DC Field | Value | Language |
dc.contributor.author | FERRAR, HAdjar ARoua | - |
dc.date.accessioned | 2024-10-06T08:07:27Z | - |
dc.date.available | 2024-10-06T08:07:27Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/731 | - |
dc.description | Encadreur :Dr. Maxime DEVANNE / Dr. Belkacem KHALDI Co-Encadreur : Dr. Ali Ismail FAWAZ | en_US |
dc.description.abstract | Exercise-based rehabilitation programs play a vital role in helping patients recover from
injuries or surgeries, significantly enhancing their quality of life while reducing mortality
rates and the likelihood of re-hospitalization. However, traditional rehabilitation methods
are often resource-intensive, requiring continuous supervision by therapists. This
creates a substantial barrier for patients who need to attend rehabilitation sessions
multiple times a week. One way to resolve this is by providing technological support
for home-based rehabilitation.
Recent advancements in deep learning offer promising new avenues for automating
and improving these rehabilitation practices by generating human motion patterns.
Previous works in the domain of motion generation have rarely explored the generation
of rehabilitation movements.
In this project, we propose a deep learning-based framework for generating rehabilitation
human motion. Given a prescribed action type and a quality score, we
aim to generate plausible 3D rehabilitation human motion sequences. We propose a
conditional Variational Auto-Encoder (VAE) that encourages a diverse sampling of the
motion space according to a specified performance score. | en_US |
dc.language.iso | en | en_US |
dc.subject | Rehabilitation Human Motion | en_US |
dc.subject | Variational Auto-Encoder | en_US |
dc.subject | Deep Learning | en_US |
dc.title | Deep Learning architectures for generating rehabilitation human motion | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Ingenieur
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