Next Article in Journal
All-In-Focus Polarimetric Imaging Based on an Integrated Plenoptic Camera with a Key Electrically Tunable LC Device
Next Article in Special Issue
Design of an Effective Prosthetic Hand System for Adaptive Grasping with the Control of Myoelectric Pattern Recognition Approach
Previous Article in Journal
Influence of Current on Soil Corrosion of Galvanized Steel of Grounding Grids
Previous Article in Special Issue
A Portable Waist-Loaded Soft Exosuit for Hip Flexion Assistance with Running
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Micromachines
Volume 13
Issue 2
10.3390/mi13020191
micromachines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Embedded Machine Learning Using a Multi-Thread Algorithm on a Raspberry Pi Platform to Improve Prosthetic Hand Performance

by
Triwiyanto Triwiyanto
1,*,
Wahyu Caesarendra
2,*,
Mauridhi Hery Purnomo
3,
Maciej Sułowicz
4,
I Dewa Gede Hari Wisana
1,
Dyah Titisari
1,
Lamidi Lamidi
1 and
Rismayani Rismayani
5
1
Department of Medical Electronics Technology, Poltekkes Kemenkes Surabaya, Surabaya 60282, Indonesia
2
Manufacturing Systems Engineering, Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong BE1410, Brunei
3
Department of Computer Engineering, Institute of Sepuluh Nopember, Surabaya 60111, Indonesia
4
Department of Electrical Engineering, Cracow University of Technology, 31-155 Cracow, Poland
5
Department of Software Engineering, Dipa Makassar University, Makassar 90245, Indonesia
*
Authors to whom correspondence should be addressed.
Micromachines 2022, 13(2), 191; https://doi.org/10.3390/mi13020191
Submission received: 14 December 2021 / Revised: 18 January 2022 / Accepted: 22 January 2022 / Published: 26 January 2022
(This article belongs to the Special Issue Wearable Robotics)
Browse Figures
Versions Notes

Abstract

High accuracy and a real-time system are priorities in the development of a prosthetic hand. This study aimed to develop and evaluate a real-time embedded time-domain feature extraction and machine learning on a system on chip (SoC) Raspberry platform using a multi-thread algorithm to operate a prosthetic hand device. The contribution of this study is that the implementation of the multi-thread in the pattern recognition improves the accuracy and decreases the computation time in the SoC. In this study, ten healthy volunteers were involved. The EMG signal was collected by using two dry electrodes placed on the wrist flexor and wrist extensor muscles. To reduce the complexity, four time-domain features were applied to extract the EMG signal. Furthermore, these features were used as the input of the machine learning. The machine learning evaluated in this study were k-nearest neighbor (k-NN), Naive Bayes (NB), decision tree (DT), and support vector machine (SVM). In the SoC implementation, the data acquisition, feature extraction, machine learning, and motor control process were implemented using a multi-thread algorithm. After the evaluation, the result showed that the pairing of the MAV feature and machine learning DT resulted in higher accuracy among other combinations (98.41%) with a computation time of ~1 ms. The implementation of the multi-thread algorithm in the pattern recognition system resulted in significant impact on the time processing.
Keywords: multi-thread; embedded system; Raspberry Pi; EMG; machine learning; time-domain feature; prosthetic hand multi-thread; embedded system; Raspberry Pi; EMG; machine learning; time-domain feature; prosthetic hand

Share and Cite

MDPI and ACS Style

Triwiyanto, T.; Caesarendra, W.; Purnomo, M.H.; Sułowicz, M.; Wisana, I.D.G.H.; Titisari, D.; Lamidi, L.; Rismayani, R. Embedded Machine Learning Using a Multi-Thread Algorithm on a Raspberry Pi Platform to Improve Prosthetic Hand Performance. Micromachines 2022, 13, 191. https://doi.org/10.3390/mi13020191

AMA Style

Triwiyanto T, Caesarendra W, Purnomo MH, Sułowicz M, Wisana IDGH, Titisari D, Lamidi L, Rismayani R. Embedded Machine Learning Using a Multi-Thread Algorithm on a Raspberry Pi Platform to Improve Prosthetic Hand Performance. Micromachines. 2022; 13(2):191. https://doi.org/10.3390/mi13020191

Chicago/Turabian Style

Triwiyanto, Triwiyanto, Wahyu Caesarendra, Mauridhi Hery Purnomo, Maciej Sułowicz, I Dewa Gede Hari Wisana, Dyah Titisari, Lamidi Lamidi, and Rismayani Rismayani. 2022. "Embedded Machine Learning Using a Multi-Thread Algorithm on a Raspberry Pi Platform to Improve Prosthetic Hand Performance" Micromachines 13, no. 2: 191. https://doi.org/10.3390/mi13020191

APA Style

Triwiyanto, T., Caesarendra, W., Purnomo, M. H., Sułowicz, M., Wisana, I. D. G. H., Titisari, D., Lamidi, L., & Rismayani, R. (2022). Embedded Machine Learning Using a Multi-Thread Algorithm on a Raspberry Pi Platform to Improve Prosthetic Hand Performance. Micromachines, 13(2), 191. https://doi.org/10.3390/mi13020191

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop