Data Glove Research Articles

Basic Daily Activity Recognition with a Data Glove

Many people in the world are affected by the Alzheimer disease leading to the dysfunctionality of the hand. In one side, this symptom is not the most important of this disease and not much attention is given to this one. In the other side, the literrature provides two main solutions such as computer vision and data glove allowing to recognize hand gestures for virtual reality or robotic applications. From this finding and need, we decided to developed our own data glove prototype allowing to monitor the evolution of the dysfunctionality of the hand by recognizing objects in basic daily activities. Our approach is simple, cheap (~220$) and efficient (~100% of correct predictions) considering that we are abstracting all the theory about the gesture recognition. Also, we can access directly and easily to the raw data. Finally, the proposed prototype is described in a way that researchers can reproduce it.

Modeling simulation and virtual platform construction of crossbow making technology

In this paper, a set of virtual simulation platform for crossbow making is established. First, build a virtual model of Lisu crossbow, optimize the model, accelerate the organization, model generation, modification and editing of the database; then complete the database establishment of the corresponding model, nest the comment entity class and filemeta entity class into the file entity class, store them in a nested data set, and complete the data storage, management and query operations; finally, combined with Kalman filtering, arithmetic average filtering and KNN algorithm are used to build the quest 3D virtual platform to improve the 3D interaction effect with data gloves and complete the construction of the 3D platform.

An Electrooculogram-Based Interaction Method and Its Music-on-Demand Application in a Virtual Reality Environment

Recently, the devices commonly used for interaction with a virtual reality (VR) environment include game controllers, data gloves, and motion tracking systems. However, these devices may limit the immersive experience or cause inconvenience, especially for patients with limb paralysis. This paper proposes a new nonmanual human-computer interface (HCI) based on a single-channel electrooculogram (EOG) signal and enables real-time interactions with the VR environment. The graphical user interface of the EOG-based HCI in VR includes several buttons flashing in a random order. The user needs to blink in synchrony with the corresponding button's flashes to issue a command, while the algorithm detects the eye blinks from the EOG signal and determines the users' target button. Furthermore, with the EOG-based HCI, we developed a music-on-demand system in the VR environment. By blinking, users can search and select target songs in the music library, play the selected songs, switch to the next song, and delete songs from the list. Ten healthy subjects and five patients with spinal cord injuries (SCIs) participated in online experiments. The experimental results demonstrated the effectiveness of the EOG-based HCI as a new method for interacting with the VR environment. Furthermore, the music-on-demand system can be used for entertainment not only by healthy people but also by SCI patients with limb paralysis.

Development and Control of an MR Brake-Based Passive Force Feedback Data Glove

Development and Control of an MR Brake-Based Passive Force Feedback Data Glove

High-speed gesture modelling through boundary analysis of active signals from wearable data glove

High-speed gesture modelling through boundary analysis of active signals from wearable data glove

High-speed gesture modelling through boundary analysis of active signals from wearable data glove

Assistive technology utilises communication by means of gestures with high appreciation in the fields of rehabilitation engineering and security. The necessity for such technique is to extract the communications in terms of intentions from the disabled community. Such systems play a very crucial role during emergency as well as to perform regular communication during normal course of life. To interpret and communicate most distinctive requirements of the person with disability, the caregivers and medical support agents need a well- defined and distinguishable gesture paradigm with its recognition system. Conversion of such communicative gestures is to be made precise and easy. In this proposed work, the feature construction is made with a simple modelling of the gesture signals along the time zone considerations created during the gesture activity. The identification of most active channels for every subject involved in this experiment for different gestures contributes to reduction of complexity in processing and hardware cost.

(1610-3210) MAN - MACHINE INTERACTION SYSTEM FOR SUBJECT INDEPENDENT SIGN LANGUAGE RECOGNITION USING FUZZY HIDDEN MARKOV MODEL

Sign language recognition has spawned more and more interest in human–computer interaction society. The major challenge that SLR recognition faces now is developing methods that will scale well with increasing vocabulary size with a limited set of training data for the signer independent application. The automatic SLR based on hidden Markov models (HMMs) is very sensitive to gesture's shape information that makes the accurate parameters of the HMM not capable of characterizing the ambiguous distributions of the observations in gesture's features. This paper presents an extension of the HMMs using interval type-2 fuzzy sets (IT2FSs) to produce interval type-2 fuzzy HMMs to model uncertainties of hypothesis spaces (unknown varieties of parameters of the decision function). The benefit of this enlargement is that it can control both the randomness and fuzziness of traditional HMM mapping. Membership function (MF) of type-2 FS is three-dimensional that provides additional degrees of freedom to evaluate HMM's uncertainties. This system aspires to be a solution to the scalability problem, i.e. has real potential for application on a large vocabulary. Furthermore, it does not rely on the use of data gloves or other means as input devices, and operates in isolated signer-independent modes. Experimental results show that the interval type-2 fuzzy HMM has a comparable performance as that of the fuzzy HMM but is more robust to the gesture variation, while it retains almost the same computational complexity as that of the FHMM.

