Adaptive multichannel FES neuroprosthesis with learning control and automatic gait assessment

dc.contributor.authorMüller, Philipp
dc.contributor.authorAma, Antonio J. del
dc.contributor.authorMoreno, Juan C.
dc.contributor.authorSchauer, Thomas
dc.date.accessioned2020-11-30T09:14:43Z
dc.date.available2020-11-30T09:14:43Z
dc.date.issued2020-02-28
dc.description.abstractBackground FES (Functional Electrical Stimulation) neuroprostheses have long been a permanent feature in the rehabilitation and gait support of people who had a stroke or have a Spinal Cord Injury (SCI). Over time the well-known foot switch triggered drop foot neuroprosthesis, was extended to a multichannel full-leg support neuroprosthesis enabling improved support and rehabilitation. However, these neuroprostheses had to be manually tuned and could not adapt to the persons’ individual needs. In recent research, a learning controller was added to the drop foot neuroprosthesis, so that the full stimulation pattern during the swing phase could be adapted by measuring the joint angles of previous steps. Methods The aim of this research is to begin developing a learning full-leg supporting neuroprosthesis, which controls the antagonistic muscle pairs for knee flexion and extension, as well as for ankle joint dorsi- and plantarflexion during all gait phases. A method was established that allows a continuous assessment of knee and foot joint angles with every step. This method can warp the physiological joint angles of healthy subjects to match the individual pathological gait of the subject and thus allows a direct comparison of the two. A new kind of Iterative Learning Controller (ILC) is proposed which works independent of the step duration of the individual and uses physiological joint angle reference bands. Results In a first test with four people with an incomplete SCI, the results showed that the proposed neuroprosthesis was able to generate individually fitted stimulation patterns for three of the participants. The other participant was more severely affected and had to be excluded due to the resulting false triggering of the gait phase detection. For two of the three remaining participants, a slight improvement in the average foot angles could be observed, for one participant slight improvements in the averaged knee angles. These improvements where in the range of 4circat the times of peak dorsiflexion, peak plantarflexion, or peak knee flexion. Conclusions Direct adaptation to the current gait of the participants could be achieved with the proposed method. The preliminary first test with people with a SCI showed that the neuroprosthesis can generate individual stimulation patterns. The sensitivity to the knee angle reset, timing problems in participants with significant gait fluctuations, and the automatic ILC gain tuning are remaining issues that need be addressed. Subsequently, future studies should compare the improved, long-term rehabilitation effects of the here presented neuroprosthesis, with conventional multichannel FES neuroprostheses.en
dc.description.sponsorshipTU Berlin, Open-Access-Mittel – 2020en
dc.identifier.eissn1743-0003
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/12080
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-10954
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc600 Technik, Technologiede
dc.subject.othermultichannel neuroprosthesisen
dc.subject.otherfunctional electrical stimulationen
dc.subject.otheriterative learning controlen
dc.subject.otherautomatic gait assessmenten
dc.subject.otherreal-time motion analysisen
dc.subject.otherjoint angle trackingen
dc.subject.othergait supporten
dc.titleAdaptive multichannel FES neuroprosthesis with learning control and automatic gait assessmenten
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber36en
dcterms.bibliographicCitation.doi10.1186/s12984-020-0640-7en
dcterms.bibliographicCitation.journaltitleJournal of NeuroEngineering and Rehabilitationen
dcterms.bibliographicCitation.originalpublishernameBioMed Centralen
dcterms.bibliographicCitation.originalpublisherplaceLondonen
dcterms.bibliographicCitation.volume17en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Energie- und Automatisierungstechnik::FG Regelungssystemede
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Regelungssystemede
tub.affiliation.instituteInst. Energie- und Automatisierungstechnikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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