RoboGait Studio: Multi-Sensor Gait Analysis and Synchronization
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13732Keywords:
Mobile robots, Computer vision, Biomedical systems, Motion analysis, Sensor fusion, Data processingAbstract
RoboGait Studio is a software framework designed for synchronized multi-sensor gait analysis and cross-system comparison. The platform integrates participant and trial management, signal visualization, temporal synchronization, gait-cycle normalization, and comparative analysis within a unified workflow. Temporal alignment is achieved using ankle-distance trajectories extracted from independently acquired gait recordings, avoiding hardware-level synchronization requirements. The framework was evaluated using paired gait data collected from a mobile robot-assisted RGB-D acquisition system and a VICON motioncapture system. Experimental results demonstrated accurate synchronization and good agreement between systems for representative spatiotemporal and kinematic gait parameters. The proposed framework facilitates consistent comparison of gait data acquired from heterogeneous sensing modalities and provides a flexible foundation for future online and real-time gait-analysis applications.
References
Balta, H., Velipasalar, S., Cavusoglu, M. C., 2024. RGB-D Sensor-Based Gait Analysis for Neurological Rehabilitation and Clinical Assessment. IEEE Access, 12, 25411–25429. DOI: 10.1109/ACCESS.2024.3361287
Barzyk, M., Kwolek, B., Michalczuk, A., 2024. Robot-Assisted Gait Monitoring Using RGB-D Sensing Technologies in Clinical Environments. Robotics and Autonomous Systems, 176, 104698. DOI: 10.1016/j.robot.2024.104698
Clark, R. A., Mentiplay, B. F., Pua, Y. H., Bower, K. J., 2021. Reliability and Validity of Technology-Based Gait Assessment in Clinical Applications. Sensors, 21(7), 2308. DOI: 10.3390/s21072308
Fritsch, F. N., Carlson, R. E., 1980. Monotone Piecewise Cubic Interpolation. SIAM Journal on Numerical Analysis, 17(2), 238–246. DOI: 10.1137/0717021
Han, X., Guffanti, D., Brunete, A., 2025. A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis. Sensors, 25(2), 498. DOI: 10.3390/s25020498
Lagomarsino, M., Rossi, S., Bianchi, M., 2024. Markerless Motion Capture Technologies for Biomechanical Gait Assessment: A Systematic Review. Journal of Biomechanics, 172, 112245. DOI: 10.1016/j.jbiomech.2024.112245
Mentiplay, B. F., Perraton, L. G., Bower, K. J., et al., 2021. Gait Assessment Using Wearable and Vision-Based Technologies in Neurological Populations. Gait & Posture, 84, 277–293. DOI: 10.1016/j.gaitpost.2020.12.017
Muro-de-la Herran, A., Garcia-Zapirain, B., Mendez-Zorrilla, A., 2014. Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems. In: Highlights of Practical Applications of Heterogeneous Multi-Agent Systems. Springer, pp. 407–414. DOI: 10.1007/978-3-319-07767-340
Smirnova, D., Sazonov, E., Huo, Z., 2022. Evaluation of Kinect-Based Gait Analysis Systems Compared with Optical Motion Capture. Sensors, 22(9), 3275. DOI: 10.3390/s22093275
VICON Motion Systems Ltd., 2024. Vicon Nexus Documentation. Available online: https://www.vicon.com/
Vun, C. H., Lee, J. Y., Lim, W. H., et al., 2024. Recent Advances in Markerless Gait Analysis Technologies for Clinical and Rehabilitation Applications. Biomedical Signal Processing and Control, 89, 105741. DOI: 10.1016/j.bspc.2023.105741
Winter, D. A., 2009. Biomechanics and Motor Control of Human Movement (4th Edition). John Wiley & Sons.
Yeung, L. F., Yang, Z., Cheng, K. C. C., Du, D., Tong, R. K. Y., 2021. Evaluation of the Microsoft Azure Kinect for Clinical Gait Analysis. PLoS ONE, 16(2), e0246073. DOI: 10.1371/journal.pone.0246073
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Copyright (c) 2026 Xiaofeng Han, Diego Guffanti, Alberto Brunete, Miguel Hernando, David Álvarez
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
