Glove System Research Articles

Systematic Evaluation of IMU Sensors for Application in Smart Glove System for Remote Monitoring of Hand Differences.

Human hands have over 20 degrees of freedom, enabled by a complex system of bones, muscles, and joints. Hand differences can significantly impair dexterity and independence in daily activities. Accurate assessment of hand function, particularly digit movement, is vital for effective intervention and rehabilitation. However, current clinical methods rely on subjective observations and limited tests. Smart gloves with inertial measurement unit (IMU) sensors have emerged as tools for capturing digit movements, yet their sensor accuracy remains underexplored. This study developed and validated an IMU-based smart glove system for measuring finger joint movements in individuals with hand differences. The glove measured 3D digit rotations and was evaluated against an industrial robotic arm. Tests included rotations around three axes at 1°, 10°, and 90°, simulating extension/flexion, supination/pronation, and abduction/adduction. The IMU sensors demonstrated high accuracy and reliability, with minimal systematic bias and strong positive correlations (p > 0.95 across all tests). Agreement matrices revealed high agreement (<1°) in 24 trials, moderate (1-10°) in 12 trials, and low (>10°) in only 4 trials. The Root Mean Square Error (RMSE) ranged from 1.357 to 5.262 for the 90° tests, 0.094 to 0.538 for the 10° tests, and 0.129 to 0.36 for the 1° tests. Likewise, mean absolute error (MAE) ranged from 0.967 to 4.679 for the 90° tests, 0.073 to 0.386 for the 10° tests, and 0.102 to 0.309 for the 1° tests. The sensor provided precise measurements of digit angles across 0-90° in multiple directions, enabling reliable clinical assessment, remote monitoring, and improved diagnosis, treatment, and rehabilitation for individuals with hand differences.

Triboelectric-Inertial Sensing Glove Enhanced by Charge-Retained Strategy for Human-Machine Interaction.

As technology advances, human-machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric-based sensors, with their self-powered, cost-effective, and material various characteristics, can offer promising solutions for enhancing existing glove systems. However, a key limitation of these sensors is that charge leakage in the measurement circuit results in capturing only transient signals, rather than continuous changes. To address this issue, a charge-retained circuit that effectively prevents triboelectric signal attenuation is developed, enabling accurate measurement of continuous finger movements. This innovation forms the foundation of a highly integrated smart glove system, enhancing HMI functionality by combining continuous triboelectric signals with inertial sensor data. The system showcases a diverse range of applications, including complex robotic control, virtual reality interaction, smart home lighting adjustments, and intuitive interface operations. Furthermore, by leveraging artificial intelligence (AI) techniques, the system achieves accurate recognition of complex sign language with an impressive 99.38% accuracy. This work presents a charge-retained approach for continuous sensing with triboelectric-based sensors, offering valuable insights for developing future multifunctional HMI and sign language recognition systems. The proposed smart glove system, with its dual-mode sensing and AI integration, holds great potential for revolutionizing various domains and enhancing user experiences.

Human–robot interaction via wearable device—a wireless glove system for remote control of 7-DoF robotic arm

Human–robot interaction via wearable device—a wireless glove system for remote control of 7-DoF robotic arm

Design and application of pneumatic rehabilitation glove system based on brain-computer interface.

Stroke has been the second leading cause of death and disability worldwide. With the innovation of therapeutic schedules, its death rate has decreased significantly but still guides chronic movement disorders. Due to the lack of independent activities and minimum exercise standards, the traditional rehabilitation means of occupational therapy and constraint-induced movement therapy pose challenges in stroke patients with severe impairments. Therefore, specific and effective rehabilitation methods seek innovation. To address the overlooked limitation, we design a pneumatic rehabilitation glove system. Specially, we developed a pneumatic glove, which utilizes ElectroEncephaloGram (EEG) acquisition to gain the EEG signals. A proposed EEGTran model is inserted into the system to distinguish the specific motor imagination behavior, thus, the glove can perform specific activities according to the patient's imagination, facilitating the patients with severe movement disorders and promoting the rehabilitation technology. The experimental results show that the proposed EEGTrans reached an accuracy of 87.3% and outperformed that of competitors. It demonstrates that our pneumatic rehabilitation glove system contributes to the rehabilitation training of stroke patients.

MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking.

Brain-computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements-open/close, finger tapping, and wrist rotation-along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.

Stretchable glove for accurate and robust hand pose reconstruction based on comprehensive motion data

We propose a compact wearable glove capable of estimating both the finger bone lengths and the joint angles of the wearer with a simple stretch-based sensing mechanism. The soft sensing glove is designed to easily stretch and to be one-size-fits-all, both measuring the size of the hand and estimating the finger joint motions of the thumb, index, and middle fingers. The system was calibrated and evaluated using comprehensive hand motion data that reflect the extensive range of natural human hand motions and various anatomical structures. The data were collected with a custom motion-capture setup and transformed into the joint angles through our post-processing method. The glove system is capable of reconstructing arbitrary and even unconventional hand poses with accuracy and robustness, confirmed by evaluations on the estimation of bone lengths (mean error: 2.1 mm), joint angles (mean error: 4.16°), and fingertip positions (mean 3D error: 4.02 mm), and on overall hand pose reconstructions in various applications. The proposed glove allows us to take advantage of the dexterity of the human hand with potential applications, including but not limited to teleoperation of anthropomorphic robot hands or surgical robots, virtual and augmented reality, and collection of human motion data.

GESTURE CONTROLLED SMART GLOVE WITH WEB APPLICATION

his paper presents a gesture-controlled smart glove system integrated with a web application for interactive user interface and control. The glove utilizes an array of sensors, such as accelerometers, gyroscopes, and flex sensors, to detect hand movements and gestures with high precision. These sensors capture real-time data, which is processed by an embedded microcontroller unit (MCU) onboard the glove. The processed data is then transmitted wirelessly to a web application via Bluetooth or Wi-Fi connectivity. The web application, accessible through a browser on any device, provides a user-friendly interface for controlling various applications and devices remotely. Users can customize gesture mappings to specific actions, enabling seamless interaction with IoT devices, virtual reality environments, and computer applications. The system's architecture emphasizes modularity and scalability, allowing for easy integration of additional sensors or functionalities. Moreover, the web application employs responsive design principles, ensuring compatibility with different screen sizes and devices. In conclusion, the proposed gesture-controlled smart glove system offers a versatile and intuitive interface for interacting with digital environments. Its integration with a web application enhances accessibility and usability, making it suitable for a wide range of applications, including gaming, rehabilitation, and virtual control interfaces. Key Words: MCU, Flex sensors, Web Application, Gesture Control, IOT

Travaler Empowerment through Iot - Based Wearable Health Monitoring Glove

The Travel Empowerment with IoT-Based Wearable Health Monitoring Gloves for Accident Prevention is a cutting-edge device that uses the Internet of Things (IoT) to increase road safety and empower travelers. This innovative system includes a wearable device that resembles gloves and is equipped with state-of-the-art sensors, including GPS, GSM, ultrasonic, infrared (IR), and a heart rate sensor. The intention behind incorporating these sensors is to provide travelers with a comprehensive safety net. The gloves' GPS and GSM components provide precise position monitoring and a real-time communication with emergency services. By using this service, travelers may be sure that assistance will be dispatched right to their area in the case of an accident or tragedy. The passenger receives an extra layer of safety from the ultrasonic sensor, which proactively prevents accidents by detecting potential hazards or blockages in the traveler's immediate surroundings. By identifying any hazards that the human eye might overlook, the infrared sensor enhances the safety system even more. This comprehensive hazard detection system improves overall travel safety as well as the prevention of accidents. Additionally, a heart rate sensor included into the gloves monitors the wearer's physiological well-being over time. In the case of an emergency while traveling, the technology may promptly send out alarms in the event of aberrant heart rate patterns. The Travel Empowerment system gathers data in real-time from various sensors. The gathered data is subsequently processed by a clever algorithm. In the case of an anomaly or emergency, the gloves instantly sound a warning, providing vital information to emergency contacts, including the traveler's whereabouts, the severity of the issue, and their heart rate. This Internet of Things (IoT)-based wearable health monitoring glove system not only addresses the urgent problems of emergency response and accident avoidance, but it also allows users to track their health in real time while traveling. By enabling a complete approach to travel safety, the combination of GPS, GSM, IR, ultrasonic, and heart rate sensors gives customers a more empowered and secure travel experience.

