Sensor Glove Research Articles

The fabrication of water-stable perovskite-europium hybrid polychromatic fluorescence nanosensor for fast visual sensing of tetracycline

The development of real-time and efficient visual tetracycline sensors is helpful to evaluate the pollution status of tetracycline antibiotics in the environment. Considering the unique fluorescence properties of perovskite and rare earth elements, an innovative water-stable perovskite-europium hybrid fluorescence sensor MAPbBr3@PbBr(OH)@SiO2-Cit-Eu was fabricated. The introduction of tetracycline resulted in the increase of the fluorescence intensity of Eu complex, while the fluorescence intensity of perovskite quantum dots decreased slowly, accompanied by visual green-red switching. The sensor had good linear response in the range of 0–25 μM, and the detection limit was 11.15 nM. With the help of color analysis software in smart phones, the real-time intelligent detection of TC was realized. In addition, the wearable glove sensor obtained by fixing the nanosensor in the gel can realize the qualitative and semi-quantitative visual sensing of TC on the surface of many foods (eggs, apples, bananas, pears and oranges). It is believed that this work will open a new avenue for perovskite-europium hybrid nanosensors in intelligent sensing fields.

Sensing System for Plegic or Paretic Hands Self-Training Motivation.

Patients after stroke with paretic or plegic hands require frequent exercises to promote neuroplasticity and to improve hand joint mobilization. Available devices for hand exercising are intended for persons with some level of hand control or provide continuous passive motion with limited patient involvement. Patients can benefit from self-exercising where they use the other hand to exercise the plegic or paretic one. However, post-stroke neuropsychological complications, apathy, and cognitive impairments such as forgetfulness make regular self-exercising difficult. This paper describes Przypominajka v2—a system intended to support self-exercising, remind about it, and motivate patients. We propose a glove-based device with an on-device machine-learning-based exercise scoring, a tablet-based interface, and a web-based application for therapists. The feasibility of on-device inference and the accuracy of correct exercise classification was evaluated on four healthy participants. Whole system use was described in a case study with a patient with a paretic hand. The anomaly classification has an accuracy of 91.3% and value of 91.6% but achieves poorer results for new users (78% and 81%). The case study showed that patients had a positive reaction to exercising with Przypominajka, but there were issues relating to sensor glove: ease of putting on and clarity of instructions. The paper presents a new way in which sensor systems can support the rehabilitation of after-stroke patients with an on-device machine-learning-based classification that can accurately score and contribute to patient motivation.

Improving Data Glove Accuracy and Usability Using a Neural Network When Measuring Finger Joint Range of Motion.

Data gloves capable of measuring finger joint kinematics can provide objective range of motion information useful for clinical hand assessment and rehabilitation. Data glove sensors are strategically placed over specific finger joints to detect movement of the wearers’ hand. The construction of the sensors used in a data glove, the number of sensors used, and their positioning on each finger joint are influenced by the intended use case. Although most glove sensors provide reasonably stable linear output, this stability is influenced externally by the physical structure of the data glove sensors, as well as the wearer’s hand size relative to the data glove, and the elastic nature of materials used in its construction. Data gloves typically require a complex calibration method before use. Calibration may not be possible when wearers have disabled hands or limited joint flexibility, and so limits those who can use a data glove within a clinical context. This paper examines and describes a unique approach to calibration and angular calculation using a neural network that improves data glove repeatability and accuracy measurements without the requirement for data glove calibration. Results demonstrate an overall improvement in data glove measurements. This is particularly relevant when the data glove is used with those who have limited joint mobility and cannot physically complete data glove calibration.

Wearable multifunctional soft sensor and contactless 3D scanner using supersonically sprayed silver nanowires, carbon nanotubes, zinc oxide, and PEDOT:PSS

The multifunctional soft sensor developed here is capable of simultaneously sensing six stimuli, including pressure, bending strain, temperature, proximity, UV light, and humidity, with high accuracy and without interference among the respective built-in components. The sensor is fabricated via a facile, scalable, and cost-effective supersonic cold-spraying method using silver nanowires (AgNWs), carbon nanotubes (CNTs), zinc oxide (ZnO), and conducting polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). A mask and laser cutter are used in conjunction with the supersonic cold-spraying method to produce miniaturized multifunctional sensors that can be readily installed on various substrates; for example, the production of gloves capable of multifunctional sensing. In particular, the proximity sensor of the multifunctional glove sensor can produce a three-dimensional (3D) image of a scanned object, showing high potential for use in military, medical, and industrial applications.

