Estimating dynamic plantar pressure distribution from wearable inertial sensors using a hybrid CNN-BiLSTM architecture
By: Yihan Qian, Dong Sun, Zifan Xia, Enze Shao, Yang Song, József Sárosi, István Bíró, Zixiang Gao and Yaodong Gu
References
- A
kiba T., Sano S., Yanase T., Ohta T., Koyama M., Optuna: A next-generation hyperparameter optimization framework, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, Anchorage AK USA, 2019, 2623–2631, DOI: 10.1145/3292500.3330701. - B
ötzel K., Marti F.M., Rodríguez M.Á.C., Plate A., Vicente A.O., Gait recording with inertial sensors – how to determine initial and terminal contact, J. Biomech., 2016, 49 (1), 332–337, DOI: 10.1016/j.jbiomech.2015.12.035. - C
en X., Song Y., Yu P., Sun D., Simon J., Bíró I., Gu Y., Effects of plantar fascia stiffness on the internal mechanics of idiopathic pes cavus by finite element analysis: Implications for metatarsalgia, Comput. Methods Biomech. Biomed. Eng., 2024, 27 (14), 1961–1969, DOI: 10.1080/10255842.2023.2292723. - C
en X., Yu P., Song Y., Sun D., Liang M., Bíró I., Gu Y., Influence of medial longitudinal arch flexibility on lower limb joint coupling coordination and gait impulse, Gait Posture, 2024, 114, 208–214, DOI: 10.1016/j.gaitpost.2024.05.011. - C
hehab E.F., Andriacchi T.P., Favre J., Speed, age, sex, and body mass index provide a rigorous basis for comparing the kinematic and kinetic profiles of the lower extremity during walking, J. Biomech., 2017, 58, 11–20, DOI: 10.1016/j.jbiomech. 2017.04.014. - C
hhoeum V., Kim Y., Min S.D., A convolution neural network approach to access knee joint angle using foot pressure mapping images: a preliminary investigation, IEEE Sensors J., 2021, 21 (15), 16937–16944, DOI: 10.1109/JSEN.2021.3079516. - G
ao S., Song Y., Sun D., Cen X., Wang M., Lu Z., Gu Y., Impact of Becker muscular dystrophy on gait patterns: Insights from biomechanical analysis, Gait Posture, 2025. (Note: As this is a future publication, volume, page, and DOI information may not yet be available). - H
amdi M.M., Awad M.I., Abdelhameed M.M., Tolbah F.A., Lower limb motion tracking using IMU sensor network, Cairo International Biomedical Engineering Conference (CIBEC), 2014, 28–33, DOI: 10.1109/CIBEC.2014.7020957. - H
annink J., Kautz T., Pasluosta C.F., Gaßmann K.-G., Klucken J., Eskofier B.M., Sensor-based gait parameter extraction with deep convolutional neural networks, IEEE J. Biomed. Health Inform., 2017, 21 (1), 85–93, DOI: 10.1109/JBHI.2016.2636456. - H
azari A., Maiya A., Agouris I., Monteiro A., Shivashankara , Prediction of peak plantar pressure for diabetic foot: the regressional model, Foot, 2019, 40, 87–91, DOI: 10.1016/j.foot.2019.06.001. - I
offe S., Szegedy C., Batch normalization: accelerating deep network training by reducing internal covariate shift, Proceedings of the 32nd International Conference on Machine Learn -ing, PMLR, 2015, 448–456, http://proceedings.mlr.press/v37/ioffe15.html [Accessed: April 11, 2025]. - J
oo S.-B., Oh S.E., Sim T., Kim H., Choi C.H., Koo H., Mun J.H., Prediction of gait speed from plantar pressure using artificial neural networks, Expert Syst. Appl., 2014, 41 (16), 7398–7405, DOI: 10.1016/j.eswa.2014.06.002. - J
ordan K., Challis J.H., Newell K.M., Walking speed influences on gait cycle variability, Gait Posture, 2007, 26 (1), 128–134, DOI: 10.1016/j.gaitpost.2006.08.010. - K
huyagbaatar B., Tumurbaatar M., Tsenkherjav K., Purevsuren T., Shambaljamts T., Kim K., Kim Y.H., Kinematic comparison of snatch and clean lifts in weightlifters using wearable inertial measurement unit sensors, Phys. Act. Health, 2024, 8 (1), DOI: 10.56286/pah.v8i1.309. - K
ohavi R., A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), 1995, 2, 1137–1143. - M
ajumder S., Deen M.J., Wearable IMU-based system for real- -time monitoring of lower-limb joints, IEEE Sensors J., 2021, 21 (6), 8267–8275, DOI: 10.1109/JSEN.2020.3044800. - M
eyer C., Killeen T., Easthope C.S., Curt A., Bolliger M., Linnebank M., Zörner B., Filli L., Familiarization with treadmill walking: how much is enough?, Sci. Rep., 2019, 9, 5232, DOI: 10.1038/s41598-019-41721-0. - M
oon J., Lee D., Jung H., Choi A., Mun J.H., Machine learning strategies for low-cost insole-based prediction of center of gravity during gait in healthy males, Sensors (Basel), 2022, 22 (9), 3499, DOI: 10.3390/s22093499. - M
yn U., Link M., Awinda M., Xsens Mvn User Manual, Xsens Motion Technol. BV, Enschede Neth, 2015. - O’R
eilly M., Caulfield B., Ward T., Johnston W., Doherty C., Wearable inertial sensor systems for lower limb exercise detection and evaluation: a systematic review, Sports Med., 2018, 48 (5), 1221–1246, DOI: 10.1007/s40279-018- 0878-4. - O
rdóñez F.J., Roggen D., Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition, Sensors, 2016, 16 (1), 115, DOI: 10.3390/s16010115. - R