Triboelectric in-sensor deep learning for self-powered gesture recognition toward multifunctional rescue tasks

Gesture recognition is an effective method to voice-off communication in daily life, offering an intuitive and immersive means to interactive terminal. Current commercialized solutions to gesture recognition are exposed to limitations of either susceptibility to environmental conditions or the huge cost on fabrication, energy, and computing power. Here, we propose a deep-learning-enabled smart gesture recognition glove system that can capture and recognize the hand gesture in real time and execute the users’ interactive instructions for multifunctional rescue tasks. The self-powered gesture recognition glove system can successfully classify 7 typical gestures with an accuracy as high as 98.57%. As a proof of concept, a rescue vehicle equipped with water pump, dexterous robotic arm, or laser radar is demonstrated to be guided by gestures outside the environment to complete rescue tasks even in environments that are inaccessible to humans. We aim to use the self-powered gesture recognition glove for future intelligent interaction and intelligent manufacturing, especially in the diversified scenarios that require more advanced and dexterous manipulations in the dark or blocked spaces.

An Intelligent Glove System for Real‐Time Multiple Electrical Signal Acquisition and Display

AbstractRealizing multimodal tactile perception for robots is highly desirable for achieving more intelligent human–machine interaction. A real‐time intelligent glove system that can capture and monitor hand tactile information, including pressure, bending, and temperature is proposed. This system processes the collected data and visualizes the tactile information in a 2D image. The intelligent glove system integrates a palm‐shaped sensor unit, a multi‐channel data acquisition board, and a data analysis module. The glove consists of 34 flexible sensors, including 28 pressure sensors, five bending sensors, and one temperature sensor, which convert biological information into electrical signals. By enhancing the performance of the system, the intelligent glove achieves high sensitivity and good fault tolerance, allowing it to analyze the multimodal tactile information of the entire hand with fast responsivity. By analyzing the acquired multi‐channel electrical signals, the system can analyze object shapes and various gripping states. This work paves the way for wearable devices used in multimodal tactile perception and promotes the emergence of new modes of human–machine interaction.

Implementing Sign Language Recognition System using Flex Sensors

Sign languages rely on a combination of handshapes, facial expressions, and body movements to convey meaning. They are usually learnt at a tender age as one’s first language. This paper is aimed at designing, implementing, and developing a sensor-based smart glove system for the recognition of sign language. The system includes a wearable glove embedded with flex sensors for detecting hand movement and gestures used in sign language. This information from the sensors is fed into a microcontroller running an algorithm that identifies the signs and modulates them into speech. The system was aimed to provide an inexpensive and effective solution for the deaf/dumb community to communicate with others through sign language. Thirty-one phrases have been successfully obtained in the implemented system. The evaluation regarding the performance of the system was conducted by conducting user studies and tests, and results were presented to show the effectiveness of the proposed solution.

AI-based Sensory Glove System to Recognize Bengali Sign Language (BaSL)

AI-based Sensory Glove System to Recognize Bengali Sign Language (BaSL)

Synergistic advancements in high-performance flexible capacitive pressure sensors: structural modifications, AI integration, and diverse applications.

