Summary: Researchers have developed a new algorithm for a walking system that allows those with spinal cord injury to regain mobility. Researchers say their system, Smart Walk Assist, is designed to promote natural walking patterns in patients, so the nervous system can learn how to walk again.
Source: EPFL.
A mobile harness suspended from the ceiling is now equipped with intelligent motion analysis for tailored walking rehabilitation in people suffering from spinal cord injury, stroke and other neurological disorders affecting gait.
Scientists from NCCR Robotics at EPFL and at the Lausanne University Hospital (CHUV) developed an algorithm that adjusts how a mobile harness, suspended from the ceiling, assists patients suffering from spinal cord injury or stroke. In a clinical study with over 30 patients, the scientists showed that the patients wearing the smart walking assist immediately improved their locomotor abilities, enabling them to perform activities of daily living that would not be possible without the support. The results are published in the July 20th edition of Science Translational Medicine.
In rehabilitation involving neurological disorders or injury, teaching the nervous system to adopt the correct movements is a major challenge. The loss of muscle mass that prevents people from walking correctly, as does the neurological wiring that needs to be trained to relearn proper posture and walking movements. As long as the patient repeats unnatural movements, the nervous system will keep on remembering the flawed motion.
The idea of the smart walking assist is to promote natural walking in patients so that the nervous system learns how to walk normally again. Body-weight support systems are already used in rehabilitation. In this latest study, it is the first time such a support system operates in conjunction with an algorithm that tailors the assistance to each and every patient.
Relieving the patient of his own weight
The algorithm is based on careful monitoring of the patient as he or she moves, including parameters like leg movement, length of stride and muscle activity. Based on these observations, the algorithm determines the forces to be applied to the trunk of the body, via the smart walking assist, in order to enable natural walking patterns. Concretely, this translates into either relieving the patient of his or her own weight, pushing the patient forwards or backwards, to one side or the other, or a combination of the above, for a more natural posture. “I expect that this platform will play a critical role in the rehabilitation of walking for people with neurological disorders,” says Grégoire Courtine, neuroscientist at EPFL and at the Lausanne University Hospital.
The research results triggered the development of the next-generation smart walking assist device, termed RYSEN, which is performed under the umbrella of EUROSTARS, a European Union subsidy project. The collaboration is European with partners in Switzerland and the Netherlands, including EPFL, Technical University of Delft, Motek, the EPFL spin-off G-Therapeutics and the clinical partner SUVA in Sion.
Source: Hillary Sanctuary – EPFL
Image Source: NeuroscienceNews.com image is adapted from the EPFL video.
Video Source: The video is credited to EPFL.
Original Research: Abstract for “A multidirectional gravity-assist algorithm that enhances locomotor control in patients with stroke or spinal cord injury” by Jean-Baptiste Mignardot, Camille G. Le Goff, Rubia van den Brand, Marco Capogrosso, Nicolas Fumeaux, Heike Vallery, Selin Anil, Jessica Lanini, Isabelle Fodor, Grégoire Eberle, Auke Ijspeert, Brigitte Schurch, Armin Curt, Stefano Carda, Jocelyne Bloch, Joachim von Zitzewitz, and Grégoire Courtine in Science Translational Medicine. Published online July 18 2017 doi:10.1126/scitranslmed.aah3621
[cbtabs][cbtab title=”MLA”]EPFL “Smart Walk Assist Improves Rehabilitation.” NeuroscienceNews. NeuroscienceNews, 20 July 2017.
<https://neurosciencenews.com/smart-walk-assist-rehab-7134/>.[/cbtab][cbtab title=”APA”]EPFL (2017, July 20). Smart Walk Assist Improves Rehabilitation. NeuroscienceNew. Retrieved July 20, 2017 from https://neurosciencenews.com/smart-walk-assist-rehab-7134/[/cbtab][cbtab title=”Chicago”]EPFL “Smart Walk Assist Improves Rehabilitation.” https://neurosciencenews.com/smart-walk-assist-rehab-7134/ (accessed July 20, 2017).[/cbtab][/cbtabs]
Abstract
A multidirectional gravity-assist algorithm that enhances locomotor control in patients with stroke or spinal cord injury
Gait recovery after neurological disorders requires remastering the interplay between body mechanics and gravitational forces. Despite the importance of gravity-dependent gait interactions and active participation for promoting this learning, these essential components of gait rehabilitation have received comparatively little attention. To address these issues, we developed an adaptive algorithm that personalizes multidirectional forces applied to the trunk based on patient-specific motor deficits. Implementation of this algorithm in a robotic interface reestablished gait dynamics during highly participative locomotion within a large and safe environment. This multidirectional gravity-assist enabled natural walking in nonambulatory individuals with spinal cord injury or stroke and enhanced skilled locomotor control in the less-impaired subjects. A 1-hour training session with multidirectional gravity-assist improved locomotor performance tested without robotic assistance immediately after training, whereas walking the same distance on a treadmill did not ameliorate gait. These results highlight the importance of precise trunk support to deliver gait rehabilitation protocols and establish a practical framework to apply these concepts in clinical routine.
“A multidirectional gravity-assist algorithm that enhances locomotor control in patients with stroke or spinal cord injury” by Jean-Baptiste Mignardot, Camille G. Le Goff, Rubia van den Brand, Marco Capogrosso, Nicolas Fumeaux, Heike Vallery, Selin Anil, Jessica Lanini, Isabelle Fodor, Grégoire Eberle, Auke Ijspeert, Brigitte Schurch, Armin Curt, Stefano Carda, Jocelyne Bloch, Joachim von Zitzewitz, and Grégoire Courtine in Science Translational Medicine. Published online July 18 2017 doi:10.1126/scitranslmed.aah3621
