Systems, Man & Cybernetics - January 2016 - 24

◆ After sterilization, a needle will be inserted into the

chest cavity to release excessive air. Although the
insertion procedure has been well studied in many
existing research works, the collaborative operations
between two hands to minimize skin deformation
while inserting the needle is rarely implemented
and studied.
As discussed, none of these tasks can be systematically implemented at the current stage of MPHs. However, with the framework and its underlying abstract MPH
model, different interaction models can be applied to different tasks and work together to form a complete training system.
Easy to Use
One major goal we intend to achieve from this framework
is to make the development of MPH applications easier. As
such, the following features have been identified.
1) The framework is designed and implemented based on
object-oriented programming and follows the rules
of inheritance, polymor phism, and en capsulation.
These have been widely adopted in many programming frameworks and SDKs. Programmers will be able
to comprehend the framework logic rather easily.
2) With reference to the abstract MPH model, the component hierarchy is easily understandable as it resembles
how haptic sensations actually work on human hands.
Designers of new MPHs and MPH applications can
clearly define their project scope from the beginning
and identify the essential components and communication approaches they need. This will also help synchronize the development between the hardware and
the applications.
3) The device-specific function call conversion routines will only need to be written once based on standard dynamic linking libraries (DLLs). Once finished,
the specific device will be recognized by the framework
as a new MPH, with all essential function call templates
implemented. A haptic application programmer will
be able to write examples for new MPHs similar to what
he or she used to work with other SDKs, such as
CHAI3D. Existing demos will also be easily adaptable
into the proposed framework and be compatible with
new MPHs.
Conclusion
MPH is a natural replication and extension of human
hands and is supposed to be one of the most intuitive,
effective, and immersive approaches for human-computer interaction. Due to the limitations on hardware, computation power, and cost, the development of MPHs has
been evolving slowly in the past decades, and many
existing solutions act in their own ways. In this article,
we proposed a universal framework with its underlying
interaction model to support not only existing but
also future MPHs. We aim to make the design and
24

IEEE SyStEmS, man, & CybErnEtICS magazInE Janu ar y 2016

implementation of various MPH configurations as well
as have them uniformly communicate and collaborate at
different interaction levels and with different features.
Application examples can also be easily developed and
deployed on different MPHs. With these features in
mind, the proposed framework may lead to a prosperity
of MPHs and truly realize the intuition of complex
human-computer interaction.
About the Authors
Lei Wei (lei.wei@deakin.edu.au) earned his B.Eng.
(honors) degree in computer science from Tianjin University and his Ph.D. degree from Nanyang Technological University in 2006 and 2011, respectively. He is
currently a senior research fellow with the Institute for
Intelligent Systems Research and Innovation in Australia. His research interests include haptic rendering,
haptic collision detection, networked haptics, medical
haptics, human-computer interaction, and image processing. He is a Member of the IEEE.
Hailing Zhou ( h a i l i n g.z hou@de a k i n .edu.au)
earned her B.Eng. (honors) degree in computer science from Xidian University, China, in 2006 and her
Ph.D. degree from Nanyang Technological University
(NTU), Singapore, in 2012. In 2012, she was a project
officer working on object-based image editing at NTU.
She is currently a research fellow at the Institute for
Intelligent Systems Research and Innovation in Australia. Her main areas of research are traffic modeling and simulation, pattern recognition, and image
segmentation.
Saeid Nahavandi (saeid.nahavandi@deakin.edu.au)
earned his Ph.D. degree from Durham University, United
Kingdom. He is an Alfred Deakin Professor, chair of
engineering, and the director of the Institute for Intelligent Systems Research and Innovation, Australia. He has
published more than 450 papers in various international
journals and conferences. His research interests include
modeling of complex systems, robotics, and haptics. He
is the coeditor-in-chief of IEEE Systems Journal and an
editor (South Pacific region) of International Journal of
Intelligent Automation and Soft Computing. He is a
Senior Member of the IEEE as well as a fellow of Engineers Australia and the Institution of Engineering and
Technology.
Dangxiao Wang (hapticwang@buaa.edu.cn) earned
his Ph.D. degree from Beihang University, Beijing,
China, in 2004. He is currently an associate professor at
the State Key Laboratory of Virtual Reality Technology
and Systems at Beihang University. From 2004 to 2006,
he was a post doc at Beihang University. From 2006 to
2007, he was an assistant professor in the School of
Mechanical Engineering and Automation, Beihang University. His research interests include haptic rendering,
neurohaptics, and medical robotic systems. He is a
Senior Member of the IEEE. He has been the chair of



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Systems, Man & Cybernetics - January 2016 - Cover3
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