dynamics sensing devices and motion analysis
application to sports training and healthcare
  Using our expertise in motion dynamics of complex structures we develop new techniques to quantify and understand human motions. We use dynamics measurement of movements and develop algorithms to analyze this data. Our research is used for sports training and healthcare but can also be used in human-machine collaboration.
related publications

Y. Jiang, V. Hernandez, G. Venture, D. Kulic, B. Chen, Prediction of fatigue in exercises using wearable and force plate data: A Machine Learning/Deep Learning approach, Sensors, 21(4), 1499, 2021.

Chakraborty, S., Jain, S., Nandy, A. et al. Pathological Gait Detection Based on Multiple Regression Models Using Unobtrusive Sensing Technology. J Sign Process Syst, 2020.

E. Coronado, G. Venture, Towards IoT-aided Human-Robot Interaction using NEP and ROS, a Platform-Independent, Accessible and Distributed Approach, Sensors, Vol. 20, No. 5, 1500, 2020. doi:10.3390/s20051500 pdf

V. Hernandez, D. Kulic, G. Venture, Adversarial autoencoder for visualization and classification of human activity: application to Wii Balance Board, J. of Biomechanics, 2020.

S. Chakraborty, S. Jain, A. Nandy, T. Yamaguchi, V. Bonnet, G. Venture, Accuracy of image data stream of a markerless motion capture system in determining the local dynamic stability and joint, J. of Biomechanics, 2020.

V. Hernandez, T. Suzuki, G. Venture, Convolutional and Recurrent Neural Network for Human Activity Recognition: application on American Sign Language, PlosOne, 2020.

F. Rida, L. Rincon, L. E. Coronado, A. Nait Ali, G. Venture, From motion to emotion prediction: a hidden biometrics approach, Hidden Biometrics, pp. 185-202, Springer, 2019. ISBN 978-981-13-0956-4

V. Hernandez, N. Rezoug, P. Gorce, G. Venture, Force Feasible Set prediction with artificial neural network and musculoskeletal model, Computer Methods in Biomechanics and Biomedical Engineering, No. 21, Vol. 14, pp. 740-749, 2018. DOI

V. Hernandez, N. Rezoug, P. Gorce, G. Venture, Wheelchair propulsion: force orientation prediction with Recurrent Neural Network, J. of Biomechanics, No 78, pp. 166-171, 2018.

T. Robert, P. Leborgne, M. Abid, V. Bonnet, G. Venture, R. Dumas, Whole body segment inertia parameters estimation from movement and ground reaction forces: a feasibility study, Computer Methods in Biomechanics and Biomedical Engineering, No. 20 (sup1), pp. 175-176, 2018. 10.1080/10255842.2017.1382919

V. Joukov, V. Bonnet, M. Karg, G. Venture, and D. Kulic, Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation , IEEE Trans. on Neural Systems & Rehabilitation Engineering, Vol. 26, No. 2, pp. 407-418, 2018. 10.1109/TNSRE.2017.2659730

S. Futamure, V. Bonnet, R. Dumas, G. Venture, A sensitivity analysis method for the body segment inertial parameters based on ground reaction and joint moment regressor matrices, J. of Biomechanics, Vol. 64, No. 7, pp. 85-92, 2017. doi:

V. Bonnet, C. Azevedo Coste, T. Robert, P. Fraisse, G. Venture, Optimal External Wrench Distribution During a Multi-Contact Sit-to-Stand Task, IEEE Trans. on Neural Systems & Rehabilitation Engineering, Vol. 25, No. 7, pp. 987-997, 2017. 10.1109/TNSRE.2017.2676465 ieeexplore

V. Bonnet, R. Dumas, A. Cappozzo, V. Joukov, G. Daune, D. Kulić, P. Fraisse, S. Andary, G. Venture, A constrained extended Kalman filter for the optimal estimate of kinematics and kinetics of a sagittal symmetric exercise, J. of Biomechanics, Vol. 62C, pp. 140-147, 2017. DOI:

V. Bonnet, V. Richard, V. Camomilla, G. Venture, A. Cappozzo, R. Dumas, Joint kinematics estimation using a multi-body kinematics optimisation and an extended Kalman filter, and embedding a soft tissue artefact model, J. of Biomechanics, Vol. 62C, pp. 148-155, 2017. DOI:

C. Hansen, G. Venture, N. Rezzoug, P. Gorce, B. Isableu. Reconstructing the postural sway using body segment inertial parameters, PLOSONE, vol.12 No.6, pp.1-14, 2017. 10.1371/journal.pone.0180011

V. Hernandez, G. Venture, N. Rezzoug, P. Gorce, Improving the upper-limb force feasible set evaluation by muscles maximal isometric force identification and cocontraction factors , J. of Biomechanics, Vol. 57, pp.131-135, 2017. 10.1016/j.jbiomech.2017.03.021

V.Bonnet, V.Joukov, D.Kulić, P.Fraisse, N.Ramdani, G.Venture, Monitoring of Hip and Knee Joint Angles Using A Single Inertial Measurement Unit During Rehabilitation , IEEE Sensors Journal, vol. 16, no. 6, pp. 1557-1564, 2016. doi: 10.1109/JSEN.2015.2503765

V. Bonnet, G. Venture, Fast determination of the planar body segment inertial parameters using affordable sensors, IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol. 23, No. 4, pp. 628-635, 2015. pdf@IEEE Explore

V. Bonnet, C. Azevedo-Coste, L. Lapierre, J. Cadic, P. Fraisse, R. Zapata, G. Venture, C. Geny, Towards an affordable mobile analysis platform for pathological walking assessment, Robotics and Autonomous Systems, Vol. 66, pp. 116–128, 2015. DOI: pdf@ScienceDirect

G. Venture, H. Kadone, T. Zhang, J. Grezes, A. Berthoz, H. Hicheur, Recognizing Emotions Conveyed by Human Gait, Int. J. of Social Robotics, Vol. 6, No. 4, pp. 621-632, 2014. DOI: 10.1007/s12369-014-0243-1 pdf

C. Hansen, G. Venture, N. Rezzoug, P. Gorce, B. Isableu, An individual and dynamic Body Segment Inertial Parameter validation method using ground reaction forces, J. of Biomechanics, 47(7), pp. 1577-1581, 2014. pdf@elsevier

M. Karg, G. Venture, J. Hoey, D. Kulic, Human Movement Analysis as a Measure for Fatigue: A Hidden Markov-Based Approach, IEEE Trans. on Neural Systems & Rehabilitation Engineering, Vol. 22, No. 3, pp. 1-12, 2014. DOI:10.1109/TNSRE.2013.2291 pdf@ieeexplore



copyright Gentiane Venture 2009