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For humans, the hands represent the most important medium of interaction with the environment. In fact, their sophisticated kinematic structure and perception abilities allow to hold objects, perform gestures and perceive contact forces. The development of tools capable of detecting this information has become increasingly important in recent years given the implications in various fields ranging from medicine to robotics.

Wearable devices such as data gloves have experienced tremendous growth over the past two decades. It is vital to develop flexible sensors with fast response, high sensitivity and high stability for intelligent data gloves. Therefore, a tractable low-cost flexible data glove with self-calibration function based on a space-division multiplexed flexible optical fiber sensor is proposed. A simple, stable and economical method was used to fabricate flexible silicone rubber fiber for a stretchable double-layered coaxial cylinder. The test results show that the fiber is not sensitive to the temperature range of (20~50 °C) and exhibits excellent flexibility and high stability under tensile, bending and torsional deformation. In addition, the signal detection part of the data glove enables compact and efficient real-time information acquisition and processing. Combined with a self-calibration function that can improve the accuracy of data acquisition, the data glove can be self-adaptive according to different hand sizes and bending habits. In a gesture capture test, it can accurately recognize and capture each gesture, and guide the manipulator to make the same action. The low-cost, fast-responding and structurally robust data glove has potential applications in areas such as sign language recognition, telemedicine and human–robot interaction.

For technical or medical applications, the knowledge of the exact kinematics of the human hand is key to utilizing its capability of handling and manipulating objects and communicating with other humans or machines. The optimal relationship between the number of measurement parameters, measurement accuracy, as well as complexity, usability and cost of the measuring systems is hard to find. Biomechanic assumptions, the concepts of a biomechatronic system and the mechatronic design process, as well as commercially available components, are used to develop a sensorized glove. The proposed wearable introduced in this paper can measure 14 of 15 angular values of a simplified hand model. Additionally, five contact pressure values at the fingertips and inertial data of the whole hand with six degrees of freedom are gathered. Due to the modular design and a hand size examination based on anthropometric parameters, the concept of the wearable is applicable to a large variety of hand sizes and adaptable to different use cases. Validations show a combined root-mean-square error of 0.99° to 2.38° for the measurement of all joint angles on one finger, surpassing the human perception threshold and the current state-of-the-art in science and technology for comparable systems.

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.

Many diseases, such as stroke, arthritis, and spinal cord injury, can cause severe hand impairment. Treatment options for these patients are limited by expensive hand rehabilitation devices and dull treatment procedures. In this study, we present an inexpensive soft robotic glove for hand rehabilitation in virtual reality (VR). Fifteen inertial measurement units are placed on the glove for finger motion tracking, and a motor—tendon actuation system is mounted onto the arm and exerts forces on fingertips via finger-anchoring points, providing force feedback to fingers so that the users can feel the force of a virtual object. A static threshold correction and complementary filter are used to calculate the finger attitude angles, hence computing the postures of five fingers simultaneously. Both static and dynamic tests are performed to validate the accuracy of the finger-motion-tracking algorithm. A field-oriented-control-based angular closed-loop torque control algorithm is adopted to control the force applied to the fingers. It is found that each motor can provide a maximum force of 3.14 N within the tested current limit. Finally, we present an application of the haptic glove in a Unity-based VR interface to provide the operator with haptic feedback while squeezing a soft virtual ball.

In this paper, the interactive application of data gloves based on emotion recognition and judgment system is investigated. A system of emotion recognition and judgment is established based on the set of optimal features of physiological signals, and then a data glove with multi-channel data transmission based on the recognition of hand posture and emotion is constructed. Finally, the system of virtual hand control and a manipulator driven by emotion is built. Five subjects were selected for the test of the above systems. The test results show that the virtual hand and manipulator can be simultaneously controlled by the data glove. In the case that the subjects do not make any hand gesture change, the system can directly control the gesture of the virtual hand by reading the physiological signal of the subject, at which point the gesture control and emotion control can be carried out at the same time. In the test of the manipulator driven by emotion, only the results driven by two emotional trends achieve the desired purpose.

