dc.contributor.author |
Manathunga, Ramitha |
|
dc.date.accessioned |
2025-06-16T03:59:55Z |
|
dc.date.available |
2025-06-16T03:59:55Z |
|
dc.date.issued |
2024 |
|
dc.identifier.citation |
Manathunga, Ramitha (2024) Movementor - Your Personal Workout Guide. BSc. Dissertation, Informatics Institute of Technology |
en_US |
dc.identifier.issn |
20200910 |
|
dc.identifier.uri |
http://dlib.iit.ac.lk/xmlui/handle/123456789/2552 |
|
dc.description.abstract |
"This research covers the underrated issue of incorrect workout techniques and the absence
of corrective feedback during workout sessions, which often result in injuries and strain. This
research introduces a hybrid model known as the Long-term Recurrent Convolutional Network
(LRCN), which takes advantage of both Convolutional Neural Networks (CNN) and Long ShortTerm Memory (LSTM) to enhance pose detection. This approach aims to detect workouts and
provide real-time feedback on intense workouts ensuring safe and effective exercise execution." |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
LRCN |
en_US |
dc.subject |
Motion Recognition |
en_US |
dc.subject |
CNN |
en_US |
dc.title |
Movementor - Your Personal Workout Guide |
en_US |
dc.type |
Thesis |
en_US |