Publicação
Assistive locomotion strategies for active lower limb devices
| Resumo: | In order to actively aid or restore legged locomotion to individuals suffering from muscular impairments, weakness or neurologic injury, rehabilitation is recommended as a more appropriate way to achieve the ultimate goal of a continuous ambulatory monitoring. Also, the assistance with wearable robots (WRs) during daily living activities provides a more intensive and purposeful targeted therapeutic training, and also reduces the treatment cost and the number of health care personnel. Thus, it is crucial the development of locomotion strategies that recognize in real-time the locomotion mode of human-robot interaction in overground daily living activities. Thus, this thesis intends to develop two locomotion strategies which will be integrated in high level control of exoskeleton H2 (Exo-H2), the WR developed under the scope of BioMot project. The first locomotion strategy proposed and validated addresses online detection of events and gait phases uniquely through information from embedded sensors. This knowledge will allow determining in real-time the biomechanical parameters of assisted walking, and consequently to assess the progress of rehabilitation process by means of WR. The solution validation in different locomotion conditions (assisted walking by WR, walking of humanoid robot and walking of healthy subject) shows up that the proposed solution led to a robust and general tool for gait detection, which is also capable to detect more events and gait phases comparatively to the works presented in literature. Locomotion mode recognition is the second locomotion strategy developed in this thesis, which allows the recognition of different locomotion modes. Based on an exhaustive state of the art survey, a more robust and accurate procedure that leads to a more robust and accurate tool was delineated. According to the results achieved for offline scenario it was verified that the performance of the locomotion strategy increases by using different types of biomechanical parameters, which should be previously selected by means of multivariate statistic methods. Both binary and multiclass classification were addressed through support vector machine (SVM). The implementation of these methods led to a powerful and accurate tool of offline recognition of locomotion modes. Additionally, a strategy for online recognition was proposed. Further work will consist on the application of these locomotion strategies in real-time environment of gait rehabilitation. |
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| Autores principais: | Figueiredo, Joana Sofia Campos |
| Assunto: | Abnormal gait patterns Assistance and rehabilitation Lower limbs exoskeletons Biomechanical parameters Embedded sensors Detection of events and gait phases Locomotion mode recognition Feature selection methods Gait classification methods Padrões anormais da marcha Reabilitação Exosqueletos dos membros inferiores Parâmetros biomecânicos Sensores embebidos Deteção de eventos e fases da marcha Reconhecimento de modos de locomoção Métodos de seleção de features Métodos de classificação Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
| Ano: | 2015 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | In order to actively aid or restore legged locomotion to individuals suffering from muscular impairments, weakness or neurologic injury, rehabilitation is recommended as a more appropriate way to achieve the ultimate goal of a continuous ambulatory monitoring. Also, the assistance with wearable robots (WRs) during daily living activities provides a more intensive and purposeful targeted therapeutic training, and also reduces the treatment cost and the number of health care personnel. Thus, it is crucial the development of locomotion strategies that recognize in real-time the locomotion mode of human-robot interaction in overground daily living activities. Thus, this thesis intends to develop two locomotion strategies which will be integrated in high level control of exoskeleton H2 (Exo-H2), the WR developed under the scope of BioMot project. The first locomotion strategy proposed and validated addresses online detection of events and gait phases uniquely through information from embedded sensors. This knowledge will allow determining in real-time the biomechanical parameters of assisted walking, and consequently to assess the progress of rehabilitation process by means of WR. The solution validation in different locomotion conditions (assisted walking by WR, walking of humanoid robot and walking of healthy subject) shows up that the proposed solution led to a robust and general tool for gait detection, which is also capable to detect more events and gait phases comparatively to the works presented in literature. Locomotion mode recognition is the second locomotion strategy developed in this thesis, which allows the recognition of different locomotion modes. Based on an exhaustive state of the art survey, a more robust and accurate procedure that leads to a more robust and accurate tool was delineated. According to the results achieved for offline scenario it was verified that the performance of the locomotion strategy increases by using different types of biomechanical parameters, which should be previously selected by means of multivariate statistic methods. Both binary and multiclass classification were addressed through support vector machine (SVM). The implementation of these methods led to a powerful and accurate tool of offline recognition of locomotion modes. Additionally, a strategy for online recognition was proposed. Further work will consist on the application of these locomotion strategies in real-time environment of gait rehabilitation. |
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