The Action Research Arm Test (ARAT) can provide subjective results due to the difficulty assessing abnormal patterns in stroke patients. The aim of this study was to identify joint impairments and compensatory grasping strategies in stroke patients with left (LH) and right (RH) hemiparesis. An experimental study was carried out with 12 patients six months after a stroke (three women and nine men, mean age: 65.2 ± 9.3 years), and 25 healthy subjects (14 women and 11 men, mean age: 40.2 ± 18.1 years. The subjects were evaluated during the performance of the ARAT using a data glove. Stroke patients with LH and RH showed significantly lower flexion angles in the MCP joints of the Index and Middle fingers than the Control group. However, RH patients showed larger flexion angles in the proximal interphalangeal (PIP) joints of the Index, Middle, Ring, and Little fingers. In contrast, LH patients showed larger flexion angles in the PIP joints of the Middle and Little fingers. Therefore, the results showed that RH and LH patients used compensatory strategies involving increased flexion at the PIP joints for decreased flexion in the MCP joints. The integration of a data glove during the performance of the ARAT allows the detection of finger joint impairments in stroke patients that are not visible from ARAT scores. Therefore, the results presented are of clinical relevance.

The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.

Tracking detailed hand motion is a fundamental research topic in the area of human-computer interaction (HCI) and has been widely studied for decades. Existing solutions with single-model inputs either require tedious calibration, are expensive or lack sufficient robustness and accuracy due to occlusions. In this study, we present a real-time system to reconstruct the exact hand motion by iteratively fitting a triangular mesh model to the absolute measurement of hand from a depth camera under the robust restriction of a simple data glove. We redefine and simplify the function of the data glove to lighten its limitations, i.e., tedious calibration, cumbersome equipment, and hampering movement and keep our system lightweight. For accurate hand tracking, we introduce a new set of degrees of freedom (DoFs), a shape adjustment term for personalizing the triangular mesh model, and an adaptive collision term to prevent self-intersection. For efficiency, we extract a strong pose-space prior to the data glove to narrow the pose searching space. We also present a simplified approach for computing tracking correspondences without the loss of accuracy to reduce computation cost. Quantitative experiments show the comparable or increased accuracy of our system over the state-of-the-art with about 40% improvement in robustness. Besides, our system runs independent of Graphic Processing Unit (GPU) and reaches 40 frames per second (FPS) at about 25% Central Processing Unit (CPU) usage.

The methods used to quantify finger range of motion significantly influence how hand disability is reported. To date, the accuracy of sensors being utilized in data gloves from the literature has been ascertained yet need further analysis. This paper presents an inertial measurement unit sensor-based data glove for finger joint measurement developed for collecting a range of motion data of distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joints of an index finger. In this study, three inertial measurement sensors, MPU-6050 and two flexible bend sensors which are capable to detect angle displacement were attached to the distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joint points on the glove. The data taken from inertial measurement unit sensors and flexible bend sensors were acquired using Arduino and MATLAB software interface. The data obtained were compared with the reference data measured from goniometer to allow for accurate comparative measurement. The percentage of error resulted from MPU-6050 sensor unit were ranged from 0.81 % to 5.41 % were very low which indicates high accuracy when compared with the measurements obtained using goniometer. On the other hand, flexible bend sensor shows low accuracy (11.11 % to 19.35 % error). In conclusion, the inertial measurement unit sensor-based data glove using MPU-6050 sensors can be a reliable solution for tracking the progress of finger rehabilitation exercises. In order to motivate patients to adhere to the therapy exercises, interactive rehabilitation game will be developed in the future incorporating MPU-6050 sensors on all five fingers.

Devices that track the human body movement are heavily used in numerous and various fields like medicine, automation and entertainment. The work proposed is focused on the design of a modular device able track the flection of human hand phalanxes. The overall system composed by two parts: a computer program interface and a modular wearable system applied to the finger whose motion is to be monitored. The wearable device is equipped with an Inertial Motion Unit (IMU) with the purpose to detect the first phalanx orientation and a stretch sensor applied between the first and the second phalanx to recognize the flection angle. The configuration is completed with a microcontroller unit (ATmega328P) and a Bluetooth Low Power Module (RN4871) to ensure a reliable and easy to implement communication channel. We conduct two main set of tests to verify the global functionalities. In the first set the device is used to track the full flexion of a single finger while in the second we test the device capability to recognize different grabbed objects starting from the data retrieved from two fingers. The preliminary results open the possibility of a future development focused on a modular device composed by five elements, one for each hand finger and able to detect complex gesture like pinch, spread or tap.

