research work, a myoelectric interface which consists of an
electronic conditioning circuit to measure EMG signals and the
software to record and process the EMG signals was developed.
Experimental training and testing data sets from five subjects
were collected to investigate the relationship between forearm
EMG, hand grip force and wrist angle simultaneously. |
|---|