Evaluation of a new virtual reality micro-robotic cell injection training system☆
Graphical abstract
Introduction
Cell injection is a procedure in which a small amount of material, such as protein, DNA, sperm, or bio-molecules, is injected into a biological cell. Since the introduction of enabling technology early last century, cell injection technology has been widely applied to many areas. For example, in intracytoplasmic sperm injection, cell injection technology is used for injecting an immobilized sperm into the center of a mature egg to stimulate fertilization. Another widespread cell injection application is in drug development where researchers inject drugs into a cell and observe the effects.
Micro-robotic cell injection is typically performed by an expert human bio-operator with extensive training and experience. The procedure involves delicate operations, such as positioning the micropipette accurately to penetrate the cell membrane and inject the foreign material appropriately. Successful cell injection procedure requires a high skill level.
Despite utilizing micro-robots capable of high precision movement, successful injection is often inaccurately reproducible, thus contributing to high failure rates even among experienced bio-operators [1]. The micro-robot movement is controlled manually through which a bio-operator uses input controllers, such as rotary encoders or a joystick for each of the x, y, and z axes. This human-in-the-loop approach remains common practice because human-level judgment and intuition, adaptability, and flexibility are crucial during cell injection [2]. However, it presents several major drawbacks in terms of speed, precision, throughput, and reproducibility [3], [4]. Suitable human–machine interfacing should also be considered to optimize this approach [5].
Several requirements must be considered in biological manipulation such as cell injection. The cell as the manipulated object and micropipettes as tools are extremely small, and the associated contact force is within the mN to µN range [6]. Therefore, different skills, such as precise positioning, puncturing, and penetrating, are crucial. Injection accuracy, trajectory, speed, and force are also significant in successful injection [1], [7].
VR is an area that has received much interest among researchers for its capability to provide effective learning and practice environment. Most VR training systems present advantages over real-life training in terms of cost, portability, and flexibility. Haptic technology has also played a significant assisting role in motor skill training since its introduction two decades ago. Significant growth is currently observed in the haptically enabled VR systems development, which is designed to efficiently train humans for various physical tasks. Haptically enabled VR systems have been employed in many skill training applications, such as gunnery, sports, surgery, and art. Utilizing haptics and VR offer significant benefits in skill training. This study introduces a haptically enabled VR environment developed specifically for micro-robotic cell injection skill training.
The proposed haptically enabled VR micro-robotic cell injection training system provides bio-operators with immersive virtual environment. Aside from the interactive virtual environment of a micro-robotic cell injection setup, the system provides haptic feedback to bio-operators as guidance and for increased immersion sense. A detailed discussion of the system is presented in Section 2. An experimental evaluation was designed and conducted to validate the usability and effectiveness of the system using a group of participants. The design of the experimental evaluation is discussed in Section 3. The analysis and discussion of the experimental evaluation are then presented in Sections 4 and 5, respectively. Finally, the conclusions and future works are drawn and suggested in Section 6.
Section snippets
VR micro-robotic cell injection system
The VR micro-robotic cell injection training system extends our previous work, which discusses keyboard control for cell injection [8], to also consider haptic input control and feedback from virtual fixtures (VFs) [9].
The virtual micro-robotic cell injection environment also utilizes the reconfigurable haptic interface (Fig. 1) from our prior work [10]. The haptic interface can be employed with up to two Phantom Omni (now known as Geomagic Touch) haptic devices for various application types.
Design of user training evaluation
Experimental evaluation was designed to consider the system usability and effectiveness in training users to improve their performance against a set of defined metrics. First, the evaluation considers the performance improvement of the participants utilizing the keyboard control mode. Aside from the ubiquitous use of the keyboard, where the utilization of transferable skills is possible, it provides a simple and low-cost approach of controlling the micro-robot. Thus, the keyboard control mode
User training and performance analysis of input control methods
This section presents the results and analysis from the implemented evaluation with human participants. The main objectives of the analysis are to study the training effects by adopting each input control mode and usability of the input control modes by examining the participants’ performance.
Discussion
Training using different input modes had a significant effect on all measures of performance. The haptic device group showed performance improvement on accuracy and success rate. The results can be related to the improvement of performance after undergoing each of the training sessions. By contrast, the progress of the keyboard group showed no particular trend of improvement or deterioration of performance after each training session. Another important parameter evaluated in this study is the
Conclusions and future work
The findings indicate that the participants achieved a significantly high success rate using the input control methods. Utilizing haptic device, which provides an intuitive control method, leads to improved performance of the participants in terms of accuracy. The participants achieved consistent performance after undergoing training using the haptic device control method.
Haptic guidance provided by the intuitive control method can also serve as an efficient training tool for bio-operators. The
Syafizwan Faroque is a Senior Assistant Director within the Ministry of Higher Education Malaysia. He received his B.Sc. in Electrical Engineering from University of Technology Malaysia, M.Ed. in Technical and Vocational Education from University Tun Hussein Onn Malaysia, and Ph.D. from Deakin University, Australia. His research is concerned with the utilization of haptic technology in providing skill training.
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2019, Medical Engineering and PhysicsCitation Excerpt :The tasks related to needle insertion training can involve hand-eye coordination because many techniques use medical imaging to help guide the needle insertion. In several situations, this task focused on certain skills depending on the desired training, such as: image interpretation (makes use of previously captured images instead of real-time to find the appropriate needle insertion) [44,48–53]; and needle navigation in two ways - the first one with radiographic guidance, e.g., real-time images acquired by other instruments [27,34,37,40,45,47,53–75], and the second one with palpation to guide during the insertion [10,26,32,33,76–85]. Conversely, certain tissue simulations were not found in the studies included, for example, tissues bearing pathologies.
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Syafizwan Faroque is a Senior Assistant Director within the Ministry of Higher Education Malaysia. He received his B.Sc. in Electrical Engineering from University of Technology Malaysia, M.Ed. in Technical and Vocational Education from University Tun Hussein Onn Malaysia, and Ph.D. from Deakin University, Australia. His research is concerned with the utilization of haptic technology in providing skill training.
Michael Mortimer received his Electronics Engineering Degree from Deakin University, Australia in 2013. Michael is currently undertaking a PhD in the field of Virtual Reality and Robotics at CADET VR Lab, Deakin University. Michael's research looks at providing teleoperators with a dynamic virtual reality user interface for teleoperation of heterogeneous robotic teams.
Mulyoto Pangestu is a Lecturer in Clinical Embryology and Laboratory Manager at Department Obstetrics and Gynaecology, Monash University. He obtained his Bachelor in Animal Science from Jenderal Soedirman University Indonesia, and continued his Master and PhD at Monash University, Australia. His research interests are embryo manipulation and cryopreservation in human and animal.
Mehdi Seyedmahmoudian received his B.Sc in Electrical Power Engineering in 2009, M.Eng in Industrial Electronics and Control in 2012 and recently received his PhD from School of Engineering Deakin University Australia. He is now lecturer at Deakin University, Australia and his research interests includes Renewable Energy Systems, Control and Intelligent Systems and applications of AI methods in Control and Optimization.
Ben Horan is a Senior Lecturer, Director of the CADET Virtual Reality (VR) Lab and the Head of the Bachelor of Mechatronics Engineering within the School of Engineering, Deakin University, Australia. His current research interests include haptic human robotic interaction, virtual reality, haptic device design and mobile robotics.
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Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. A. Chaudhary.