The development of flexible pressure sensors for monitoring human motion and physiological signals has attracted extensive scientific research. However, achieving low monitoring limits, a wide detection range, large bending stresses, and excellent mechanical stability simultaneously remains a serious challenge. With the aim of developing a high-performance capacitive pressure sensor (CPS), this paper introduces the successful preparation of a single-walled carbon nanotube (SWNT)/polydimethylsiloxane (S-PDMS) composite dielectric with a foam-like structure (high permittivity and low elasticity modulus) and MXene/SWNT (S-MXene) composite film electrodes with a micro-crumpled structure. The above structurally modified CPS (SMCPS) demonstrated an excellent response output during pressure loading, achieving a wide pressure detection range (up to 700 kPa), a low detection limit (16.55 Pa), fast response/recovery characteristics (48/60 ms), enhanced sensitivity across a wide pressure range, long-term stability under repeated heavy loading and unloading (40 kPa, >2000 cycles), and reliable performance under various temperature and humidity conditions. The SMCPS demonstrated a precise and stable capacitive response in monitoring subtle physiological signals and detecting motion, owing to its unique electrode structure. The flexible device was integrated with an Internet of Things module to create a smart glove system that enables real-time tracking of dynamic gestures. This system demonstrates exceptional performance in gesture recognition and prediction with artificial intelligence analysis, highlighting the potential of the SMCPS in human-machine interface applications.

3D Printed Robotic Hand with Piezoresistive Touch Capability

This work proposes the design of a low-cost sensory glove system that complements the operation of a 3D-printed mechanical hand prosthesis, providing it with the ability to detect touch, locate it and even measure the intensity of associated forces. Firstly, the production of the prosthetic model was performed using 3D printing, which allowed for quick and cheap production of a robotic hand with the implementation of a mechanical system that allows controlled movements with high performance and with the possibility of easily replacing each piece individually. Secondly, we performed the construction and instrumentation of a complementary sensory mimicry add-on system, focusing on the ability to sense touch as the primary target. Using piezoresistive sensors attached to the palm of the glove, a multi-sensor system was developed that was able to locate and quantify forces exerted on the glove. This system showed promising results and could be used as a springboard to develop a more complex and multifunctional system in the future.

Design and fabrication of a moving robotic glove system

&lt;p&gt;&lt;span lang="EN-US"&gt;This paper presents the research, design, and manufacture of a robotic hand to control movement with a glove. The moving glove-controlled robotic hand is based on two main parts: the hand mechanism and the control circuit. The control glove unit includes an Arduino nRF24l01 microcontroller module and five flex sensors for five fingers. These sensors are used to collect data about the curvature of each finger. Then those data will be received by the Arduino microcontroller and sent by the nRF24l01 module. The hand's microcontroller will process that information and control five servo motors so that the five fingers of the robotic hand are moved. The result of this research is to produce a robotic hand that accurately simulates the curvature of a user's finger and mimics the motion of a glove well. Moreover, the robot hand can grip objects of different sizes (from 0.1 to 1 kg) and shapes, from which this robot helps users easily manipulate objects.&lt;/span&gt;&lt;/p&gt;

Restorative Hand Therapy Exercise using IoT-Based Flex Force Smart Glove

Stroke has long been a major health concern in Malaysia, with cerebrovascular disease accounting for one-third of all deaths. Patients who have had a stroke have varying degrees of movement impairment, which can be improved with physiotherapy. A smart glove system is proposed in this paper to measure and visualize patient finger flexes in real-time remotely and record these flexes for further analysis to monitor and assess patient physiotherapy sessions. The system is designed to help physiotherapists evaluate physiotherapy exercises of patients and provide detailed information regarding the rehabilitation process. This paper also includes performance analysis and data from physiotherapy sessions using the system. Overall, the flex force smart glove shows promise in aiding restorative hand therapy exercises.

