Enhancing Smart Wheelchair Control: A Comparative Study of Optical Flow and Haar Cascade for Head Movement

Nimatul Ma Muriyah; Paerin Paerin; Andik Yulianto

doi:10.63158/journalisi.v7i4.1302

Nimatul Ma Muriyah Batam International University, Indonesia
Paerin Paerin Batam International University, Indonesia
Andik Yulianto Batam International University, Indonesia

DOI: 10.63158/journalisi.v7i4.1302

Keywords: Head movement, Smart Wheelchair, Optical flow, Haar cascade classifier, Real-time

Abstract

The development of Artificial Intelligence, particularly in Computer Vision, has enabled real-time recognition of human movements such as head gestures, which can be utilized in smart wheelchairs for users with limited mobility. This study compares two lightweight non-deep-learning methods Lucas–Kanade Optical Flow and Haar Cascade Classifier for real-time head movement detection. Both methods were implemented in Python using OpenCV and tested in four basic directions (left, right, up, and down) under three different lighting conditions: bright, normal, and dim. Each condition consisted of 16 trials per method, resulting in a total of 96 trials. The evaluation focused on detection accuracy and decision time. Under bright lighting, Optical Flow achieved 87.5% accuracy with a decision time of 0.338-1.41 s, while Haar Cascade reached 50% accuracy with 0.616–1.20 s. Under normal lighting, Optical Flow maintained 87.5% accuracy with 0.89–1.21 s, compared to Haar Cascade’s 68.75% accuracy with 0.83–1.25 s. Under dim lighting, Optical Flow improved to 93.8% accuracy with 0.90–1.31 s, whereas Haar Cascade dropped to 62.5% accuracy with 0.89–1.58 s. These findings confirm that Optical Flow delivers more reliable and adaptive performance across varying illumination levels, making it more suitable for real-time smart wheelchair control. This study contributes to the development of affordable assistive technologies and highlights future directions for multi-user testing and hardware integration.

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References

M. A. Gunawan, H. S. Purba, N. A. B. Saputra, N. Wiranda, and M. H. Adini, “Perancangan Pendeteksi Wajah dengan Metode Haar Cascade dan Local Binary Pattern Berbasis OpenCV,” Comput. Educ. Technol. J., vol. 4, no. 1, p. 7, 2024, doi: 10.20527/cetj.v4i1.12332.

N. Ma Muriyah, J. H. Sim, and A. Yulianto, “Evaluating YOLOv5 and YOLOv8: Advancements in Human Detection,” J. Inf. Syst. Informatics, vol. 6, no. 4, pp. 2999–3015, 2024, doi: 10.51519/journalisi.v6i4.944.

A. Yulianto, W. Andreas, and S. Sabariman, “Perancangan Prototype Brankas Menggunakan Sistem Pengenalan Wajah Dengan Metode Convolutional Neural Network (CNN),” Telcomatics, vol. 8, no. 1, p. 10, 2023, doi: 10.37253/telcomatics.v8i1.7852.

H. Haeruddin, H. Herman, and P. P. Hendri, “Pengembangan Aplikasi Emoticon Recognition dan Facial Recognition menggunakan Algoritma Local Binary Pattern Histogram (LBPH) dan Convolutional Neural Network (CNN),” J. Teknol. Terpadu, vol. 9, no. 1, pp. 49–55, 2023, doi: 10.54914/jtt.v9i1.613.

M. Salehi, N. Javadpour, B. Beisner, M. Sanaei, and S. B. Gilbert, “Cybersickness Detection Through Head Movement Patterns: A Promising Approach,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 14735 LNAI, pp. 239–254, 2024, doi: 10.1007/978-3-031-60611-3_18.

C. H. Choi, J. Kim, J. Hyun, Y. Kim, and B. Moon, “Face Detection Using Haar Cascade Classifiers Based on Vertical Component Calibration,” Human-centric Comput. Inf. Sci., vol. 12, 2022, doi: 10.22967/HCIS.2022.12.011.

I. Print, F. L. Ramadini, and E. Haryatmi, “Penggunaan Metode Haar Cascade Classifier dan LBPH Untuk Pengenalan Wajah Secara Realtime,” InfoTekJar, vol. 6, no. 2, 2022.

V. Pradeep, “Fuzzy based Computing for Disabled using 3D Head Movement,” Inform., vol. 49, no. 15, pp. 1–14, 2025, doi: 10.31449/inf.v49i15.5399.

