Embedded machine learning using a multi-thread algorithm on a Raspberry Pi platform to improve prosthetic hand performance
Authors:
- Triwiyanto Triwiyanto,
- Wahyu Caesarendra,
- Mauridhi Hery Purnomo,
- Maciej Sułowicz,
- I Dewa Gede Hari Wisana,
- Dyah Titisari,
- Lamidi Lamidi,
- Rismayani Rismayani
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 a significant impact on the time processing.
- Record ID
- CUTf4e3f8710bd748b0b25d2f3c52272780
- Publication categories
- ;
- Author
- Journal series
- Micromachines, ISSN , e-ISSN 2072-666X, Monthly
- Issue year
- 2022
- Vol
- 13
- No
- 2
- Pages
- [1-16]
- Article number
- 191
- Other elements of collation
- fot.; schem.; tab.; wykr.; Bibliografia (na s.) - 14-16; Bibliografia (liczba pozycji) - 44; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 13, Iss. 2, Spec. Iss.
- Substantive notes
- Special Issue: Wearable Robotics
- Keywords in English
- multi-thread, embedded system, Raspberry Pi, EMG, machine learning, time-domain feature, prosthetic hand
- ASJC Classification
- ; ;
- DOI
- DOI:10.3390/mi13020191 Opening in a new tab
- URL
- https://www.mdpi.com/2072-666X/13/2/191 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 70
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 14
- Additional fields
- Indeksowana w: Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTf4e3f8710bd748b0b25d2f3c52272780/
- URN
urn:pkr-prod:CUTf4e3f8710bd748b0b25d2f3c52272780
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.