Leg Flexibility Classification Using AutoML Tables
Silat performance covers many style of self-defence that requires hands and legs to perform punching and kicking. The aim of this project was to classify the leg flexibility by studying the correlation between flexibility index and kicking angle by means of classification using machine learning method. The main objectives were to develop an IoT based prototype utilizing flex sensor and Blynk platform, to measure the kicking angle and leg flexibility index on subjects and finally to conduct classification study on the measured data by using AutoML Table provided by Google Cloud. Twenty participant from two different backgrounds; silat athlete and non-silat athletes are selected as subjects in this study. In this project, the AutoML Tables was automatically built and deployed machine learning models based on the structured data. The .CSV file contained data of kicking angle and leg flexibility index are used to train the classification model. The prediction model successfully predicts the outcome (leg flexibility) when the two input features (flexibility index and kicking angle) are keyed-in during “Test and Use”. In conclusion, the leg flexibility classification can be determined based on two parameters namely as flexibility index and kicking angle by using AutoML Tables. In the future, bigger sample size of data can be collected and trained using BigQuery.