Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control
JOURNAL OF SENSORS, cilt.2024, sa.1, ss.1-17, 2024 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 2024 Sayı: 1
- Basım Tarihi: 2024
- Doi Numarası: 10.1155/2024/6808322
- Dergi Adı: JOURNAL OF SENSORS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
- Sayfa Sayıları: ss.1-17
- Yozgat Bozok Üniversitesi Adresli: Evet
Özet
In this study, a wearable exoskeleton arm was designed and controlled with different control methods to help people with muscle
disorders in their arms and support treatment. The developed robot arm was transferred to Simulink software with the Simmechanics application. Two electromyography (EMG) muscle sensors and the ADXL335 position and acceleration sensors attach to
the human arm’s biceps and triceps muscle areas. As the human moved the arm, data were obtained from the EMG muscle sensors
and the ADXL335 position and acceleration sensor. The received data were first trained with the fuzzy logic algorithm. The same
data were then trained with machine learning algorithms in Simulink software. It has been determined that the best result is
the quadratic support vector machine (SVM) algorithm. The fuzzy logic algorithm trained with the PID controller block and the
received sensor data have been added to the degrees of freedom regions that will enable rotation in the block diagram of the
previously exported system. Later, the fuzzy logic block was removed and the machine learning algorithm, the quadratic SVM
algorithm, was added. The designed system was operated with two different control systems, and the control algorithm closest to
the human arm movement was determined. In addition, each part of the system, whose design was prepared, was removed and
assembled separately with a 3D printer. ESP32 microcontroller development board was used to control the system, and it was run
in real-time with EMG muscle sensors and position sensors