The application of data gloves in the evaluation of patients with hand injuries

Objective To develop a data glove evaluation system to assist rehabilitation physicians and therapists in assessing the degree of rehabilitation of hand-wounded patients. Methods A data glove containing 13 6-axis inertial sensors was designed and produced. It was tested on 10 healthy subjects and 3 hand trauma patients admitted to the Department of Rehabilitation Medicine at Run Run Shaw Hospital. The glove transmitted acceleration and angular velocity components in the X, Y and Z directions to a computer terminal, allowing the bending angles of the subject′s finger joints to be calculated. Traditional protractor measurements were used to verify to what extent the data glove accurately reflected the hand and joint activity. Results Compared with the traditional protractor measurement method, the data glove quickly gave accurate maximum bending angles both in healthy and hand-wounded subjects . Such data can facilitate the rehabilitation of hand injuries. Conclusions The data glove produced in this study can effectively assist rehabilitation doctors and therapists in assessing the recovery of hand-injured patients. Key words: Data gloves; Inertial sensors; Hand injury

Design and Research of Data Glove Based on Visual Information Fusion

Design and Research of Data Glove Based on Visual Information Fusion

Deep learning‐based grasp‐detection method for a five‐fingered industrial robot hand

To improve the accuracy of robotic grasp in some uncertain environments, a deep learning‐based object‐detection method for a five‐fingered industrial robot hand model is proposed in this study. The authors first design a five‐fingered industrial robot hand model with 21‐degrees of freedom (DOF). Based on the sensor data of a 5DT data glove, the industrial robot hand can be controlled in real time. They use the object‐detection network's faster regions convolutional neural network and single shot multibox detector to locate the grasp objects. To optimise the robotic grasp detection, two grasp‐predictor methods, direct grasp predictor and multi‐modal grasp predictor, are applied to obtain the best graspable region. In the simulation designed in this study, cooperating with a 6‐DOF robot arm, the five‐fingered industrial robot hand can detect an object accurately and grasp it steadily.

3D indirect shape retrieval based on hand interaction

In this work, we present a novel 3D indirect shape analysis method which successfully retrieves 3D shapes based on hand–object interaction. To this end, the human hand information is first transferred to the virtual environment by the Leap Motion controller. Position-, angle- and intersection-based novel features of the hand and fingers are used for this part. In the guidance of these features that define the way humans grab objects, a support vector machine (SVM) classifier is trained. Experiments validate that SVM results are useful for retrieval of 3D shapes. We also compare the retrieval performance of our method with an interaction-based indirect method based on the Data Glove controller as well as a direct method based on 3D shape distribution histograms. These comparisons reveal different advantages of our method, which are (i) being lower-cost and more accurate compared to the Data Glove, and (ii) being more discriminative compared to a direct approach. We finally note that our algorithm is rigid-motion invariant and able to explore databases of arbitrarily represented 3D shapes.

Development and Design Wearable Sensorial System of Low Cost to Determine the Metacarpophalangeal and Interphalangeal Joints Angles

This paper presents the results of a low-cost wearable sensory system design process for the simultaneous capture of the angles of the metacarpophalangeal and interphalangeal joints of the human hand, as well as the characterization of its performance compared with similar commercial systems, The processing subsystem has machine learning algorithms that were compared in order to identify the optimal technique for capturing information corresponding to basic movements such as hold different objects and characterizing displacements at the level of phalanges in the hand. The development was carried out at the hardware level through the manufacture of a glove with flexible resistive sensors of the reference SEN-10264 (it is a sensor with resistive cells that varies its value in ohms depending on the flexion to which it is subjected) two per each finger, for a total of 10 sensors adhered to the surface of the glove, after going through a coupling stage (to avoid large amounts of noise) the signals generated by the sensors are acquired through the data capture module Analogs of a Microcontroller. In terms of software, the Microcontroller was first programmed with a digital filter stage for the signals acquired by the SEN-10264 flex sensor sensors and a machine learning algorithm for the processing of these and in this way obtain the individual metacarpophalangeal and interphalangeal joints angles of the hand evaluated. Finally, obtain the sensor data in terms of angles and export it to a flat text file to be graphed. For a total of 10 people, multiple acquisitions were made with the developed system and in this way, similar patterns were determined in the angles of the metacarpophalangeal and interphalangeal between the test subjects in basic tasks such as hold different types of objects. Finally, the development of the sensor system was validated with two types of wearable sensors: the 5DT Data Glove device and the Optitrack Flex 3 high-speed cameras, these two types of devices have acquisition protocols predetermined by their manufacturers. Given that the variables to obtain are the metacarpophalangeal and interphalangeal joints angles, the first 5DT Data Glove device delivers only one output signal for each finger of the evaluated hand, basically the software of the manufacturer performs a mathematical interpolation and approximations depending on the postures of the fingers, which makes it incapable by means of this technique to determine if a flexion is occurring from the metacarpophalangeal and interphalangeal joints separately, the Optitrack Flex 3 high-speed cameras generate real-time data graphs of the position in 3d and the trajectories of each of the joints of the hand, the problem is that the system performs the capture of data through markers located in the hand and first of all the size of these makes them crash in a task of grip in addition to making the task of characterizing the ag arrest because the viewing angles of the cameras must detect each marker located in the joints of the hand. The individual angles will be used later to carry out a simulation project of a postural biological synergy of some basic object grabs (with a control command generating movement in several degrees of freedom) through the implementation of an artificial intelligence algorithm.

