Prediction of Gold Standard Gait Data from Inertial Data: A Machine Learning Approach

• William Fröhlich PUCRS
• Diego Pinheiro Feevale
• Sandro Rigo UNISINOS
• Rafael Baptista PUCRS
• César Marcon PUCRS

DOI: https://doi.org/10.5753/sbcas.2025.7298

Abstract

Biomechanics laboratories typically use kinematic cameras and force platforms as the gold standard for gait assessment. However, these systems are expensive and have limited availability. Wearable devices, equipped with sensors—primarily inertial sensors—can capture movement data, enabling the inference of human gait behavior. To enhance the quality of measurements obtained from wearables, this study investigates the feasibility of predicting kinematic parameters from inertial data collected by wearable sensors. Machine learning techniques, including Random Forest, XGBoost, and Gradient Boosting, were used to correlate inertial measurements with data from traditional motion capture systems. Feature importance analysis and SHAP highlighted the significance of velocity and acceleration in predicting kinematic parameters. Experimental results indicate that tree-based models, particularly Gradient Boosting and XGBoost, achieved the best performance, with coefficient of determination values close to 0.989, demonstrating the feasibility of the proposed approach.

References

Akhtaruzzaman, M., Shafie, A., and Khan, M. (2016). Gait analysis: Systems, technologies, and importance. Journal of Mechanics in Medicine and Biology, 16(07):1630003.

Benson, L., Clermont, C., Bošnjak, E., and Ferber, R. (2018). The use of wearable devices for walking and running gait analysis outside of the lab: A systematic review. Gait and Posture, 63:124–138.

BTS Bioengineering (2024a). BTS G-WALK - Wireless Sensor for Gait Analysis. [link]. Accessed: 2024-08-31.

BTS Bioengineering (2024b). BTS GAITLAB - 3D Motion Analysis System. [link]. Accessed: 2024-08-31.

da Rosa Tavares, J., Ullrich, M., Roth, N., Kluge, F., Eskofier, B., Gaßner, H., Klucken, J., Gladow, T., Marxreiter, F., da Costa, C., da Rosa Righi, R., and Victória Barbosa, J. (2023). utug: An unsupervised timed up and go test for parkinson’s disease. Biomedical Signal Processing and Control, 81:104394.

Delval, A., Betrouni, N., Tard, C., Devos, D., Dujardin, K., Defebvre, L., Labidi, J., and Moreau, C. (2021). Do kinematic gait parameters help to discriminate between fallers and non-fallers with parkinson’s disease? Clinical Neurophysiology, 132(2):536–541.

Desai, R., Martelli, D., Alomar, J., Agrawal, S., Quinn, L., and Bishop, L. (2024). Validity and reliability of inertial measurement units for gait assessment within a post stroke population. Topics in Stroke Rehabilitation, 31(3):235–243. PMID: 37545107.

He, Y., Chen, Y., Tang, L., Chen, J., Tang, J., Yang, X., Su, S., Zhao, C., and Xiao, N. (2024). Accuracy validation of a wearable imu-based gait analysis in healthy female. BMC Sports Science, Medicine and Rehabilitation, 16(1):2.

Jakob, V., Küderle, A., Kluge, F., Klucken, J., Eskofier, B., Winkler, J., Winterholler, M., and Gassner, H. (2021). Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with parkinson’s disease. Sensors, 21(22).

Kotiadis, D., Hermens, H., and Veltink, P. (2010). Inertial gait phase detection for control of a drop foot stimulator: Inertial sensing for gait phase detection. Medical Engineering and Physics, 32(4):287–297.

Kvist, A., Tinmark, F., Bezuidenhout, L., Reimeringer, M., Conradsson, D., and Franzén, E. (2024). Validation of algorithms for calculating spatiotemporal gait parameters during continuous turning using lumbar and foot mounted inertial measurement units. Journal of Biomechanics, 162:111907.

Millecamps, A., Lowry, K., Brach, J., Perera, S., Redfern, M., and Sejdić, E. (2015). Understanding the effects of pre-processing on extracted signal features from gait accelerometry signals. Computers in Biology and Medicine, 62:164–174.

Parashar, A., Parashar, A., Ding, W., Shabaz, M., and Rida, I. (2023). Data preprocessing and feature selection techniques in gait recognition: A comparative study of machine learning and deep learning approaches. Pattern Recognition Letters, 172:65–73.

Ripic, Z., Nienhuis, M., Signorile, J., Best, T., Jacobs, K., and Eltoukhy, M. (2023). A comparison of three-dimensional kinematics between markerless and marker-based motion capture in overground gait. Journal of Biomechanics, 159:111793.

Rousanoglou, E., Foskolou, A., Emmanouil, A., and Boudolos, K. (2024). Inertial sensing of the abdominal wall kinematics during diaphragmatic breathing in head standing. Biomechanics, 4(1):63–83.

Silva, L. and Stergiou, N. (2020). Chapter 7 - the basics of gait analysis. In Stergiou, N., editor, Biomechanics and Gait Analysis, pages 225–250. Academic Press.

Tsakanikas, V., Ntanis, A., Rigas, G., Androutsos, C., Boucharas, D., Tachos, N., Skaramagkas, V., Chatzaki, C., Kefalopoulou, Z., Tsiknakis, M., and Fotiadis, D. (2023). Evaluating gait impairment in parkinson’s disease from instrumented insole and imu sensor data. Sensors, 23(8).

Zhang, Y., Wang, M., Awrejcewicz, J., Fekete, G., Ren, F., and Gu, Y. (2017). Using gold-standard gait analysis methods to assess experience effects on lower-limb mechanics during moderate high-heeled jogging and running. Journal of visualized experiments : JoVE, 127:55714.