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

Sign language is the predominant mode of communication for the Deaf community. For the millions of people who suffer from hearing loss around the world, interaction with people who have the ability to hear and do not suffer from hearing impairment or loss is considered as complicated. In line with this issue, technology is perceived as a crucial factor in being an enabler of providing solutions to enhance the quality of life of the Deaf and people with hearing impairment by increasing accessibility. This research aims to review and analyze articles related to sign language recognition based on the sensor- based glove system, in order to identify academic motivations, challenges, and recommendations related to this field. The search for the relevant review materials and articles was performed on four major databases ranging from 2017 to 2022: Science Direct, Web of Science, IEEE Xplore, and Scopus. The articles were chosen based on our inclusion and exclusion criteria. The literature findings of this paper indicate the dataset size to be open issues and challenges for hand gesture recognition. Furthermore, the majority of research on sign language recognition based on data glove was performed on static, single hand, and isolated gestures. Moreover, recognition accuracy typically achieved results higher than 90%. However, most experiments were carried out with a limited number of gestures. Overall, it is hoped that this study will serve as a roadmap for future research and raise awareness among researchers in the field of sign language recognition.

HaptWarp: Implementation of Bend, Twist, and Press Manipulations for Intuitive Interaction in between the Virtual and Physical World

In virtual reality applications, such as games and training, the use of two-handed controllers to interact with virtual objects is usually supported. To reproduce the interactive sensation of holding objects of various shapes and behaviors with both hands, previous researchers have used mechanical connections or set various peripheral brakes between controllers to simulate physical changes. However, these external devices are hard to quickly adapt to for the simulation of dynamic objects, nor can they be removed to support free manipulations. This research introduces Deformation Response virtual reality Glove, which is a pair of sensor gloves. There is no physical link and users can stretch, bend, or twist flexible materials and display physical deformations on virtual objects, allowing users to perceive the difference between haptic sensation and physical sensation simply by using their hands.

A Preliminary Study on Ergonomic Contribution to the Engineering Design Approach of a Wheel Loader Control Lever System

Studies show that heavy machinery operators are exposed to risk factors of musculoskeletal diseases. However, there has yet to be a study investigating the grip analysis of heavy machinery control levers. This preliminary study aims to investigate the grip analysis of a system that emulates the push–pull operations, handle shapes, and resistance of wheel loader control lever systems. The system was designed, analysed, and optimised using Autodesk Inventor 2019 before fabrication and testing. It underwent usability testing for estimated and perceived grip force analysis (ergonomics analysis). The tests measured estimated force using a sensor glove, and perceived force using the Borg CR10 scale. The data were analysed using regression and paired t-tests. The findings suggested that pulling and high resistance factors required higher estimated force (339.50 N) and perceived force (5.625) than pushing and low resistance factors in manoeuvring the system (p < 0.05). The cylindrical handle required more estimated force (339.50 N) but less perceived force (4.5) than the spherical handle due to ergonomic design considerations (p < 0.05). Although there were inaccuracies in force measurement methods, the perceived method was still effective for data collection, since it is challenging to measure grip force in a real situation with heavy machinery. While this study was only a simulation, it provided researchers with ideas that may solve problems in the manipulation of heavy machinery control levers.

Extension of interval-valued Pythagorean FDOSM for evaluating and benchmarking real-time SLRSs based on multidimensional criteria of hand gesture recognition and sensor glove perspectives

