Publication: Classification analysis for cyclists’ posture recognition
| datacite.subject.fos | oecd::Engineering and technology::Mechanical engineering::Mechanical engineering | |
| dc.contributor.author | Chua, Ching Cheng | |
| dc.date.accessioned | 2026-08-10T02:20:27Z | |
| dc.date.available | 2026-08-10T02:20:27Z | |
| dc.date.issued | 2016-06 | |
| dc.description.abstract | In cycling sports, there are three basic postures: standing, normal and lean forward to be analyzed for the most efficient posture. Previous works in cycling motion detailed into the physics concept for good cycling performances. However, no studies had realized the body joints and angles postulations worth some consideration. Another constraint is on the complicated steps and processes required for analyzing the cycling motion. Therefore, the purposes of this study are to (i) analyzed three different cycling motion postures: Normal, Lean Forward and Standing for classification analysis, (ii) study skeletal 2D postures that determine optimum posture for classification, (iii) relate selective attributes to enhance the cycling posture classification accuracies. The study begins with experimental cycling motion capture data, followed by data conversion into images and numeric data. Data mining analysis consisting of four stages is employed. It is found that the lower body segments (below waists) are important for classifying data into its corresponding classes. Best classification accuracy obtained show up to 99.67 % accurately classified data using the Tree Classifier on 10 fold cross validation mode. A further study showed that three body joint coordinates: kx, ky and llAngle are the critical attributes, while the knee being the important attribute to distinguish the different cycling postures perfectly. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/24818 | |
| dc.language.iso | en | |
| dc.title | Classification analysis for cyclists’ posture recognition | |
| dc.type | Resource Types::text::report::technical report | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |