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Human activity recognition for an intelligent knee orthosis
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Orientador(es)
Resumo(s)
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%.
Descrição
Dissertação para obtenção do Grau de Mestre em
Engenharia Biomédica
Palavras-chave
Biosignals Human activity recognition Signal-processing Hidden Markov models