Several DataGlove wearable electronic devices based on real-time Sign Language Recognition Systems (SLRSs) have been developed recently to assist the deaf and dumb community in translating hand gestures to their spoken language equivalents. Multidimensional evaluation and benchmarking of these systems are critical for determining the most desirable approach for meeting all essential requirements. However, this process is considered a Multicriteria Decision-Making (MCDM) problem due to the presence of several issues, including multiple evaluation criteria, criteria importance and criteria confliction. Hence, the MCDM approach is required to solve these issues. In this study, a new extension of the Fuzzy Decision by Opinion Score Method (FDOSM) for evaluating and benchmarking SLRSs is developed under an Interval-Valued Pythagorean Fuzzy Set (IVPFS) named IVP-FDOSM. Fundamentally, the methodology includes two phases. In the first phase, a decision matrix is formulated on the basis of identified ‘multidimensional criteria of hand gesture recognition and sensor glove perspectives’ and ‘real-time SLRSs’. The second phase introduces the development of IVP-FDOSM in two stages. The decision matrix is transformed into an opinion matrix in the first stage (data transformation unit). Meanwhile, in the second stage (data-processing unit), the opinion matrix is converted into fuzzy opinion decision matrices with the assistance of three experts by transforming the opinion matrix’s linguistic terms to Interval-Value Pythagorean Fuzzy Numbers (IVPFNs). Results indicate the following: (1) individual benchmarking results of real-time SLRS showed high variation (90%) based on the preference of each Decision Maker (DM), with only 10% of preferences being identical. (2) The results of group benchmarking reveal that the 10th real-time SLRS was the optimal one and the 6th was the worst. In addition, the rates of ranking match between the group benchmarking and each DM (first DM, second DM and third DM) were 23%, 37% and 43%, respectively. (3) For the results evaluation, the statistical test-based objective assessment shows that the first group received the lowest mean value (2.80333), while the third group received the highest mean (3.8). These values indicate that the group benchmarked systems resulting from IVP-FDOSM are undergoing a systematic ranking. Furthermore, the comparative analysis reveals that IVP-FDOSM is superior to IVP-TOPSIS and IVP-AHP in terms of ranking and weighting.

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove.

The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.

A Fabric-Based Integrated Sensor Glove System Recognizing Hand Gesture

Abstract The research on wearable glove sensor system has been increasing over recent years because of the need for portability and comfort. This study presents a fabric-based integrated sensor glove system with five sensing zones. Five sensors are knitted by silver-plated nylon yarn and embedded into glove directly using intarsia technology. Various parameters including sensor elasticity, sizes of embedded sensor as well as glove are discussed, respectively. Further, selected or chosen integrated sensor glove is manufactured and tested for recognizing gestures. Results show that elasticity affects effective sensing range of knitted sensors, size has significant influence on sensors’ sensitivity, and appropriate glove size helps avoiding nonlinear sensing phenomenon. Finally, the glove system, by extracting feature data, can distinguish Chinese number gestures very well and has also the potential to recognize more hand gestures in the future.

A stretchable glove sensor toward rapid monitoring of trifluralin: A new platform for the on-site recognition of herbicides based on wearable flexible sensor technology using lab-on-glove.

In this study, a flexible glove-based electrochemical sensor as a wearable point-of-use screening tool has been fabricated for defense and food security applications. To design the wearable glove-based sensor, we drew conductive patterns on the fingers of a rubber glove via gold@silver-modified graphene quantum dots (Au@Ag core-shell/graphene quantum dots [GQDs]) nano-ink with optimal thickness. Then, this platform is combined with a portable electrochemical analyzer for on-site detection of trifluralin pesticide in the range of 10nM to 1 mM with the low limit of quantification (LLOQ) of 10nM. The high efficiency and distinction of the trifluralin at specified concentrations in real leaf and apple samples were performed by simply touching with the glove and in spikes solution by immersing of fingertips. With their high sensitivity, selectivity, rapid, and easy operation pesticide analysis, these glove-embedded sensors can also be engaged in on-site monitor of other chemical threats and can be expanded to water and environmental samples.

Design and Implementation of Upper Prosthetic Controlled remotely by Flexible Sensor Glove

The structuring and the employment for a wireless prosthetic hand adjusted by a flex sensor has been suggested. The prosthetic hand has been constructed for emulating the individual arm motion utilizing a hand mitt. The hand mitt subsists of 5 flexible sensors concerning overseeing the finger motions. The animator’s design utilized to prosthetic arm has servo-motors. The fingers motions are adjusted utilizing wires which operate similarly to the tissue for an individual hand. The prosthetic hand dominates from a faraway position utilizing using remotely unit. Prosthetic hand-design equipped with many sensors; these sensors make the prosthetic hand more intelligent. The first sensor is the force sensor resistance. The second sensor is an infrared temperature sensor. This hand contains many medical applications that can be modified to become wearable for people with missing upper limbs, and the possibility of being used by doctors to perform surgeries remotely within the specified range or much further by connecting it to the internet system in addition to industrial and military applications, which is practical and low-cost. Practically tested to imitate human hand freedom movements and holding with high accuracy.

