Data Glove Research Articles

Real-Time Semi-Physical Simulation of Industry Design System Based on Virtual Reality Technology

Research how to realize real-time hardware in the loop (HIL) of industry design system by utilizing a variety of virtual reality (VR) hardware and software. In this paper, it introduces VR and virtual prototyping technology, and then proposes joint control and simulation based on ADAMS / Aircraft, Simulink, Flightgear three software. Moreover, the virtual scene, data gloves and real-time semi-physical simulation are analyzed and researched; finally as an example, build a real-time semi-physical simulation platform of the landing gear system for multi-wheel and multi-strut aircraft. The system can do kinematic and dynamic analysis, real-time control by visual prototyping and digital models through real-time data acquisition and co-simulation.

The Research and Application of Data Glove in Virtual Interaction System

The virtual reality technology is used in the mine training simulation areas, which can overcome the limitations of traditional training methods effectively. It can also improve the quality of mine training simulation greatly, reduce training costs, and the users can achieve safe and convenient training. In this paper, a dynamic collision detection algorithm is proposed based on the prediction of the movement direction of a virtual hand, and it is the effective collision detection algorithm using data glove in the mine training simulation system. Compared with the traditional static collision detection algorithm in the performance, the dynamic algorithm is proposed to solve the collision detection between one simulation solid model and another one, and to achieve the function that a virtual hand grip and rotate virtual objects in the virtual environment. The dynamic collision detection algorithm can be extended to other simulation systems.

The Creation of Virtual Assembly System Based on Virtools

Aiming to improve the assembly efficiency of machinery products, a Virtools-based virtual assembly system is presented. Firstly, the framework of the Virtools-based virtual assembly system is proposed. Then, how the virtual scene built is introduced. Based on VE (virtual environment), combined with the data glove, the dynamic and interactive assembly is designed via the Virtools platform. Finally, the key techniques of supporting virtual assembly system such as collision detection are also studied by using the Virtools.

Learning redundant motor tasks with and without overlapping dimensions: facilitation and interference effects.

Prior learning of a motor skill creates motor memories that can facilitate or interfere with learning of new, but related, motor skills. One hypothesis of motor learning posits that for a sensorimotor task with redundant degrees of freedom, the nervous system learns the geometric structure of the task and improves performance by selectively operating within that task space. We tested this hypothesis by examining if transfer of learning between two tasks depends on shared dimensionality between their respective task spaces. Human participants wore a data glove and learned to manipulate a computer cursor by moving their fingers. Separate groups of participants learned two tasks: a prior task that was unique to each group and a criterion task that was common to all groups. We manipulated the mapping between finger motions and cursor positions in the prior task to define task spaces that either shared or did not share the task space dimensions (x-y axes) of the criterion task. We found that if the prior task shared task dimensions with the criterion task, there was an initial facilitation in criterion task performance. However, if the prior task did not share task dimensions with the criterion task, there was prolonged interference in learning the criterion task due to participants finding inefficient task solutions. These results show that the nervous system learns the task space through practice, and that the degree of shared task space dimensionality influences the extent to which prior experience transfers to subsequent learning of related motor skills.

Anthropomorphic Optimization of a Rope-Driven Prosthetic Finger

In consideration of the importance of the prosthetic finger having the motion law of human finger. We first establish the relationship between spring stiffness and joint angles of the prosthetic finger through the analysis of the prosthetic finger kinematic. Then collect the joint angles of human index finger during natural bending by using the CyberGloveII-22 data glove. Based on this, an anthropomorphic optimization method is proposed in order to let the prosthetic dinger have the motion law of human index finger. The method in this paper can also be used to plan the trajectory of the finger.

SISTEMA DE BAJO COSTO PARA LA PROGRAMACIÓN POR DEMOSTRACIÓN DEL AGARRE DE OBJETOS EN ROBÓTICA

En este trabajo se presenta la implementación y resultados de pruebas preliminares de un sistema de bajo costo, que permite la captura de las señales implicadas en el agarre de objetos en robótica. El sistema está compuesto de un guante de datos y una mano robótica de tres dedos. El guante de datos es de fabricación propia y contiene los sensores de posición y fuerza. Las señales obtenidas por estos sensores, son almacenadas para su posterior reproducción. La mano está desarrollada en prototipado rápido. El sistema desarrollado es de bajo costo comparado con los comerciales. Además puede ser fácilmente reproducible, ya que se usan elementos comerciales. A través de algunos resultados, se muestra el funcionamiento del sistema desarrollado.