Body motion tracking technologies are widespread in military, medical and sport fields. The work proposes a modular wireless wearable system able to detect hand fingers motion. Such system is composed by a readout unit that analyses the data and by a wearable measuring device directly applied on the tracked finger. This device is equipped with an Inertial Motion Unit (IMU) used to track the first phalanx motion and orientation and with a stretch sensor to monitor the bending angle between the first and second phalanxes. We carried out an experimental study, which is divided in two main parts. In the first one, the transducer performance was evaluated, whereas, in the second part, we tested the capability of the overall system to recognize simple finger movements and different objects grabbed. The preliminary results pave the possibility of developing a modular device, one for each hand finger, able to recognize the grabbed object shape or detect complex gestures.

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 normal human being sees, listens, and reacts to his/her surroundings. There are some individuals who do not have this important blessing. Such individuals, mainly deaf and dumb, depend on communication via sign language to interact with others. However, communication with ordinary individuals is a major concern for them since not everyone can comprehend their sign language. Furthermore, this will cause a problem for the deaf and dumb communities to interact with others, particularly when they attempt to involve with educational, social and work environments. In this research, the objectives are to develop a sign language translation system in order to assist the hearing or speech impaired people to communicate with normal people, and also to test the accuracy of the system in interpreting the sign language. As a first step, the best method in gesture recognition was chosen after reviewing previous researches. The configuration of the data glove includes 10 tilt sensors to capture the finger flexion, an accelerometer for recognizing the motion of the hand, a microcontroller and Bluetooth module to send the interpreted information to a mobile phone. Firstly the performance of the tilt sensor was tested. Then after assembling all connections, the accuracy of the data glove in translating some selected alphabets, numbers and words from Malaysian Sign Language is performed. The result for the first experiment shows that tilt sensor need to be tilted more than 85 degree to successfully change the digital state. For the accuracy of 4 individuals who tested this device, total average accuracy for translating alphabets is 95%, numbers is 93.33% and gestures is 78.33%. The average accuracy of data glove for translating all type of gestures is 89%. This fusion of tilt sensors and accelerometer could be improved in the future by adding more training and test data as well as underlying frameworks such as Hidden Markov Model.

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

For over 30 years, the development and control of anthropomorphic robotic hands has been a highly popular sub-discipline in robotics research. Because the human hand is an extremely sophisticated system, both in its mechanical and sensory abilities, engineers have been fascinated with replicating these abilities in artificial systems. The applications of robotic hands typically fall under the categories of standalone testbed platforms, mostly to conduct research on manipulation, prosthetics, and robotic end effectors for larger systems. The teleoperation of robotic hands is another application with significant potential, where users can control a manipulator in real time to accomplish diverse tasks. In controlling a device that seeks to emulate the function of the human hand, it is intuitive to choose a human-machine interface (HMI) that will allow for the most intuitive control. Data gloves are the ideal HMI for this need, allowing a robotic hand to accurately mimic the human operator's natural movements. In this paper we present a combined review on the critical design aspects of data gloves and robotic hands. In literature, many of the proposed designs covering both these topical areas, robotic hand and data gloves, are cost prohibitive which limits their implementation for intended tasks. After reviewing the literature, new designs of robotic hand and data glove technology are also presented, introducing low cost solutions that can serve as accessible platforms for researchers, students, and engineers to further the development of teleoperative applications. Master of Science

Learning how to speak in order to communicate with others is part of growing up. Like a normal person, deaf and mutes also need to learn how to connect to the world they live in. For this purpose, an Electronic Glove or E-Glovewas developed as a teaching aid for the hearing impaired particularly children. E-Glove makes use ofthe American Sign Language (ASL) asthe basis for recognizing hand gestures. It was designed using flex sensors and an accelerometer to detect the degree of bend made by the fingers as well asa movement of the hand. E-Glove transmits the data received from the sensors wirelessly to a computer and then displays the letter or basic word that correspondsto a gesture made by the individual wearing it. E-Glove provides a simple, accurate, reliable, cheap, speedy gesture recognition and user-friendlyteaching aid for the instructors that are teaching sign language to the deaf and mute community.