Artificial Intelligence–Enabled Gesture‐Language‐Recognition Feedback System Using Strain‐Sensor‐Arrays‐Based Smart Glove

Wearable smart glove of gesture language provides a novel strategy for the hearing‐impaired people to commutate with the world. Current commercialized solutions of gesture language are limited by the full extent of human interaction beyond operation dexterity, sensory feedback, and the huge cost of fabrication. Herein, a low‐cost, high‐efficient gesture‐language‐recognition feedback system combined with the strain‐sensor arrays and machine‐learning technology is proposed. The strain‐sensor arrays integrated with 3D‐printed glove can extract both spatial and temporal information about the finger's movement. The smart glove achieves gesture‐language recognition using machine learning with an accuracy of over 99%. Integrating with multidimensional manipulation, visual feedback and artificial intelligence (AI)‐based gesture‐language recognition, the smart system can accurately recognize complex gestures and provide real‐time feedback to users. The smart glove system can not only provide an efficient way for hearing‐impaired persons to communicate with the outside world, but also benefit industries in multiple fields such as entertainment, home healthcare, sports training, and the medical industry.

Design, Control, and Experimental Evaluation of A Novel Robotic Glove System for Patients with Brachial Plexus Injuries.

This paper presents the development of an exoskeleton glove system for people who suffer from brachial plexus injuries, aiming to assist their lost grasping functionality. The robotic system consists of a portable glove system and an embedded controller. The glove system consists of Linear Series Elastic Actuators (LSEA), Rotary Series Elastic Actuators (RSEA), and optimized finger linkages to provide imitated human motion to each finger and a coupled motion of the hand. The design principles and optimization strategies were investigated to balance functionality, portability, and stability. The model-based force control strategy compensated with a backlash model and model-free force control strategy are presented and compared. Results show that our proposed model-free control method achieves the goal of accurate force control. Finally, experiments were conducted with the prototype of the developed integrated exoskeleton glove system. Results from 3 subjects with 150 trials show that our proposed exoskeleton glove system has the potential to be used as a rehabilitation device for patients.

Patient-Therapist Cooperative Hand Telerehabilitation through a Novel Framework Involving the Virtual Glove System.

Telerehabilitation is important for post-stroke or post-surgery rehabilitation because the tasks it uses are reproducible. When combined with assistive technologies, such as robots, virtual reality, tracking systems, or a combination of them, it can also allow the recording of a patient's progression and rehabilitation monitoring, along with an objective evaluation. In this paper, we present the structure, from actors and functionalities to software and hardware views, of a novel framework that allows cooperation between patients and therapists. The system uses a computer-vision-based system named virtual glove for real-time hand tracking (40 fps), which is translated into a light and precise system. The novelty of this work lies in the fact that it gives the therapist quantitative, not only qualitative, information about the hand's mobility, for every hand joint separately, while at the same time providing control of the result of the rehabilitation by also quantitatively monitoring the progress of the hand mobility. Finally, it also offers a strategy for patient-therapist interaction and therapist-therapist data sharing.

Mixed reality-integrated soft wearable biosensing glove for manipulating objects

Recent advances in flexible sensors and wireless electronics have driven the development of lightweight and ergonomic wearable sensing gloves. Such gloves can be employed in mixed reality (MR) environments to give haptic capabilities during interactions with various objects. However, no prior study shows a quantitative measurement of physical user interactions of object manipulation in MR. Here, we report an MR-integrated soft bioelectronic system on a glove for quantifying the changes in the user's pinching tasks. We use nanomanufacturing techniques to fabricate flexible sensors, wireless circuits, and stretchable interconnectors seamlessly integrated with a wearable glove. The wearable biosensing glove with an integrated capacitive pressure sensor evaluates how users interact directly and indirectly interact with objects. The direct mode describes a user's direct touching and manipulating objects in MR. In contrast, in the indirect mode, objects are located far away and touched via a narrow light beam. The virtual object measurement parameters include mass, movement latency, dynamic friction coefficient, angular drag coefficient, and linear drag coefficient. The experimental results with human subjects show positive, linear relationships between pinching force and dynamic friction coefficient and mass parameters during the direct manipulation mode. Collectively, the MR-enabled wearable biosensing glove system offers unique advantages in detecting physical interactions and sensory feedback for various rehabilitation applications and MR human-machine interfaces.