T. K. T. Zizi, S. Ramli, M. Wook, and M. A. M. Shukran, “Optical Flow-Based Algorithm Analysis to Detect Human Emotion from Eye Movement-Image Data,” J. Image Graph. Kingdom), vol. 11, no. 1, pp. 53–60, 2023, doi: 10.18178/joig.11.1.53-60.

J. Xie, C. Yang, W. Xie, and A. Zisserman, “Moving Object Segmentation: All You Need is SAM (and Flow),” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) , vol. 15481 LNCS, no. i, pp. 291–308, 2025, doi: 10.1007/978-981-96-0972-7_17.

Y. Fang, W. Liu, and S. Zhang, “Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals,” Appl. Sci., vol. 13, no. 13, 2023, doi: 10.3390/app13137572.

E. S. Wahyuni, Z. Iqbal, and D. Farahiya, “Detection of Human Movement Direction Using Optical Flow Analisys on Multiple Camera Angles,” J. Nas. Tek. ELEKTRO, vol. 10, no. 2, Jul. 2021, doi: 10.25077/jnte.v10n2.924.2021.

H. Hameed et al., “Wi-Fi and Radar Fusion for Head Movement Sensing Through Walls Leveraging Deep Learning,” pp. 1–14.

O. A. Naser, S. Mumtazah, K. Samsudin, M. Hanafi, S. M. B. Shafie, and N. Z. Zamri, “Comparative Analysis of MTCNN and Haar Cascades for Face Detection in Images with Variation in Yaw Poses and Facial Occlusions,” J. Commun. Softw. Syst., vol. 21, no. 1, pp. 109–119, 2025, doi: 10.24138/jcomss-2024-0084.

H. Guo and Y. Wang, “Enhanced Optical Flow Estimation via Multiscale Kernel Selection and Super-Resolution Integration,” Res. Sq., pp. 0–23, 2025.

M. Gehrig, M. Muglikar, and D. Scaramuzza, “Dense Continuous-Time Optical Flow From Event Cameras,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 7, pp. 4736–4746, 2024, doi: 10.1109/TPAMI.2024.3361671.

C. Murui, Z. Zheng, T. Wenkai, and L. Junlin, “Research on Largemouth Bass Target Recognition and Tracking Utilizing the Optical Flow Approach,” Adv. Comput. Signals Syst., vol. 8, no. 1, pp. 142–152, 2024, doi: 10.23977/acss.2024.080117.

Jamal Rosid, “Face Recognition Dengan Metode Haar Cascade dan Facenet,” Indones. J. Data Sci., vol. 3, no. 1, pp. 30–34, 2022, doi: 10.56705/ijodas.v3i1.38.

N. A. Mohd Ariffin, U. A. Gimba, and A. Musa, “Face Detection based on Haar Cascade and Convolution Neural Network (CNN),” J. Adv. Res. Comput. Appl., vol. 38, no. 1, pp. 1–11, 2025, doi: 10.37934/arca.38.1.111.

A. Z. Larasati and F. Utaminingrum, “Sistem Kendali Pengikut Terarah Smart Wheelchair pada Pencahayaan Beragam Menggunakan Gamma Correction dan YOLOv8n Termodifikasi,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 2, pp. 2–6, 2025.

J. Zhong, T. Chen, and L. Yi, “Face expression recognition based on NGO-BILSTM model,” Front. Neurorobot., vol. 17, no. 2, pp. 212–218, 2023, doi: 10.3389/fnbot.2023.1155038.

A. Riza Mufita and F. Utaminingrum, “Deteksi Arah Pergerakan Kepala untuk Navigasi pada Kursi Roda Pintar Menggunakan Kombinasi Metode Berbasis YOLOv8N,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 2, pp. 2548–964, 2025.

U. Masud, N. Abdualaziz Almolhis, A. Alhazmi, J. Ramakrishnan, F. Ul Islam, and A. Razzaq Farooqi, “Smart Wheelchair Controlled Through a Vision-Based Autonomous System,” IEEE Access, vol. 12, no. March, pp. 65099–65116, 2024, doi: 10.1109/ACCESS.2024.3395656.

I. Komang Somawirata and F. Utaminingrum, “Smart wheelchair controlled by head gesture based on vision,” J. Phys. Conf. Ser., vol. 2497, no. 1, 2023, doi: 10.1088/1742-6596/2497/1/012011.

W. Krisna Wijaya, I. Komang Somawirata, and R. Putra Muhammad Davi Labib, “Deteksi Objek Menggunakan YOLO V3 Untuk Keamanan Pada Pergerakan Kursi Roda Elektrik,” Nucl. J., vol. 1, no. 2, pp. 94–98, 2022, doi: 10.32492/nucleus.v1i2.49.