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Hoky Ajicahyadi
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Aplikasi Penilaian Posisi Karate Menggunakan Sensor Kinect Ajicahyadi, Hoky; Jusak, Jusak; Sukmaaji, Anjik
Jurnal JSIKA Vol 3, No 1 (2014)
Publisher : Jurnal JSIKA

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Abstract

Kihon is the fundamental knowledge which is important to learn Karate. Kihon consists of stances, punches, and kicks. In traditional learning environment, Karate teachers have difficulty in monitoring student position in learning Karate. This paper present an approach to overcome this problem by using Kinect sensor data to capture student position then compare the elevation degree of selected joint. As a result, we developed an application that could measuring body position compared to stored model position. By using skeleton stream which has vector data, we can estimate the angle between two vector and save it as reference for further assessment with user model. This application has accuracy rate of 83% for performing above mentioned task.
Aplikasi Penilaian Posisi Karate Menggunakan Sensor Kinect Ajicahyadi, Hoky; Jusak, Jusak; Sukmaaji, Anjik
Jurnal Sistem Informasi dan Komputerisasi Akuntansi (JSIKA) Vol 3, No 1 (2014)
Publisher : Jurnal Sistem Informasi dan Komputerisasi Akuntansi (JSIKA)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kihon is the fundamental knowledge which is important to learn Karate. Kihon consists of stances, punches, and kicks. In traditional learning environment, Karate teachers have difficulty in monitoring student position in learning Karate. This paper present an approach to overcome this problem by using Kinect sensor data to capture student position then compare the elevation degree of selected joint. As a result, we developed an application that could measuring body position compared to stored model position. By using skeleton stream which has vector data, we can estimate the angle between two vector and save it as reference for further assessment with user model. This application has accuracy rate of 83% for performing above mentioned task.
Aplikasi Penilaian Posisi Karate Menggunakan Sensor Kinect Ajicahyadi, Hoky; Jusak, Jusak; Sukmaaji, Anjik
Jurnal Sistem Informasi dan Komputerisasi Akuntansi (JSIKA) Vol 3, No 1 (2014)
Publisher : Jurnal Sistem Informasi Universitas Dinamika (JSIKA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (82.778 KB)

Abstract

Kihon is the fundamental knowledge which is important to learn Karate. Kihon consists of stances, punches, and kicks. In traditional learning environment, Karate teachers have difficulty in monitoring student position in learning Karate. This paper present an approach to overcome this problem by using Kinect sensor data to capture student position then compare the elevation degree of selected joint. As a result, we developed an application that could measuring body position compared to stored model position. By using skeleton stream which has vector data, we can estimate the angle between two vector and save it as reference for further assessment with user model. This application has accuracy rate of 83% for performing above mentioned task.
Aplikasi Penilaian Posisi Karate Menggunakan Sensor Kinect Hoky Ajicahyadi; Jusak Jusak; Anjik Sukmaaji
Jurnal Sistem Informasi dan Komputerisasi Akuntansi (JSIKA) Vol 3, No 1 (2014)
Publisher : Jurnal Sistem Informasi Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kihon is the fundamental knowledge which is important to learn Karate. Kihon consists of stances, punches, and kicks. In traditional learning environment, Karate teachers have difficulty in monitoring student position in learning Karate. This paper present an approach to overcome this problem by using Kinect sensor data to capture student position then compare the elevation degree of selected joint. As a result, we developed an application that could measuring body position compared to stored model position. By using skeleton stream which has vector data, we can estimate the angle between two vector and save it as reference for further assessment with user model. This application has accuracy rate of 83% for performing above mentioned task.
Co-Authors Anjik Sukmaaji Jusak Jusak Jusak Jusak