Analysis of Smartphone Camera Stabilization Based on Gyroscope Using Exponential Smoothing in Mobile Applications
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Jul 7, 2026
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Abstract
Camera usage on mobile devices under dynamic conditions frequently induces vibration (jitter) and unstable orientation changes. This study aims to analyze the effectiveness of the Exponential Smoothing method in reducing rotational fluctuations of a gyroscope-based smartphone camera in a mobile application. An experimental approach was employed across six movement scenarios, namely stationary conditions, slow movement, fast movement, walking, stair climbing, and vehicle riding, by measuring rotation angle values and jitter values before and after stabilization. The results indicate a significant reduction in jitter following the application of Exponential Smoothing across all testing scenarios. The gyroscope-based stabilization system utilizing this method is proven effective in improving smartphone camera rotation stability in mobile applications.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Suci Lestari MS, Tito Pinandita, & Dimara Kusuma Hakim. (2026). Analysis of Smartphone Camera Stabilization Based on Gyroscope Using Exponential Smoothing in Mobile Applications. Jurnal E-Komtek, 10(1), 257-274. https://doi.org/10.37339/e-komtek.v10i1.3285
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[4] M. S. Alam, M. A. Morshidi, T. S. Gunawan, R. F. Olanrewaju, and F. Arifin, “Pose estimation algorithm for mobile augmented reality based on inertial sensor fusion,” International Journal of Electrical and Computer Engineering, vol. 12, no. 4, pp. 3620–3631, Aug. 2022, doi: 10.11591/ijece.v12i4.pp3620-3631.
[5] R. I. Alfian, A. Ma’Arif, and S. Sunardi, “Noise reduction in the accelerometer and gyroscope sensor with the Kalman filter algorithm,” Journal of Robotics and Control (JRC), vol. 2, no. 3, pp. 180–189, May 2021, doi: 10.18196/jrc.2375.
[6] K. Nirmal et al., “Noise modeling and analysis of an IMU-based attitude sensor: improvement of performance by filtering and sensor fusion,” Aug. 2016, doi: 10.1117/12.2234255.
[7] J. Everette S. Gardner, “Exponential Smoothing: The State of the Art,” J. Forecast., vol. 4, pp. 1–28, 1985.
[8] M. B. Alatise and G. P. Hancke, “Pose estimation of a mobile robot based on fusion of IMU data and vision data using an extended kalman filter,” Sensors (Switzerland), vol. 17, no. 10, Oct. 2017, doi: 10.3390/s17102164.
[9] F. Han, L. Xie, Y. Yin, H. Zhang, G. Chen, and S. Lu, “Video Stabilization for Camera Shoot in Mobile Devices via Inertial-Visual State Tracking,” IEEE Trans. Mob. Comput., vol. 20, no. 4, pp. 1714–1729, Apr. 2021, doi: 10.1109/TMC.2019.2961313.
[10] M. Kok, J. D. Hol, and T. B. Schön, “Using inertial sensors for position and orientation estimation,” 2017, Now Publishers Inc. doi: 10.1561/2000000094.
[11] L. Zheng, X. Zhan, and X. Zhang, “Nonlinear complementary filter for attitude estimation by fusing inertial sensors and a camera,” Sensors (Switzerland), vol. 20, no. 23, pp. 1–19, Nov. 2020, doi: 10.3390/s20236752.
[12] A. Karpenko, D. Jacobs, J. Baek, and M. Levoy, “Digital Video Stabilization and Rolling Shutter Correction using Gyroscopes.”
[13] R. J. Hyndman and G. Athanasopoulos, “Forecasting: principles and practice,” 3rd edition, OTexts: Melbourne, Australia, OTexts.com/fpp3. Accessed on may 10 2026.
[14] A. M. Sabatini, “Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing,” IEEE Trans Biomed Eng. 2006 Jul;53(7):1346-56. doi: 10.1109/TBME.2006.875664. PMID: 16830938.
[15] E. M. Day, R. S. Alcantara, M. A. McGeehan, A. M. Grabowski and M. E. Hahn, “Low-pass filter cutoff frequency affects sacral-mounted inertial measurement unit estimations of peak vertical ground reaction force and contact time during treadmill running,” J Biomech. 2021 Apr 15;119:110323. doi: 10.1016/j.jbiomech.2021.110323. Epub 2021 Feb 9. PMID: 33609984.