IoST-Enabled Robotic Arm Control and Abnormality Prediction Using Minimal Flex Sensors and Gaussian Mixture Models

This work presents a groundbreaking approach with a fusion of the Internet of Sensing Things (IoST) and Robotics. This system utilizes four flex sensors strategically placed on the most flexible fingers across both hands to control a Six-DoF robotic arm, offering a novel interface for those with lim...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Akhund, Tajim Md. Niamat Ullah, Shaikh, Zaffar Ahmed, Diez, Isabel de la Torre, Gafar, Manal, Ajabani, Deep H., Alfarraj, Osama, Tolba, Amr, Fabian-Gongora, Henry, Lopez, Luis Alonso Dzaul
Format: Article
Language:English
Subjects:
Abnormality Prediction
Data acquisition
Finger Controlled Arm
Fingers
Flex sensor
Flexible printed circuits
Gaussian Mixture Models
Gaussian processes
Internet of Sensing Things (IoST)
Internet of Things
Machine learning
Manipulators
Mathematical models
Mixtures
Predictive models
Probabilistic models
Robot arms
Robot control
Robot sensing systems
Robotics
Robots
Sensors
Servocontrol
Servomechanisms
Servomotors
Six-DoF Robotic Arm
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work presents a groundbreaking approach with a fusion of the Internet of Sensing Things (IoST) and Robotics. This system utilizes four flex sensors strategically placed on the most flexible fingers across both hands to control a Six-DoF robotic arm, offering a novel interface for those with limited mobility. This system can also be used for moving toxic objects. The Raspberry Pi is the central control unit that acquires data from the flex sensor and controls the servo motors. Moreover, the device incorporates machine learning to learn the daily movements of the users and predict abnormal finger movements. Multiple data analyses and visualization are initiated to predict the normal and abnormal data. GMMs or Gaussian Mixture Models showed successful results among various abnormality detection processes. This amalgamation of flexible sensing and mathematical modeling offers precision and adaptability in control mechanisms.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3380360