The goal of these studies is to understand how movements of the body can be harnessed and trained to control electrically powered wheelchairs. Advanced wheelchair technology is often perceived to be a barrier by a large number of potential wheelchair users. An approach is proposed for the removal of this barrier based on adapting the assistive technology to the residual unconstrained mobility of the patients and on enhancing motor learning. This exploratory project aims at establishing the feasibility of such an approach and at developing training methods based on the identification of natural motions and on the use of virtual reality (VR). The proposed studies will be carried out on quadriplegic spinal cord injured patients with complete or incomplete cervical injuries. Healthy volunteers will also participate in these study to fine-tune the experimental apparatus and to provide a reference baseline to assess learning and coordination. Subjects will wear a novel upper-body sensing garment. A total of 52 electrical signals generated by the garment will be modulated by movements of the wrist, elbow, shoulder and torso. These signals will be mapped into the velocity commands for a simulated wheelchair. Subjects will wear VR-goggles and a head tracker, which will provide them with a immersive view of a computer-generated environment from the perspective of the simulated wheelchair. The combination of virtual reality environments and wearable signal technology will provide a framework for evaluating training protocols that would not be feasible with actual wheelchairs. The proposed studies are organized in two specific aims:
(Aim 1.) To identify motor primitives for the control of a virtual wheelchair by unrestricted upper body motions Three well-established signal processing techniques - Principal Component Analysis, Independent Component Analysis and Isomap - will be used and compared for extracting low-dimensional signal patterns from the garment signals (Aim 2.) To identify maps and procedures that facilitate motor learning. The signal patterns extracted from Aim 1 will be used to design and test new transformations from subject motions to wheelchair commands. A well- known machine learning technique -least mean squares gradient descent - will be tested for matching the natural motor primitives of the subjects with an appropriate set of control signals to the wheelchair. Finally the safe VR environment will allow us to test whether it is most efficient to learn by gradually speeding up wheelchair motions or by gradually slowing them down. The results of these studies are expected to guide the development of new technology for assistive devices based on human motor learning and on engineering of adaptive control. Many disabled individuals are facing difficult challenges to take advantage of assistive technologies. In particular the safe and efficient use of powered wheelchair is limited by the need for patients to learn to operate their control apparatus. The proposed studies will investigate the possibility to reverse this situation and take advantage of advanced technologies for adapting the control apparatus to the residual skills of the patients. ? ?
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| Vato, Alessandro; Semprini, Marianna; Maggiolini, Emma et al. (2012) Shaping the dynamics of a bidirectional neural interface. PLoS Comput Biol 8:e1002578 |
| Casadio, Maura; Ranganathan, Rajiv; Mussa-Ivaldi, Ferdinando A (2012) The body-machine interface: a new perspective on an old theme. J Mot Behav 44:419-33 |
| Mussa-Ivaldi, Ferdinando A; Casadio, Maura; Danziger, Zachary C et al. (2011) Sensory motor remapping of space in human-machine interfaces. Prog Brain Res 191:45-64 |
| Casadio, M; Pressman, A; Acosta, S et al. (2011) Body machine interface: remapping motor skills after spinal cord injury. IEEE Int Conf Rehabil Robot 2011:5975384 |
| Casadio, Maura; Pressman, Assaf; Fishbach, Alon et al. (2010) Functional reorganization of upper-body movement after spinal cord injury. Exp Brain Res 207:233-47 |
| Mussa-Ivaldi, Ferdinando A; Alford, Simon T; Chiappalone, Michela et al. (2010) New Perspectives on the Dialogue between Brains and Machines. Front Neurosci 4:44 |
| Casadio, Maura; Pressman, Assaf; Danziger, Zachary et al. (2009) Functional reorganization of upper-body movements for wheelchair control. Conf Proc IEEE Eng Med Biol Soc 2009:4607-10 |
| Danziger, Zachary; Fishbach, Alon; Mussa-Ivaldi, Ferdinando A (2009) Learning algorithms for human-machine interfaces. IEEE Trans Biomed Eng 56:1502-11 |
| Mussa-Ivaldi, F A; Danziger, Z (2009) The remapping of space in motor learning and human-machine interfaces. J Physiol Paris 103:263-75 |
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