Wearable Data Glove Research Articles

Wearable glove gesture recognition based on fiber Bragg grating sensing using genetic algorithm-back propagation neural network

The recognition of gestures through intelligent wearable devices has a broad array of applications, including low-visibility industrial sites and robotics. However, accurately and swiftly recognizing gestures poses a significant challenge. This study addresses the complex interplay between multimodal information sensing and signal processing with flexible sensors in wearable data gloves by conducting theoretical and experimental investigations into gesture recognition using fiber Bragg grating (FBG) flexible sensors. First, the degree of bending and bending angle of the FBG sensor were calibrated, and the impact of temperature was investigated. Subsequently, a human hand wearable data glove gesture recognition system based on fiber grating sensing was constructed, and gesture recognition experiments were conducted. Finally, an enhanced error backpropagation (BP) neural network model for gesture recognition based on genetic algorithms (GA) was established and tested and the results were compared with those achieved using a conventional error BP neural network. The comparison revealed that under the same network structure, the recognition accuracy of the GA-BP neural network of approximately 100% was higher than the conventional BP neural network’s accuracy of 95.77%. Thus, the development of gesture recognition using FBG sensors provides a theoretical foundation and technical support for multiple applications of wearable data gloves, driving their adoption in areas such as human–computer interactions, intelligent robotics, and complex high-risk environments.

Multimodal Learning‐Based Proactive Human Handover Intention Prediction Using Wearable Data Gloves and Augmented Reality

Efficient object handover between humans and robots holds significant importance within collaborative manufacturing environments. Enhancing the efficacy of human–robot handovers involves enabling robots to comprehend and foresee human handover intentions. This article introduces human‐teaching–robot‐learning‐prediction framework, allowing robots to learn from diverse human demonstrations and anticipate human handover intentions. The framework facilitates human programming of robots through demonstrations utilizing augmented reality and a wearable dataglove, aligned with task requirements and human working preferences. Subsequently, robots enhance their cognitive capabilities by assimilating insights from human handover demonstrations, utilizing deep neural network algorithms. Furthermore, robots can proactively seek clarification from humans via an augmented reality system when confronted with ambiguity in human intentions, mirroring how humans seek clarity from their counterparts. This proactive approach empowers robots to anticipate human intentions and assist human partners during handovers. Empirical results underscore the benefits of the proposed approach, demonstrating highly accurate prediction of human intentions in human–robot handover tasks.

Sign Language Recognition with Multimodal Sensors and Deep Learning Methods

Sign language recognition is essential in hearing-impaired people’s communication. Wearable data gloves and computer vision are partially complementary solutions. However, sign language recognition using a general monocular camera suffers from occlusion and recognition accuracy issues. In this research, we aim to improve accuracy through data fusion of 2-axis bending sensors and computer vision. We obtain the hand key point information of sign language movements captured by a monocular RGB camera and use key points to calculate hand joint angles. The system achieves higher recognition accuracy by fusing multimodal data of the skeleton, joint angles, and finger curvature. In order to effectively fuse data, we spliced multimodal data and used CNN-BiLSTM to extract effective features for sign language recognition. CNN is a method that can learn spatial information, and BiLSTM can learn time series data. We built a data collection system with bending sensor data gloves and cameras. A dataset was collected that contains 32 Japanese sign language movements of seven people, including 27 static movements and 5 dynamic movements. Each movement is repeated 10 times, totaling about 112 min. In particular, we obtained data containing occlusions. Experimental results show that our system can fuse multimodal information and perform better than using only skeletal information, with the accuracy increasing from 68.34% to 84.13%.

3D Printable Organohydrogel with Long-Lasting Moisture and Extreme-Temperature Tolerance for Flexible Electronics.

Hydrogels with high electrical conductivity and mechanical stretchability are promising materials for flexible electronics. However, traditional hydrogels are applied in short-term usage at room temperature or low temperature due to their poor water-retention ability and freezing-tolerance property. Here, a dually cross-linked glycerol-organohydrogel (GL-organohydrogel) based on GL and acrylic acid was synthesized in a GL-water binary solvent. Fe3+ ions working as an electrolyte were added to improve the conductivity of the organohydrogel and form coordination interactions between Fe3+ ions and carboxyl groups of acrylic acid. The strong hydrogen bonding between GL and water molecules firmly lock water in the organohydrogel network, thereby endowing the GL-organohydrogel with the antifreezing property, long-term stability, and moisture lock-in capability. Our organohydrogel could endure extremely low temperature (-80 °C) over 30 days without freezing and retain its water content (almost 100% of its initial state) after being stored at room temperature (25 °C, 54% humidity) for 30 days. It also demonstrated desired stretchable properties, conductivity, three-dimensional (3D) printability, and self-healing ability. A wearable data glove was constructed by using the GL-organohydrogel and digital light processing technology. This work opens a new avenue for developing hydrogels with long-term stability, moisture lock-in capability, and extreme-temperature tolerance for stretchable electronics.

A Novel Human Intention Prediction Approach Based on Fuzzy Rules through Wearable Sensing in Human-Robot Handover.