Scalable Fabrication of Kevlar/Ti3C2Tx MXene Intelligent Wearable Fabrics with Multiple Sensory Capabilities.

Fiber-based wearable electronics are highly desirable for wearable devices that are expected to be lightweight, easily prepared, durable, flexible, washable, and conformable. However, developing fiber-based fabric electronics to simulate human perceptual systems or even transcend the sensory capabilities of natural creatures is still a pivotal challenge. Herein, we present a Kevlar/MXene (KM) intelligent wearable fabric with multiple sensory capabilities using an ingenious strategy of continuous wet-spinning. The KM fibers can be washed, knitted, sewed, and fabricated into smart KM fabric sensory systems. An intelligent KM sensory mask is prepared to monitor human breathing in time to detect respiratory problems with high accuracy and portability. It provides an important reference for judging diseases and achieving remote diagnosis. Additionally, a smart temperature-responsive sensory glove is developed to help people make proper behavioral prejudgments and prevent potential injuries by sensing surrounding hazards beforehand. Moreover, this sensory system allows soft robotics to make a rough identification about the basic properties of unknown liquid molecules. Overall, by the virtue of the ultrafast responsiveness (90 ms), resilience (110 ms), and ultrasensitive capability in pressure responding, this KM sensory system offers a gentle approach for wireless detection in information encryption, transmission, and preservation by touching the sensory system with variable pressing time on the basis of the International Morse code principles, establishing a competitive and promising candidate for next generation wearable flexible fabric electronics.

Measurements comparison of finger joint angles in hand postures between an sEMG armband and a sensory glove

This study compares the simultaneous measurements of finger joint angles obtained with a myoelectric armband (Myo), composed of eight surface electromyography (sEMG) sensors mounted on an elastic support, and a data glove, equipped with ten flex sensor on metacarpal and proximal finger joints.The flexion angles of all finger joints in four hand postures, that is open hand, closed hand and grasping two 3D printed molds of different size, were measured with a manual goniometer, and used to create, for each finger joint, a linear model from the measurement of the corresponding flex sensor in an electronic glove, as well as a regression model from the simultaneous measurements of 8 sEMG sensors of the Myo armband. The regression models were extracted testing different algorithms from the Matlab Regression Learner Toolbox. The performance of the models of the two wearable devices were evaluated and compared, applying a standard test, taken from literature on sensory gloves to evaluate the repeatability, reproducibility and reliability of finger joint measurements. These results were also compared with those reported by published works that followed the same standard test, using data gloves based on different sensing technologies. This work aims to demonstrate that the sEMG armbands can be applied to register the static postures of each finger joint with almost the same accuracy of sensory gloves.

Smart System with Artificial Intelligence for Sensory Gloves.

This paper presents a new sensory system based on advanced algorithms and machine learning techniques that provides sensory gloves with the ability to ensure real-time connection of all connectors in the cabling of a cockpit module. Besides a microphone, the sensory glove also includes a gyroscope and three accelerometers that provide valuable information to allow the selection of the appropriate signal time windows recorded by the microphone of the glove. These signal time windows are subsequently analyzed by a convolutional neural network, which indicates whether the connection of the components has been made correctly or not. The development of the system, its implementation in a production industry environment and the results obtained are analyzed.

