Open AccessTime-based Simplified Denavit-Heartenberg Translation (TS-DH) for Capturing Finger Kinematic Data
Author(s) -
Mohd Amir Idzham Iberahim,
Syadiah Nor Wan Shamsuddin,
Mokhairi Makhtar,
Mohd Nordin Abdul Rahman,
Nordin Simbak
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.28.20958
Subject(s) - kinematics , computer science , translation (biology) , virtual reality , process (computing) , consistency (knowledge bases) , rehabilitation , artificial intelligence , simulation , computer vision , human–computer interaction , medicine , biochemistry , chemistry , physics , classical mechanics , messenger rna , gene , operating system , physical therapy
Virtual finger model is commonly used in many applications for stroke fine motor rehabilitation especially in Virtual Reality (VR) applications. Capturing movement data for fingers is one of the important phases in any virtual fine motor rehabilitation process. Manual observation provides inconsistent evaluation given by different therapists for different rehabilitation sessions. Although the process of capturing data is performed, without time series of captured data, the data will not have a significant impact in producing consistent and meaningful evaluation. Furthermore, the consistency of the assessment of rehabilitation sessions will deliver more robust rehabilitation progress analysis. This data is very useful when paired with time information which can be analyzed to produce optimal evaluation. This paper proposes Time-based Simplified Denavit-Heartenberg Translation (TS-DH) consisting of forward kinematic with simplified DH parameter for capturing coordinate of end of each bone from virtual finger model paired with timeframe data. The DH model is enhanced by implementing 2 additional rules in assigning joint parameter. The data will be recorded with timeframe of every finger movement. As a conclusion, TS-DH model can be used in any virtual finger environment accurately.