aza A., Mahmood I., Sultana T., Evaluation of weightbearing, walking stability, and gait symmetry in patients undergoing restoration following hip joint fractures, Int. J. Biomed. Eng. Technol., 2025, 47 (3), 195–213, DOI: 10.1504/IJBET.2025.144815. - R
oetenberg D., Luinge H., Slycke P., Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors, Xsens Technologies B.V., 2009. - S
ánchez -Rodríguez R., Martínez -Nova A., Escamilla - -Martínez E., Pedrera -Zamorano J.D., Can the foot posture index or their individual criteria predict dynamic plantar pressures?, Gait Posture, 2012, 36 (4), 591–595, DOI: 10.1016/j.gaitpost.2012.05.024. - S
chepers M., Giuberti M., Bellusci G., Xsens MVN: Consistent tracking of human motion using inertial sensing, Xsens Technologies, 2018, DOI: 10.13140/RG.2.2.22099.07205. - S
iami -Namini S., Tavakoli N., Namin A.S., The performance of LSTM and BiLSTM in forecasting time series, IEEE International Conference on Big Data (Big Data), 2019, 3285–3292, DOI: 10.1109/BigData47090.2019.9005997. - S
mith L.N., Cyclical learning rates for training neural networks, IEEE Winter Conference on Applications of Computer Vision (WACV), 2017, 464–472, DOI: 10.1109/WACV.2017.58. - S
ong Y., Cen X., Sun D., Bálint K., Wang Y., Chen H., Gu Y., Curved carbon-plated shoe may further reduce forefoot loads compared to flat plate during running, Sci. Rep., 2024, 14 (1), 13215, DOI: 10.1038/s41598-024-63385-z. - S
ong Y., Cen X., Wang M., Bálint K., Tan Q., Sun D., Zhang M., The influence of simulated worn shoe and foot inversion on heel internal biomechanics during running impact: A subject-specific finite element analysis, J. Biomech., 2025, 180, 112517, DOI: 10.1016/j.jbiomech.2024.112517. - S
ong Y., Cen X., Wang M., Gao Z., Tan Q., Sun D., Zhang M., A Systematic Review of Finite Element Analysis in Running Footwear Biomechanics: Insights for Running-Related Musculoskeletal Injuries, J. Sports Sci. Med., 2025, 24 (2), 370, (Note: Please verify if a DOI is available closer to the publication date). - V
aswani A., Shazeer N., Parmar J., Uszkoreit J., Jones L., Gomez A.N., Kaiser Ł., Polosukhin I., Attention is all you need, [in:] Advances in Neural Information Processing Systems 30 (NIPS 2017), Curran Associates, Inc., 2017, 5998–6008. https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html [Accessed: March 20, 2025]. - W
ang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P., Image quality assessment: From error visibility to structural similarity, IEEE Trans Image Process, 2004, 13 (4), 600–612, DOI: 10.1109/TIP.2003.819861. - W
u G., Siegler S., Allard P., Kirtley C., Leardini A., Rosenbaum M., Whittle M., D’Lima D.D., Cristofolini L., Witte H., Schmid O., Stokes I., Standardization and Terminology Committee of the International Society of Biomechanics, ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion. Part I: Ankle, hip, and spine, J. Biomech., 2002, 35 (4), 543–548, DOI: 10.1016/s0021-9290(01)00222-6. - X
iang L., Gu Y., Gao Z., Yu P., Shim V., Wang A., Fernandez J., Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors, Comput. Biol. Med., 2024, 170, 108016, DOI: 10.1016/j.compbiomed.2024.108016. - X
iao Y., Yin H., Zhang Y., Qi H., Zhang Y., Liu Z., A dual- -stage attention-based conv-LSTM network for spatio-temporal correlation and multivariate time series prediction, Int. J. Intell. Syst., 2021, 36 (5), 2036–2057, DOI: 10.1002/int.22370. - Y
ang F., King G.A., Dynamic gait stability of treadmill versus overground walking in young adults, J. Electromyogr. Kinesiol., 2016, 31, 81–87, DOI: 10.1016/j.jelekin.2016.09.004. - Z
han H., Kou J., Cao Y., Guo Q., Zhang J., Shi Y., Human gait phases recognition based on multi-source data fusion and BILSTM attention neural network, Measurement, 2024, 238, 115396, DOI: 10.1016/j.measurement.2024.115396. - Z
hang J.-T., Novak A.C., Brouwer B., Li Q., Concurrent validation of xsens MVN measurement of lower limb joint angular kinematics, Physiol. Meas., 2013, 34 (8), N63–N71, DOI: 10.1088/0967-3334/34/8/N63. - Z
hang L., Soselia D., Wang R., Gutierrez -Farewik E.M., Lower-limb joint torque prediction using LSTM neural networks and transfer learning, IEEE Trans. Neural Syst. Rehabil. Eng., 2022, 30, 600–609, DOI: 10.1109/TNSRE.2022.3156786. - Z
hu C., Song Y., Xu Y., Zhu A., Baker J.S., Liu W., Gu Y., Toe box shape of running shoes affects in-shoe foot displacement and deformation: a randomized crossover study, Bioengineering, 2024, 11 (5), 457, DOI: 10.3390/bioengineering11050457.
Language: English
Page range: 211 - 227
Submitted on: Jun 3, 2025
Accepted on: Jul 3, 2025
Published on: Aug 26, 2025
Published by: Wroclaw University of Science and Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Related subjects:
© 2025 Yihan Qian, Dong Sun, Zifan Xia, Enze Shao, Yang Song, József Sárosi, István Bíró, Zixiang Gao, Yaodong Gu, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.