New Application of Children’s Sign Language Enlightenment Based on Data Glove

In order to enhance the learning interest of deaf-mute children as beginners in sign language and promote the balance of educational reform, we have developed a new application which based on data gloves, to be the enlightenment for children learning sign language. The application mainly includes three parts: data acquisition and conversion, wireless communication, and cartoon interactive application. The application, taking STM32 development board as the control core, is integrated with multiple sensor function modules including Wi-Fi wireless communication, three-axis gyroscope, bending resistor and others, therefore, it can realize the gesture recognition and data transmission. After repeated testing, the system has high accuracy in gesture recognition, not only data transmission is reliable but also the application response timely. Deaf-mute children can use the cartoonish application to get sign language examination questions and to answer by wearing data gloves, and then the system will give a star rating comprehensively based on whether the answer is correct, on whether the cue is clicked in answering question and on other aspects, so as to realize the interaction with the PC program and let deaf-mute children contact and learn sign language in games. The design concept of the application has received favorable response from Qingdao school for the deaf, which has a certain practical and promoted value.

A Modular Data Glove System for Finger and Hand Motion Capture Based on Inertial Sensors

This study proposes a modular data glove system to accurately and reliably capture hand kinematics. This data glove system’s modular design enhances its flexibility. It can provide the hand’s angular velocities, accelerations, and joint angles to physicians for adjusting rehabilitation treatments. Three validations—raw data verification, static angle verification, and dynamic angle verification—were conducted to verify the reliability and accuracy of the data glove. Furthermore, to ensure the wearability of the data glove, 15 healthy participants and 15 participants with stroke were recruited to test the data glove and fill out a questionnaire. The errors of the finger ROMs obtained from the fusion algorithm were less than 2°, proving that the fusion algorithm can measure the wearer’s range of motion accurately. The result of the questionnaire shows the participants’ high satisfaction with the data glove. Moreover, a comparison between the proposed data glove and related research shows that the proposed data glove is superior to other data glove systems.

Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user’s hand. A good concept hereby allows to intuitively switch the interaction context on demand by using different hand gestures. The recognition of various, possibly complex hand gestures, however, introduces unintentional overhead to the system. Consequently, we present a data glove prototype comprising a glove-embedded gesture classifier utilizing data from Inertial Measurement Units (IMUs) in the fingertips. In an extensive set of experiments with 57 participants, our system was tested with 22 hand gestures, all taken from the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, using complementary filter with a gyroscope-to-accelerometer ratio of 93%. Our approach has also been compared to the local fusion algorithm on an IMU motion sensor, showing faster settling times and less delays after gesture changes. Real-time performance of the recognition is shown to occur within 63 milliseconds, allowing fluent use of the gestures via Bluetooth-connected systems.

Design of an Inertial-Sensor-Based Data Glove for Hand Function Evaluation.

Capturing hand motions for hand function evaluations is essential in the medical field. Various data gloves have been developed for rehabilitation and manual dexterity assessments. This study proposed a modular data glove with 9-axis inertial measurement units (IMUs) to obtain static and dynamic parameters during hand function evaluation. A sensor fusion algorithm is used to calculate the range of motion of joints. The data glove is designed to have low cost, easy wearability, and high reliability. Owing to the modular design, the IMU board is independent and extensible and can be used with various microcontrollers to realize more medical applications. This design greatly enhances the stability and maintainability of the glove.

