Publicação
Human activity recognition for an intelligent knee orthosis
| dc.contributor.advisor | Gamboa, Hugo | |
| dc.contributor.advisor | Amma, Christoph | |
| dc.contributor.advisor | Schultz, Tanja | |
| dc.contributor.author | Santos, Diliana Maria Barradas Rebelo dos | |
| dc.date.accessioned | 2013-01-10T14:25:38Z | |
| dc.date.available | 2013-01-10T14:25:38Z | |
| dc.date.issued | 2012 | |
| dc.description | Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica | por |
| dc.description.abstract | Activity recognition with body-worn sensors is a large and growing field of research. In this thesis we evaluate the possibility to recognize human activities based on data from biosignal sensors solely placed on or under an existing passive knee orthosis, which will produce the needed information to integrate sensors into the orthosis in the future. The development of active orthotic knee devices will allow population to ambulate in a more natural, efficient and less painful manner than they might with a traditional orthosis. Thus, the term ’active orthosis’ refers to a device intended to increase the ambulatory ability of a person suffering from a knee pathology by applying forces to correct the position only when necessary and thereby make usable over longer periods of time. The contribution of this work is the evaluation of the ability to recognize activities with these restrictions on sensor placement as well as providing a proof-of-concept for the development of an activity recognition system for an intelligent orthosis. We use accelerometers and a goniometer placed on the orthosis and Electromyography (EMG) sensors placed on the skin under the orthosis to measure motion and muscle activity respectively. We segment signals in motion primitives semi-automatically and apply Hidden-Markov-Models (HMM) to classify the isolated motion primitives. We discriminate between seven activities like for example walking stairs up and ascend a hill. In a user study with six participants, we evaluate the systems performance for each of the different biosignal modalities alone as well as the combinations of them. For the best performing combination, we reach an average person-dependent accuracy of 98% and a person-independent accuracy of 79%. | por |
| dc.identifier.uri | http://hdl.handle.net/10362/8493 | |
| dc.language.iso | eng | por |
| dc.publisher | Faculdade de Ciências e Tecnologia | por |
| dc.subject | Biosignals | por |
| dc.subject | Human activity recognition | por |
| dc.subject | Signal-processing | por |
| dc.subject | Hidden Markov models | por |
| dc.title | Human activity recognition for an intelligent knee orthosis | por |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | por |
| rcaap.type | masterThesis | por |