Título: | AN APPROACH BASED ON INTERACTIVE MACHINE LEARNING AND NATURAL INTERACTION TO SUPPORT PHYSICAL REHABILITATION | ||||||||||||
Autor: |
JESSICA MARGARITA PALOMARES PECHO |
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Colaborador(es): |
ALBERTO BARBOSA RAPOSO - Orientador |
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Catalogação: | 10/AGO/2021 | Língua(s): | PORTUGUESE - BRAZIL |
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Tipo: | TEXT | Subtipo: | THESIS | ||||||||||
Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=54139&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=54139&idi=2 |
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DOI: | https://doi.org/10.17771/PUCRio.acad.54139 | ||||||||||||
Resumo: | |||||||||||||
Physiotherapy aims to improve the physical functionality of people, seeking
to mitigate the disabilities caused by any injury, disorder or disease. In
this context, several computational technologies have been developed in order
to support the rehabilitation process, such as the end-user adaptable technologies.
These technologies allow the physiotherapist to adapt applications and
create activities with personalized characteristics according to the preferences
and needs of each patient. This thesis proposes a low-cost approach based on
interactive machine learning (iML) that aims to help physiotherapists to create
personalized activities for their patients easily and without the need for
software coding, from just a few examples in RGB video (captured by a digital
video camera). To this end, we take advantage of pose estimation based on deep
learning to track, in real time, the key joints of the human body from image
data. This data is processed as time series using the Dynamic Time Warping
algorithm in conjunction with the K-Nearest Neighbors algorithm to create a
machine learning model. Additionally, we use an anomaly detection algorithm
in order to automatically assess movements. The architecture of our approach
has two modules: one for the physiotherapist to present personalized examples
from which the system creates a model to recognize these movements; another
to the patient performs personalized movements while the system evaluates
the patient. We assessed the usability of our system with physiotherapists
from five rehabilitation clinics. In addition, experts have clinically evaluated
our machine learning model. The results indicate that our approach contributes
to automatically assessing patients movements without direct monitoring by
the physiotherapist, in addition to reducing the specialist s time required to
train an adaptable system.
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