Platform for Multimodal Signal Acquisition for the Control of Lower Limb Rehabilitation Devices

Douglas Ruy Soprani S. Araujo, Thomaz Rodrigues Botelho, Camila Rodrigues C. Carvalho, Anselmo Frizera, Andre Ferreira, Eduardo Rocon

Abstract

Patients with some sort of motor disability may benefit from robotic rehabilitation since it can provide more control, accuracy and variety of training modes. This enhances the efficiency of the rehabilitation and, therefore, the recovery of the patient. Assistive devices, like exoskeletons or orthoses, can make use of physiological data, such as electromyography (EMG) and electroencephalography (EEG), in order to detect the movement intention. Combination of data can potentially improve the adaptability of assistive devices with respect to the individual demands. Different methods can be applied depending on the neuromuscular disorder, therapy or assistive device. In this work, we present a multimodal interface which integrates EEG, EMG and inertial sensors (IMU) signals. Experiments were conducted with healthy subjects performing lower limb motor tasks. The aim of the proposed system is to analyze the movement intention (EEG signal), the muscle activation (EMG signal) and the limb motion onset (IMU signal). An experimental protocol is proposed. The results obtained showed that the system is capable to acquire and process the biological signals synchronously. Results indicated that the system is able to identify the movement intention, based on the EEG signal, the movement anticipation, based on the muscle activation, and the limb motion onset.

References

  1. Denève, A., Moughamir, S., Afilal, L., and Zaytoon, J. (2008). Control system design of a 3-DOF upper limbs rehabilitation robot. Computer methods and programs in biomedicine, 89(2):202-14.
  2. Favre, J., Aissaoui, R., Jolles, B. M., de Guise, J. a., and Aminian, K. (2009). Functional calibration procedure for 3D knee joint angle description using inertial sensors. Journal of biomechanics, 42(14):2330-5.
  3. Gallego, J. A., Ibán˜ ez, J., Dideriksen, J. L., Serrano, J. I., del Castillo, M. D., Farina, D., and Rocon, E. (2012). A multimodal human-robot interface to drive a neuroprosthesis for tremor management. IEEE Transactions on Systems, Man, and Cybernetics, 42(6):1159- 1168.
  4. Hermens, H. J., Freriks, B., Merletti, R., Stegeman, D., Blok, J., Rau, G., Disselhorst-Klug, C., and Hägg, G. (1999). European recommendations for surface electromyography.
  5. Ibán˜ ez, J., Serrano, J., del Castillo, M., Gallego, J., and Rocon, E. (2013). Online detector of movement intention based on EEG-Application in tremor patients. Biomedical Signal Processing and Control, 8(6):822- 829.
  6. Ju, M.-S., Lin, C.-C. K., Lin, D.-H., Hwang, I.-S., and Chen, S.-M. (2005). A rehabilitation robot with forceposition hybrid fuzzy controller: hybrid fuzzy control of rehabilitation robot. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 13(3):349-58.
  7. Kirchner, E. A., Tabie, M., and Seeland, A. (2014). Multimodal movement prediction - towards an individual assistance of patients. PloS one, 9(1):e85060.
  8. Muralidharan, A., Chae, J., and Taylor, D. M. (2011). Extracting Attempted Hand Movements from EEGs in People with Complete Hand Paralysis Following Stroke. Frontiers in neuroscience, 5(March):39.
  9. Pfurtscheller, G. and Lopes da Silva, F. H. (1999). Eventrelated EEG/MEG synchronization and desynchronization: basic principles. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 110(11):1842-57.
  10. Pons, J. L. (2008). Wearable Robots: Biomechatronic Exoskeletons, Chapter 4. John Wiley & Sons.
  11. Tabie, M. and Kirchner, E. A. (2013). EMG onset detection - comparison of different methods for a movement prediction task based on EMG. In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pages 242-247.
  12. Tsukahara, A., Hasegawa, Y., and Sankai, Y. (2009). Standing-up motion support for paraplegic patient with Robot Suit HAL. 2009 IEEE International Conference on Rehabilitation Robotics, pages 211-217.
Download


Paper Citation


in Harvard Style

Ruy Soprani S. Araujo D., Rodrigues Botelho T., Rodrigues C. Carvalho C., Frizera A., Ferreira A. and Rocon E. (2014). Platform for Multimodal Signal Acquisition for the Control of Lower Limb Rehabilitation Devices . In Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX, ISBN 978-989-758-056-7, pages 49-55. DOI: 10.5220/0005138900490055


in Bibtex Style

@conference{neurotechnix14,
author={Douglas Ruy Soprani S. Araujo and Thomaz Rodrigues Botelho and Camila Rodrigues C. Carvalho and Anselmo Frizera and Andre Ferreira and Eduardo Rocon},
title={Platform for Multimodal Signal Acquisition for the Control of Lower Limb Rehabilitation Devices},
booktitle={Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,},
year={2014},
pages={49-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005138900490055},
isbn={978-989-758-056-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,
TI - Platform for Multimodal Signal Acquisition for the Control of Lower Limb Rehabilitation Devices
SN - 978-989-758-056-7
AU - Ruy Soprani S. Araujo D.
AU - Rodrigues Botelho T.
AU - Rodrigues C. Carvalho C.
AU - Frizera A.
AU - Ferreira A.
AU - Rocon E.
PY - 2014
SP - 49
EP - 55
DO - 10.5220/0005138900490055