Technology of Operations on Devices in a Virtual Aircraft Cabin

This paper focuses on the manual operations on the virtual cabin devices of a flight simulator developed by virtual reality technology. The system consists of the space positioning system, the data glove, the three-dimensional head-mounted display, the left and right eye stereoscopic imaging computers, the space positioning computer, and the main simulation computer. The hand position, attitude, and shape data are measured through the space positioning system and the data glove while the aircraft position and attitude are calculated through the aircraft simulation system. The hand model is represented by the hand position, attitude, rotation angles of fingers, the key points in the hand and the movement directions of these points .The virtual devices are modeled with object-oriented method encapsulating device shapes, operation status, the hand key points and their movement directions that are affective on the device shapes and operation status. The aircraft attitude is overlaid with the hand attitude in order to regard the moving cabin as static. Thus the coordinate transformations are confined to only the hand model and the device models that operation status changed to avoid the large amount of coordinate transformations and reduce the system time consumption. The application result shows that the virtual operation algorithm has a high efficiency, and the operation precision meets the need of system performance.

English

Moving beyond mouse and keyboard, the evolution of human-computer interaction (HCI) has been an interesting research field in recent years which changes from text-based like using a keyboard to graphic user interface (GUI) based on a mouse, from cumbersome data gloves and tracking devices to visual-based computer application. One of the interesting fields is by using hand gestures to interact with computer. Compared to many other methods, hand gestures have the advantages of being easy to use, natural and intuitive. Successful applications of hand gesture recognition include computer games control, human-robot interaction, and sign language recognition etc. In this paper, a real time vision based hand gesture interaction method is proposed .The basis of our approach is a fast segmentation process with CIELab colour space which separate hand from the image which is given as input by camera and then apply morphological operation for the removal of noise .

Application of a motion capture data glove for hand and wrist ergonomic analysis during laparoscopy

Objectives: This study aims to analyze the surgeons’ hand spatial configuration during the use of two different instrument handles for laparoscopy, by obtaining information from the data glove CyberGlove®, and establishing existing risk levels for wrist disorders. Material and methods: Fifty surgeons participated in this study and were distributed into three groups (novices, intermediate and experts). Each subject carried out suturing and dissection tasks on a physical simulator, using axial-handled or ring-handled instruments, respectively. Hand and wrist positions were registered by the CyberGlove® and a modified RULA method was applied to establish appropriate risk levels for wrist disorders. Results: We found statistically significant differences in seven of the eleven glove sensors when comparing both tasks. RULA method showed that all subjects, with the exception of the experts using an axial-handled instrument, assume a prejudicial wrist posture during the practice of suturing and dissection tasks on the simulator. Conclusions: Data glove CyberGlove® allows for the distinction between two laparoscopic exercises performed with different instruments. Also, laparoscopic intracorporeal suturing when performed with an axial-handled needle holder entails a more ergonomic posture for the wrist joint. Previous minimally invasive surgical experience is a positive influencing factor on the surgeons’ wrist postures during laparoscopy.

Hand gesture recognition using low-budget data glove and cluster-trained probabilistic neural network

Purpose – Main purpose is to present methodology which allows efficient hand gesture recognition using low-budget, 5-sensor data glove. To allow widespread use of low-budget data gloves in engineering virtual reality (VR) applications, gesture dictionaries must be enhanced with more ergonomic and symbolically meaningful hand gestures, while providing high gesture recognition rates when used by different seen and unseen users. Design/methodology/approach – The simple boundary-value gesture recognition methodology was replaced by a probabilistic neural network (PNN)-based gesture recognition system able to process simple and complex static gestures. In order to overcome problems inherent to PNN – primarily, slow execution with large training data sets – the proposed gesture recognition system uses clustering ensemble to reduce the training data set without significant deterioration of the quality of training. The reduction of training data set is efficiently performed using three types of clustering algorithms, yielding small number of input vectors that represent the original population very well. Findings – The proposed methodology is capable of providing efficient recognition of simple and complex static gestures and was also successfully tested with gestures of an unseen user, i.e. person who took no part in the training phase. Practical implications – The hand gesture recognition system based on the proposed methodology enables the use of affordable data gloves with a small number of sensors in VR engineering applications which require complex static gestures, including assembly and maintenance simulations. Originality/value – According to literature, there are no similar solutions that allow efficient recognition of simple and complex static hand gestures, based on a 5-sensor data glove.

Glove-based Interaction in Virtual Surgical Training System

A virtual training system has been developed to train novice surgeons on using the new jig and fixture in a knee replacement surgery. In this study, we described how a virtual hand was employed in a Virtual Knee Replacement Surgery Training System. The virtual hand is equipped with HOMER-G, a selection and manipulation technique that combines the Arm-extension and Ray-casting technique for easy selection and manipulation of virtual object with the virtual grasping capability during selection task. The virtual jig and fixture are selected using Ray- casting technique and the arm extension technique of HOMER-G will help to manipulate the object. The virtual hand's movement is controlled by a data glove and Flock of Birds tracking device. HOMER-G has the grasping capability that makes the manipulation of object looks more natural.

