Hand typist robot modelling for quadriplegic person using extreme learning machine

• Published in: 2017 15th International Conference on Quality in Research (QiR) : International Symposium on Electrical and Computer Engineering pp. 330 - 335
• Main Authors: Kurniawan, Dimas A., Syai'in, Mat, Kautsar, S., Hasin, M. Khoirul, Herijono, Boedi, Endrasmono, J., Soelistijono, R. T., Wahidin, Aang, Subiyanto, L., Setyoko, A. S., Soeprijanto, Adi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: Actuators; Compass sensor (CMPS10); Degrees of Freedom (DOF); Dynamixel AX-12; Extreme Learning Machine (ELM); Foldable keyboard; Hand Typist Robot (HTR); Keyboards; Manipulators; Mathematical model; Quadriplegic person; Robot sensing systems; Training
• DOI: 10.1109/QIR.2017.8168506

This paper will present an implementation of Extreme Learning Machine (ELM) in Prototype of Hand Typist Robot (HTR). HTR is Typist Robot which is designed for quadriplegic people. HTR consists of two robotic arms with three dynamixel AX-12 that mounted on each arm. It is mean that each arm has 3 DOF. To operate HTR, user has to equipped with compass sensor (CMPS10), installed on the part of body that has good function. In this paper ELM is used to map and make decision between the signal which sending by CMPS10 and position of alphabet that will be reached by Robot Arm. The advantage of ELM is superior in training process and easy to implement. Using ELM, the relationship between input and output can be present only using one simple matrix. From the experiment result shown that 73 keys of computer keyboard can be reached by HTR with an error 5%. The error is accumulated errors which is caused by vibration of dynamixel AX-12 when it is moving. To minimize the error the HTR need to reset regularly.