Pengenalan Pola Dasar Angka berdasarkan Gerakan Tangan menggunakan Machine Learning

SYAFRIYADI NOR, MUHAMMAD AZIZ MUSLIM, MUHAMMAD ASWIN

Sari


ABSTRAK

Pengenalan gerakan tangan dianggap sebagai bagian penting dari interaksi manusia komputer, memungkinkan komputer untuk mengenali dan menafsirkan gerakan tangan dan menjalankan perintah. Penggunaan machine learning dimanfaatkan untuk mencari tren dan pola yang berbeda. Namun, tantangan untuk menerapkan machine learning menjadi bagaimana memilih di antara berbagai model berbeda digunakan untuk kumpulan data atau kasus berbeda. Tujuan dari penelitian ini adalah mengukur kinerja model machine learning yang diusulkan dengan pemilihan hyperparameter yang sesuai dalam mengenali 10 pola angka berdasarkan gerakan tangan di udara. Dalam makalah ini, model KNN, SVM, dan ANN-PSO diusulkan. Eksperimen dilakukan dengan mengumpulkan data gerakan yang berasal dari MPU-6050. Kinerja metode yang diusulkan dievaluasi menggunakan metrik standar seperti akurasi klasifikasi, presisi, recall, f1-score, dan AUC-ROC. Hasilnya menunjukkan bahwa akurasi rata-rata mencapai 87%.

Kata kunci: HCI, hand gesture recognition, machine learning, MPU-6050, pola

 

ABSTRACT

Hand gesture recognition is considered an essential part of human-computer interaction (HCI), enabling computers to recognize and interpret hand gesturesand execute  commands. The use of machine learning is utilized to look for different trends and patterns. However, the challenge for implementing machine learning becomes how to choose between different models used for different datasets or cases. This research aims to measure the performance of the proposed machine learning model by selecting the appropriate hyperparameters in recognizing 10 number patterns based on hand gestures in the air. In this paper, KNN, SVM, and ANN-PSO models are proposed. Experiments were carried by collecting gesture data from MPU-6050. The performance of the proposed method was evaluated using standard metrics such as classification accuracy, precision, recall, f1-score, and AUC-ROC. The results show that the average accuracy reaches 87%.

Keywords: HCI, hand gesture recognition, machine learning, MPU-6050, pattern 


Kata Kunci


HCI; Hand gesture recognition; machine learning; MPU-6050; Pola

Teks Lengkap:

PDF

Referensi


Allevard, T., Benoit, E., & Foulloy, L. (2006). Hand posture recognition with the fuzzy glove. Modern Information Processing, 417 - 427.

Abdullah, A., Abdul-Kadir, N. A., & Che Harun, F. K. (2020). An Optimization of IMU Sensors-Based Approach for Malaysian Sign Language Recognition, International Conference on Computing Engineering and Design (ICCED), (pp. 1 - 4).

Anwar, S., Sinha, S. K., Vivek, S., & Ashank, V. (2019). Hand gesture recognition: A survey. Lecture Notes in Electrical Engineering, 511(3), 365–371.

Elgeldawi, E., Sayed, A., Galal, A. R., dan Zaki, A. M. (2021). Hyperparameter tuning for machine learning algorithms used for arabic sentiment analysis. Informatics, 8(4), 1–21.

Espressif. (2021, Desember 23). ESP32 Series Datasheet. Retrieved from www.espressif.com.

Fernandez, R. A. S., Sanchez-Lopez, J. L., Sampedro, C., Bavle, H., Molina, M., & Campoy, P. (2016). Natural user interfaces for human-drone multi-modal interaction. International Conference on Unmanned Aircraft Systems (ICUAS), (pp. 1013–1022).

Filippeschi, A., Schmitz, N., Miezal, M., Bleser, G., Ruffaldi, E., dan Stricker, D. (2017). Survey of motion tracking methods based on inertial sensors: A focus on upper limb human motion. Sensors (Switzerland), 17(6), 1 - 40.

Gupta, R., & Kumar, A. (2021). Indian sign language recognition using wearable sensors and multi-label classification. Computers and Electrical Engineering, 90(October 2020), 106898.

Khomami, S. A., & Shamekhi, S. (2021). Persian sign language recognition using IMU and surface EMG sensors. Measurement: Journal of the International Measurement Confederation, 168(September 2020), 108471.

Krishnan, K. S., Saha, A., Ramachandran, S., & Kumar, S. (2018). Recognition of human arm gestures using Myo armband for the game of hand cricket. IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), (pp. 389 - 394).

Oudah, M., Al-Naji, A., & Chahl, J. (2020). Hand Gesture Recognition Based on Computer Vision: A Review of Techniques. Journal of Imaging, 6(8), 1-29.

Pramunanto, E., Sumpeno, S., & Legowo, R. S. (2017). Classification of hand gesture in Indonesian sign language system using naive bayes. International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation (ISSIMM), (pp. 187-191).

Rosalina, Yusnita, L., Hadisukmana, N., Wahyu, R. B., Roestam, R., & Wahyu, Y. (2018). Implementation of real-time static hand gesture recognition using artificial neural network. Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT ), (pp. 1-6).

Trigueiros, P., Ribeiro, F., & Reis, L. P. (2012). A comparison of machine learning algorithms applied to hand gesture recognition. 7th Iberian Conference on Information Systems and Technologies (CISTI), (pp. 1-6).

Wu, J., Sun, L., & Jafari, R. (2016). A Wearable System for Recognizing American Sign Language in Real-Time Using IMU and Surface EMG Sensors. IEEE Journal of Biomedical and Health Informatics, 20(5), 1281-1290.

Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295-316.

Yasen, M., & Jusoh, S. (2019). A systematic review on hand gesture recognition techniques, challenges and applications. PeerJ Computer Science, 2019(9), 1-30.

Zhang, X., Yang, Z., Chen, T., Chen, D., & Huang, M. C. (2019). Cooperative Sensing and Wearable Computing for Sequential Hand Gesture Recognition. IEEE Sensors Journal, 19(14), 5775-5783.




DOI: https://doi.org/10.26760/elkomika.v10i3.595

Refbacks

  • Saat ini tidak ada refbacks.


_______________________________________________________________________________________________________________________

ISSN (cetak) : 2338-8323 | ISSN (elektronik) : 2459-9638

diterbitkan oleh :

Teknik Elektro Institut Teknologi Nasional Bandung

Alamat : Gedung 20 Jl. PHH. Mustofa 23 Bandung 40124

Kontak : Tel. 7272215 (ext. 206) Fax. 7202892

Surat Elektronik : jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________

Statistik Pengunjung

Free counters!

Web Analytics Made Easy - StatCounter

Lihat Statistik Jurnal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License