Three-Dimensional Hand Tracking Based on Microstructure of Hand Motion

At present, hand tracking based on particle filter obtains current 3D hand gesture mainly by two elements, that is, by current frame of gesture images and by status information of the previous 3D hand gesture. Compared with current algorithms, the proposed algorithm focuses on behavior analysis and process modeling. Focusing on mathematical linkages establishment between microstructure model and the current frame gesture, the status of current 3D hand gesture is obtained not only on the basis of local frame information, but gesture model of overall movement process. After that, a unified and efficient data structure is presented, upon which the particle number can be reduced. First, hand motion law in specific circumstance is obtained by experiments based on data glove in the three-dimensional virtual platform. Second, the law is summarized as the microstructure of hand motion. Hand motion model is put forward and applied to forecast period of the particle filter algorithm, upon which hand tracking can be guided by the summarized microstructure. At last, hand tracking algorithm is carried out by a camera-oriented virtual three-dimensional interactive platform. The experimental result shows that, compared with Gaussian Process Annealed Particle Filter (GPAPF) gesture tracking algorithm, the time cost of the proposed algorithm was nearly 29% lower, while the accuracy 26% higher.

Development of Finger Motion Skill Learning Support System based on Data Gloves

Recently, more and more hearing person starts learning sign language. For finger motion skill learning such as sign language, it is important for an expert to give objective advice to a learner, because the learner is not able to find any motion errors in his/her sign language. In other words the learner needs the self-education tool of sign language which gives good objective advice. However, there are few studies about such a system. In this paper, we developed a finger motion skill learning support system using data gloves. This system helps a learner to recognize motion errors intuitively by himself/herself by overlaying skilled person's 3D models with learner's 3D models of hands and fingers.

Development of Motion Navigator II Enabling Integrated Motion Display of Whole Body with Fingers

In this study, we build a new system “Motion Navigator II”, which displays finger motion integrated with whole body motion with bone CG animation by AR. The function to display whole body motion is inherited from an existing motion skill learning support system “Motion Navigator”. The existing Motion Navigator can’t display fingers’ motion but shows limited variation of motion in precedent research. Therefore we acquired finger motion data using data gloves and improved Motion Navigator integrating the finger motion data with whole body motion data. We verified learning effect of the experimental group using Motion Navigator II in comparison with the control group using conventional video.

A real-time interaction strategy for virtual maintenance based on motion capture

This paper presents a real-time interaction strategy for virtual maintenance based on motion capture system. The strategy has integrated the real-time control function of motion capture systems and reusable function of motion capture data to solve the limited captured-domain problem in the motion capture system. The main techniques for this cross boundary research include maintenance feature matrix calculation, collision detection, interpolation algorithm, oriented switch, data glove and path planning, which have overcome a number of critical issues, such as data storage, automotive generation of transition frames, transformation of operation location and shifting of control approaches. Further the research has implemented the real-time interaction control of large-domain virtual maintenance operation.

Assessment of hand kinematics using inertial and magnetic sensors.

BackgroundAssessment of hand kinematics is important when evaluating hand functioning. Major drawbacks of current sensing glove systems are lack of rotational observability in particular directions, labour intensive calibration methods which are sensitive to wear and lack of an absolute hand orientation estimate.MethodsWe propose an ambulatory system using inertial sensors that can be placed on the hand, fingers and thumb. It allows a full 3D reconstruction of all finger and thumb joints as well as the absolute orientation of the hand. The system was experimentally evaluated for the static accuracy, dynamic range and repeatability.ResultsThe RMS position norm difference of the fingertip compared to an optical system was 5±0.5 mm (mean ± standard deviation) for flexion-extension and 12.4±3.0 mm for combined flexion-extension abduction-adduction movements of the index finger. The difference between index and thumb tips during a pinching movement was 6.5±2.1 mm. The dynamic range of the sensing system and filter was adequate to reconstruct full 80 degrees movements of the index finger performed at 116 times per minute, which was limited by the range of the gyroscope. Finally, the reliability study showed a mean range difference over five subjects of 1.1±0.4 degrees for a flat hand test and 1.8±0.6 degrees for a plastic mold clenching test, which is smaller than other reported data gloves.ConclusionCompared to existing data gloves, this research showed that inertial and magnetic sensors are of interest for ambulatory analysis of the human hand and finger kinematics in terms of static accuracy, dynamic range and repeatability. It allows for estimation of multi-degree of freedom joint movements using low-cost sensors.

