Abstract
In the present project a prototype was developed, that models the lower limbs movement, unlike other commercial devices, this prototype seeks to obtain data through low-cost electronics in order to be replicated by the scientific community interested in this research line, it integrates electronic boards and programming software such as Arduino, NodeMCU, Accelerometers and Matlab. The sensors have been placed on a shield that facilitates their connection. The connection through a bus of all the sensors is presented in a NodeMCU board, through Wi-Fi sends a table of all this data for processed in Matlab. Data shows the main articular positions of the lower limbs of a person, using the homogeneous transformation matrices we obtain a seven-bar model that shows positions and accelerations produced by individual joints, the data obtained by the suit can be contrast with simulations performed in previous works, providing good results, and allowing future developments in topics such as gait analysis, teleoperation and modeling of physical therapies to ensure their effectiveness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Roetenberg, D., Luinge, H., Slycke, R.: Xsens MVN: full 6DOF human motion tracking using miniature inertial sensors. Xsens Technologies, April 2013
Dinua, D., Fayolasbc, M., Jacquetbc, M., Leguybc, E., Slavinskib, J., Houelc, N.: Accuracy of postural human-motion tracking using miniature inertial sensors. Procedia Eng. 147, 655–658 (2016)
Zihajehzadeh, S., Park, E.: A novel biomechanical model-aided IMU/UWB fusion for magnetometer-free lower body motion capture. IEEE Trans. Syst. Man Cybern. Syst. 47, 927–938 (2017)
Zheng, Y., Chan, K.C., Wang, C.C.: Pedalvatar: an IMU-based real-time body motion capture system using foot rooted kinematic model. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014) (2014)
Mota, F.A.O., Biajo, V.H.M., Mota, H.O., Vasconcelos, F.H.: A wireless sensor network for the biomechanical analysis of the gait. In: 2017 IEEE International on Instrumentation and Measurement Technology Conference (I2MTC) (2017)
Chen, C., Jafari, R., Kehtarnavaz, N.: A survey of depth and inertial sensor fusion for human action recognition. Multimedia Tools Appl. 76, 4405–4425 (2017)
Park, Y.L., Chen, B.R., Pérez-Arancibia, N.O., Young, D., Stirling, L., Wood, R.J., Goldfield, E.C., Nagpal, R.: Design and control of a bio-inspired soft wearable robotic device for ankle–foot rehabilitation. Bioinspir. Biom. 9(1) (2014)
Gavilanes, J., Pazmiño, A., Pérez, M., Tinoco, J.: Análisis del ciclo de marcha bípedo como base de la rehabilitación física en miembros inferiores (2016)
Biomec. http://www.biomec.com.co/Laboratorio-deportivo-3d.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Gavilanes, J.J., Lema, H., Jácome, J.R., Pazmiño, A.O., Zabala, L. (2018). Development of Prototype Suit for Modeling of Lower Limbs Using NodeMcu and Matlab. In: Botto-Tobar, M., Esparza-Cruz, N., León-Acurio, J., Crespo-Torres, N., Beltrán-Mora, M. (eds) Technology Trends. CITT 2017. Communications in Computer and Information Science, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-72727-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-72727-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72726-4
Online ISBN: 978-3-319-72727-1
eBook Packages: Computer ScienceComputer Science (R0)