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All Journal Jurnal Sistem Cerdas Indonesian Journal of Electrical Engineering and Computer Science
Wan Khairunizam
University Malaysia Perlis
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Analysis of attribute domain for geometrical gesture performed by arm movements Wan Khairunizam; Khairul Ikram; Hafiz Halim; Azri Aziz; I. Zunaidi; S.A Bakar; W. Azani W. Mustafa
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp759-766

Abstract

Hand gesture recognition commonly uses a camera to track hand movements and transformed into gesture database by using various computational approaches. Motion tracking utilized to map coordinate point of the subject movement, either in skeletal model or marker tracing. Data from motion trackers usually contains massive coordinate sequences of marker movement. A reliable method is required to select best features and analyze these data. However, the current issue whether the selected features and data presentation are significant for the research or not. This research brings the concept of ontology design for arm gesture recognition systems by utilizing the motion capture system. Ontology is the conceptual structure mainly used to retrieve information by establishing relation in complex data model. The proposed ontology framework is divided into three domains which are knowledge domain, attribute domain and process domain. Knowledge domain holds pre-processed gestural data from motion capture. The attribute domain is that the level where all the attribute elements were presented. This paper shows the analysis of the datasets in attribute domain. The analysis is divided into two parts which is precision measure and ANOVA test. Both analyses are to prove the reliability of datasets in attribute domain. The precision measure is used to remove all the common data for all gesture. A statistical analysis of p-value is lower than 0.01 which means the gestural data are statistically significant to be used for the similarity measure.
Analisa Upper-limb Movement Sequence (UMS) menggunakan Absolute Trajectory Error dan Hand Speed Movement untuk Rehabilitasi Pasca Stroke Basri Noor Cahyadi; Milkhatussyafa’ah Taufiq; Wan Khairunizam
Jurnal Sistem Cerdas Vol. 5 No. 3 (2022)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v5i3.251

Abstract

2019 World Health Organization informed that the two-thirds of stroke patients have a permanent dissability. Disability disorders can affect performance or daily activities such as eating, drinking, wearing clothes, bathing, and others. Disability caused by stroke can be treated by exercising motor function and muscle strength. Studies in upper extremity rehabilitation report that the most important element in the disability recovery process is monitoring the progress of the rehabilitation itself. To monitor and evaluate upper arm rehabilitation, most therapists still rely on clinical assessments based on ordinal scales or charts and are only monitored in terms of the patient's upper arm movement. In this study, we will analyze the rehabilitation movement based on virtual reality games using the Absolute Trajectory Error and Hand Speed ​​Movement methods. While the rehabilitation movement used in this study will apply the Upper-limb Movement Sequence (UMS) method. 5 subjects contributed to data collection over three sessions and five iterations. Their movements were recorded using the Kinect Xbox V2 sensor with 10 Hz sampling data. Mean absolute trajectory error (ATE) and hand speed method were used to analyze arm movements during VR games. Although this study used healthy subjects, 80% of them experienced an increase in movement, and this condition was evidenced by a decrease in ATE values ​​in each session. Trajectory data can be used as the basis for analysis of arm movements during the VR game rehabilitation process, where with these data errors in hand position, hand speed to reach the target, and movement errors can be analyzed more deeply. Moreover, the mean ATE and hand speed movements can show the progress or changes in hand movement during the rehabilitation process clearly.
Co-Authors Azri Aziz Basri Noor Cahyadi Hafiz Halim I. Zunaidi Khairul Ikram Milkhatussyafa’ah Taufiq S.A Bakar W. Azani W. Mustafa