Objective analysis of surgeons’ ergonomy during laparoendoscopic single-site surgery through the use of surface electromyography and a motion capture data glove

Adding to the ergonomic inconveniences already presented by traditional laparoscopy (LAP), laparoendoscopic single-site (LESS) surgery has been found to entail other more specific problems, including greater reduction in movement freedom, in-line vision with loss of triangulation, and greater proximity of instruments. The objective of this study was to evaluate surgeons' ergonomy during LESS surgery, through the study of muscular activity, wrist angle, and hand movements, and compare it with conventional laparoscopy. The study group was composed by 14 experienced laparoscopic surgeons, all right-handed. Each one performed dissection tasks on a physical simulator through LAP and LESS approaches. For LAP, straight laparoscopic scissors and dissector were used, whilst for LESS articulating tip scissors and dissector were chosen. During both tasks, muscular activity of biceps brachii, triceps brachii, forearm flexors and extensors, and trapezius muscles was registered through surface electromyography. Simultaneously right-hand movements and wrist angles were obtained through a motion capture data glove (CyberGlove(®)), which allowed for the use of a modified RULA test applied to the recorded angles with subsequent establishment of risk levels for the wrist joint. Muscular activity for trapezius (LAP 6.94 ± 4.12 vs. LESS 11.32 ± 4.68; p ≤ 0.05) and forearm extensor muscles (LAP 9.2 ± 2.45 vs. LESS 37.07 ≤ 16.05; p ≤ 0.001) was significantly lower in conventional laparoscopy compared with LESS approach. No statistical significance was obtained between the different sensors, except in 3 of the 11 analyzed CyberGlove(®) sensors. The modified RULA test showed a score of 3 for laparoscopy (unacceptable), whereas for LESS a score of 2 was obtained (acceptable), with statistically significant differences between them (p ≤ 0.05). The LESS approach entails greater level of muscular activity in the trapezius and forearm extensor muscles, but we have found evidences of a better wrist position during LESS compared with traditional laparoscopy.

Quantification of thumb carpometacarpal joint angles during opposition using data glove

Objective To measure the motion angles of thumb carpometacarpal joint during opposition using data glove of virtual reality technology.Methods Fifteen healthy volunteers (male) were tested to obtain normative data.Motion signals of thumb carpometacarpal joint during opposition movement were digitized using WISEGLOVE virtual reality data glove and processed by computer software to calculate the motion angles.Results During opposition,the carpometacarpal joint had motion ranges of (40.8 ± 6.9)°,(24.3 ± 5.6)°and (38.4 ± 7.2)° in flexion/extension,abduction/adduction and pronation/supination,respectively.Conclusion Data Glove motion analysis system could accurately measure motion angles of the carpometacarpal joint in flexion/extension,abduction/adduction and pmnation/supination,which provided a few parameters for diagnosis,treatment and rehabihtation of carpometacarpal joint injury. Key words: Hand; Computer simulation; Data glove; Carpometacarpal joint; Opposition

데이터 글로브와 진동모터를 이용한 촉각전달 및 제시 방법

This paper proposes a tactile transfer and display method between a data glove and vibration motors module. The data glove is developed to capture the hand postures and to measure the grip forces. The measured data are simplified with the proposed 5-bit transfer and display algorithm, and the vibration motors module is developed to display the measured hand posture and grip force to the operator. The proposed 5-bit algorithm contains both an 8-step hand posture and 4-step grip force level information for tactile transfer to the vibration motors module. Also, the effectiveness of the proposed method is shown through several experiments.

Super-stretchable, Transparent Carbon Nanotube-Based Capacitive Strain Sensors for Human Motion Detection

Realization of advanced bio-interactive electronic devices requires mechanically compliant sensors with the ability to detect extremely large strain. Here, we design a new multifunctional carbon nanotube (CNT) based capacitive strain sensors which can detect strains up to 300% with excellent durability even after thousands of cycles. The CNT-based strain gauge devices exhibit deterministic and linear capacitive response throughout the whole strain range with a gauge factor very close to the predicted value (strictly 1), representing the highest sensitivity value. The strain tests reveal the presented strain gauge with excellent dynamic sensing ability without overshoot or relaxation, and ultrafast response at sub-second scale. Coupling these superior sensing capabilities to the high transparency, physical robustness and flexibility, we believe the designed stretchable multifunctional CNT-based strain gauge may have various potential applications in human friendly and wearable smart electronics, subsequently demonstrated by our prototypical data glove and respiration monitor.