With the use of collaborative robots in intelligent manufacturing, human-robot interaction has become more important in human-robot collaborations. Human-robot handover has a huge impact on human-robot interaction. For current research on human-robot handover, special attention is paid to robot path planning and motion control during the handover process; seldom is research focused on human handover intentions. However, enabling robots to predict human handover intentions is important for improving the efficiency of object handover. To enable robots to predict human handover intentions, a novel human handover intention prediction approach was proposed in this study. In the proposed approach, a wearable data glove and fuzzy rules are firstly used to achieve faster and accurate human handover intention sensing (HIS) and human handover intention prediction (HIP). This approach mainly includes human handover intention sensing (HIS) and human handover intention prediction (HIP). For human HIS, we employ wearable data gloves to sense human handover intention information. Compared with vision-based and physical contact-based sensing, wearable data glove-based sensing cannot be affected by visual occlusion and does not pose threats to human safety. For human HIP, we propose a fast handover intention prediction method based on fuzzy rules. Using this method, the robot can efficiently predict human handover intentions based on the sensing data obtained by the data glove. The experimental results demonstrate the advantages and efficacy of the proposed method in human intention prediction during human-robot handover.

Mass-Produced Flexible Strain Sensors Based on Dip-Coating and Water Bath for Human–Computer Interaction

Flexible strain sensors play a key role in the field of human–machine interaction because of their ability to sense deformation. To meet the needs of mass production of wearable electronic devices, it is very necessary to develop a strain sensor with excellent sensing performance and low-cost mass fabrication. In this article, a fabrication process of a flexible resistive strain sensor, which is perfectly matched by the combined method with dip-coating and water bath, is proposed. A layer of conductive polymers containing carbon black/multiwall carbon nanotubes/thermoplastic polyurethane (CB/MWCNTs/TPU) was quickly and evenly wrapped on the surface of the core-spun elastic yarn. Combining the synergistic conductive effect of the filler and the high elasticity of the yarn matrix, the designed flexible strain sensor has high sensitivity (gauge factor (GF) to 68), low detection limit, wide strain range, fast response time, fast recovery time, and high stability. The designed strain sensor exhibits outstanding sensing performance and wearability, which can be used for real-time monitoring of human joint bending and breathing state. Furthermore, a wireless operating system based on wearable data gloves has been developed to realize the remote synchronous movement of the human hand/the bionic manipulator. The results indicate that the proposed strain sensor has great potential in human–machine interaction and its fabrication technology provides guidance for the sensor in mass production.

Development of a Low-Cost Wearable Data Glove for Capturing Finger Joint Angles.

Capturing finger joint angle information has important applications in human–computer interaction and hand function evaluation. In this paper, a novel wearable data glove is proposed for capturing finger joint angles. A sensing unit based on a grating strip and an optical detector is specially designed for finger joint angle measurement. To measure the angles of finger joints, 14 sensing units are arranged on the back of the glove. There is a sensing unit on the back of each of the middle phalange, proximal phalange, and metacarpal of each finger, except for the thumb. For the thumb, two sensing units are distributed on the back of the proximal phalange and metacarpal, respectively. Sensing unit response tests and calibration experiments are conducted to evaluate the feasibility of using the designed sensing unit for finger joint measurement. Experimental results of calibration show that the comprehensive precision of measuring the joint angle of a wooden finger model is 1.67%. Grasping tests and static digital gesture recognition experiments are conducted to evaluate the performance of the designed glove. We achieve a recognition accuracy of 99% by using the designed glove and a generalized regression neural network (GRNN). These preliminary experimental results indicate that the designed data glove is effective in capturing finger joint angles.

Recognition and Classification of Dynamic Hand Gestures by a Wearable Data-Glove

This paper introduces a complete work-flow for the translation of dynamic isolated signs based on data acquired from a data-glove. A sign language translation system based on a wearable device represents indeed a more efficient solution with respect to cameras or position trackers for helping speech-impaired people on a daily basis. The paper describes the different steps required for a sign language translation, namely segmentation, feature extraction and classification, together with the custom data-glove used for data-acquisition. The paper presents also experimental results, comparing different machine learning classifiers and discussing their performances both in terms of translation accuracy and computational time. The proposed work-flow has been tested both on data acquired from a custom data-glove and on a public database, and it outperforms those of other works in literature. The reported analysis suggests a multi-layer perceptron neural network as the most suitable classifier for the realization of a wearable sign language translation system.

Deep Forest-Based Monocular Visual Sign Language Recognition

Sign language recognition (SLR) is a bridge linking the hearing impaired and the general public. Some SLR methods using wearable data gloves are not portable enough to provide daily sign language translation service, while visual SLR is more flexible to work with in most scenes. This paper introduces a monocular vision-based approach to SLR. Human skeleton action recognition is proposed to express semantic information, including the representation of signs’ gestures, using the regularization of body joint features and a deep-forest-based semantic classifier with a voting strategy. We test our approach on the public American Sign Language Lexicon Video Dataset (ASLLVD) and a private testing set. It proves to achieve a promising performance and shows a high generalization capability on the testing set.

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.

Emergency Gesture Communication by Patients, Elderly and Differently Abled with Care Takers Using Wearable Data Gloves

The Healthcare monitoring on a clinical base involves many implicit communication between the patient and the care takers. Any misinterpretation leads to adverse effects. A simple wearable system can precisely interpret the implicit communication to the care takers or to an automated support device. Simple and obvious hand movements can be used for the above purpose. The proposed system suggests a novel methodology simpler than the existing sign language interpretations for such implicit communication. The experimental results show a well-distinguished realization of different hand movement activities using a wearable sensor medium and the interpretation results always show significant thresholds.