A Stretchable, Flexible Triboelectric Nanogenerator for Self‐Powered Real‐Time Motion Monitoring

AbstractStretchable triboelectric nanogenerators have attracted increasing interests in the field of wearable electronics. In this work, a stretchable and flexible triboelectric nanogenerator (SF‐TENG) with a ladder structure that consists of several single‐electrode TENG units on the elastic rubber substrate is developed. The structural design of the ladder‐shaped SF‐TENG is found to greatly improve the mechanical flexibility and stretching ability under small external force, and it works well even under the elongation of 120% and curvature of 90º. The SF‐TENG with optimized structure can have excellent linear response to tensile force and act as a strain sensor. The sensitivities of output voltage to tensile force and tensile displacement can reach 2.01 V N−1 and 0.0412 V mm−1, respectively. Moreover, a SF‐TENG‐based data glove is fabricated to demonstrate the potential application of such TENG in the human–machine interaction through the stone–paper–scissors game.

State anxiety disorganizes finger movements during musical performance

Skilled performance, in many situations, exposes an individual to psychological stress and fear, thus triggering state anxiety and compromising motor dexterity. Suboptimal skill execution in people under pressure affects the future career prospects of trained individuals, such as athletes, clinicians, and musicians. However, it has not been elucidated in what manner state anxiety affects multijoint movements and thereby degrades fine motor control. Using principal component analysis of hand kinematics recorded by a data glove during piano performances, we tested whether state anxiety affects the organization of movements of multiple joints or merely constrains the amplitude of the individual joints without affecting joint movement coordination. The result demonstrated changes in the coordination of movements across joints in piano performances by experts under psychological stress. Overall, the change was characterized by reduction of synergistic movements between the finger responsible for the keypress and its adjacent fingers. A regression analysis further identified that the attenuation of the movement covariation between the fingers was associated with an increase in temporal error during performance under pressure. In contrast, neither the maximum nor minimum angles of the individual joints of the hand were susceptible to induced anxiety. These results suggest that degradation of fine motor control under pressure is mediated by incoordination of movements between the fingers in skilled piano performances. NEW & NOTEWORTHY A key issue in neuromuscular control of coordinated movements is how the nervous system organizes multiple degrees of freedom for production of skillful motor behaviors. We found that state anxiety disorchestrates the organization of finger movements so as to decrease synergistic motions between the fingers in musical performance, which degrades fine motor control. The findings are important to shed light on mechanisms underlying loss of motor dexterity under pressure.

Assessment of Postural Ergonomics and Surgical Performance in Laparoendoscopic Single-Site Surgery Using a Handheld Robotic Device.

New laparoscopic devices are being continuously developed to overcome some of the technical and ergonomic limitations of laparoendoscopic single-site (LESS) surgery. This study aims to assess the surgeon's surgical performance and ergonomics during the use of a handheld, robotic-driven, articulating laparoscopic instrument during LESS surgery. Seven right-handed experienced surgeons took part in this study. A set of basic suturing tasks and digestive and urological procedures in a porcine model were performed. Surgeons used both a conventional laparoscopic needle holder and a robotic device. The learning curve, execution time, and precision using the surgical needle were assessed. The surgeon's posture was analyzed using a motion tracking system and a data glove. After the training period, execution time on the intracorporeal suturing was significantly shorter using the conventional needle holder. The precision was higher using the conventional instrument in the horizontal plane, but the number of attempts to position the needle was lower using the robotic device (1.625 ± 0.250 vs 1.188 ± 0.375 attempts). The extension of the elbow (134.681 ± 14.35° vs 120.631 ± 13.134°) and the flexion of the shoulder (26.122 ± 7.411° vs 18.475 ± 14.166°) were significantly lower using the robotic instrument. The wrist posture using the robotic device was ergonomically acceptable during both surgical procedures. Results show a positive learning curve in ergonomics and surgical performance using the robotic instrument during LESS surgery. This instrument improves the surgeon's body posture and the needle positioning errors. The use of the robotic instrument is feasible and safe during LESS partial nephrectomy and sigmoidectomy procedures.

Recognition of Sign Language Alphabets and Numbers based on Hand Kinematics using A Data Glove

This paper reports real-time recognition of Indian and American sign language alphabets and numbers based on hand kinematics assessment. The finger and wrist joint angles were acquired using an indigenously developed data glove. The data set was for single handed Indian sign language alphabets (C, I, J, L, O, U, Y, W), American sign language alphabets (A to Z) and sign numbers (0 to 9). The data were pre-processed through a moving average filter and standardized feature scaling methods. The glove was able to measure the finger joint angles with an accuracy±standard deviation for metacarpophalangeal (MCP) joint±2.14°, proximal inter phalangeal (PIP) joint± 1.73° and distal inter phalangeal (DIP) joint± 1.49°, during flexion/extension and abduction/adduction movements. A radial basis function kernel support vector machine with 10-fold cross validation was used for recognition. An average recognition rate of 96.7% was achieved. Using a label matching and speech data base, the recognized alphabets and numbers were translated into speech.