Restoration of Upper Limb Motor Function After Stroke

Since the task of eliminating the consequences of a stroke remains unsolved, research on the use of robotic simulators equipped with feedback to restore upper limb motor functions is of particular relevance. Aim of the study was to conduct an experimental evaluation of the effectiveness of the use of the Anika sensory glove with biological feedback for the restoration of upper extremity motor function in the early period of ischemic stroke. Materials and methods. We analyzed 108 medical histories and selected 28 patients with a single history of stroke and moderate cognitive disorders, which were divided into 2 groups - control and experimental with 14 individuals each. Patients of the control group engaged in therapeutic physical culture according to the traditional methodology adopted in neurological practice and aimed at restoring muscle strength, passive and active movements of the upper extremity. Trainings using the sensory glove Anika with biological feedback were included in the therapeutic physical culture training program for the experimental group. Results. The inclusion of special exercises on the Anika simulator with biological feedback in a comprehensive physical rehabilitation program increases the effectiveness of the recovery process, contributing to improving articular mobility, reducing spasticity, increasing muscle strength of the hand, reducing the level of personal and situational anxiety, normalizing psychoemotional state and increasing the functional mobility of stroke patients. Conclusion. The use of the sensory glove Anika with biological feedback in the early recovery period contributed to the expansion of the motor activity of patients, the development of self-care skills, psychological and social adaptation.

Training with Agency-Inspired Feedback from an Instrumented Glove to Improve Functional Grasp Performance.

Sensory feedback from wearables can be effective to learn better movement through enhanced information and engagement. Facilitating greater user cognition during movement practice is critical to accelerate gains in motor function during rehabilitation following brain or spinal cord trauma. This preliminary study presents an approach using an instrumented glove to leverage sense of agency, or perception of control, to provide training feedback for functional grasp. Seventeen able-bodied subjects underwent training and testing with a custom-built sensor glove prototype from our laboratory. The glove utilizes onboard force and flex sensors to provide inputs to an artificial neural network that predicts achievement of “secure” grasp. Onboard visual and audio feedback was provided during training with progressively shorter time delay to induce greater agency by intentional binding, or perceived compression in time between an action (grasp) and sensory consequence (feedback). After training, subjects demonstrated a significant reduction (p < 0.05) in movement pathlength and completion time for a functional task involving grasp-move-place of a small object. Future work will include a model-based algorithm to compute secure grasp, virtual reality immersion, and testing with clinical populations.

Design framework for a seamless smart glove using a digital knitting system

The wearable electronics integrated with textile-based devices is a promising strategy to meet the requirements of human comfort as well as electrical performances. This research presents a design and development framework for a seamless glove sensor system using digital knitting fabrication. Based on the performance requirements of glove sensors for controlling a prosthetic hand, desirable design components include electrical conductivity, comfort, formfit, electrical sensitivity, and customizable design. These attributes are determined and achieved by applying appropriate materials and fabrication technologies. In this study, a digital knitting CAD/CAM system is utilized to meet the desired performance criteria, and two prototypes of the seamless glove sensor systems are successfully developed for the detection of both human and robotic finger motions. This digital knitting system will provide considerable potential for customized design development as well as a sustainable production process. This structured, systematic approach could be adapted in the future development of wearable electronic textile systems.

Anthropomorphic Arm Control System with Remote Gesture Tracking

Abstract Human hand is a dexterous device created by nature. Some humanoid robot hands are developed with the aim of imitating the dexterity and adaptability of human hand to improve the grasping functions. Such manipulators will significantly facilitate and protect the work of people. In this paper we present a five-grip robotic arm that can be applied in rehabilitation systems, cosmonautics, industry, and aggressive inaccessible environments. The paper proposes a solution for tracking of human gestures and remote control of anthropomorphic arm using sensor glove with radio transmitter. A detailed description of the sensor glove is given. Some constructive solutions for an anthropomorphic robot arm are disclosed. Experimental results illustrate functionality of the device.

Sign Language Recognition System

In this paper an attempt has been made to design a sign language recognition system. An intelligent glove has been designed to automate the communication between a deaf-mute with others by converting sign language into speech or understandable language. The sensory gloves provide data of the human hand shape or movement and translate it to text and speech. It comprises hardware and software for translating sensor data. It is wearable devices that can be put on human hands and convert hand's gestures into signs letter by letter and send the data into the firebase for further processing. The glove is equipped with flex sensors and an inertial measurement unit to recognize the movement by monitoring the finger orientation and hand motion in three-dimensional spaces that senses a person's gestures in the form of finger bend and hand fist tilt. The Hall sensor has been used to process and collect data for training and model development. The three different machine learning algorithms, i.e., support vector machine, Naive Bayes, decision tree, have been used for analysis. It has been observed that the support vector machine has the highest accuracy, i.e., 90%. After Analyzing, the data has been sent to the speech converting function, and then audible results have been produced.