Purpose state: The aim of this preliminary study was to test a data glove, CyberTouch ,combined with a virtual reality (VR) environment, for using in therapeutic training of reaching movements after spinal cord injury (SCI). Method: Nine patients with thoracic SCI were selected to perform a pilot study by comparing two treatments: patients in the intervention group (IG)conducted a VR training based on the use of a data glove, CyberTouch for 2 weeks, while patients in the control group (CG) only underwent the traditional rehabilitation. Furthermore, two functional parameters were implemented in order to assess patient?s performance of the sessions: normalized trajectory lengths and repeatability. Results: Although no statistical significance was found, the data glove group seemed to obtain clinical changes in the muscle balance (MB) and functional parameters, and in the dexterity, coordination and fine grip tests. Moreover, every patient showed variations in at least one of the functional parameters, either along Y-axis trajectory or Z-axis trajectory. Conclusions: This study might be a step forward for the investigation of new uses of motion capture systems in neurorehabilitation, making it possible to train activities of daily living (ADLs) in motivational environments while measuring objectively the patient?s functional evolution.

The use of human hand motions as an effective way to interact with computers/robots, robot manipulation learning and prosthetic hand control is being researched in-depth. This~paper proposes a novel and effective multiple sensor based hand motion capture and recognition system. Ten common predefined object grasp and manipulation tasks demonstrated by different subjects are recorded from both the human hand and object points of view. Three types of sensors, including~electromyography, data glove and FingerTPS are applied to simultaneously capture the EMG signals, the finger angle trajectories, and the contact force. Recognising different grasp and manipulation tasks based on the combined signals is investigated by using an adaptive directed acyclic graph algorithm, and results of comparative experiments show the proposed system with a higher recognition rate compared with individual sensing technology, as well as other algorithms. The proposed framework contains abundant information from multimodal human hand motions with the multiple sensor techniques, and it is potentially applicable to applications in prosthetic hand control and artificial systems performing autonomous dexterous manipulation.

Vast improvements in robotics and wireless communication have made teleoperated robots significantly more prevalent in industry, defense, and research. To help bridge the gap for these robots in the workplace, there has been a tremendous increase in research toward the development of biomimetic robotic hands that can simulate human operators. However, current methods of control are limited in scope and do not adequately represent human muscle memory and skills. The vision of this thesis is to provide a pathway for overcoming these limitations and open an opportunity for development and implementation of a cost effective methodology towards controlling a robotic hand. The first chapter describes the experiments conducted using Flexpoint bend sensors in conjunction with a simple voltage divider to generate a cost-effective data glove that is significantly less expensive than the commercially available alternatives. The data glove was able to provide sensitivity of less than 0.1 degrees. The second chapter describes the molding process for embedding pressure sensors in silicone skin and data acquisition from them to control the robotic hand. The third chapter describes a method for parsing and observing the information from the data glove and translating the relevant control variables to the robotic hand. The fourth chapter focuses on the feasibility of the brain computer interfaces (BCI) and successfully demonstrates the implementation of a simple brain computer interface in controlling a robotic hand. Master of Science

This paper presents a real-time hand gesture recognizer based on a Learning Vector Quantization (LVQ) classifier. The recognizer is formed by two modules. The first module, mainly composed of a data glove, performs the feature extraction. The second module, the classifier, is performed by means of LVQ. The recognizer, tested on a dataset of 3900 hand gestures, performed by people of different gender and physique, has shown very high recognition rate.

In this paper, a statistical analysis had been carried out on the measured joint angles of the human hand's fingers while performing some common tasks. By exploiting the correlation existing on some couples of joints, we can reduce the number of myoelectric sensors necessary to drive a (virtual or real) hand prosthesis, while still maintaining an acceptable hand's Degree of Freedom (DoF). In order to do such kind of analysis we measured the common hand tasks by means of our HITEG data glove we developed. The results of this analysis shows that the number of sensors can be halved, extrapolating the value of remaining sensors by means of linear regression, with an error which for most applications can result acceptable. This method will allow the subject, who has to drive an hand prosthesis, to perform all common hand actions and gestures with only few, not severe, limitations.

This paper presents an intelligent system allowing handicapped aphasiacs to perform basic communication tasks. It has the following three key features: (1) A 6-sensor data glove measures the finger gestures of a patient in terms of the bending degrees of his fingers. (2) A finger language recognition subsystem recognizes language components from the finger gestures. It employs multiple regression analysis to automatically extract proper finger features so that the recognition model can be fast and correctly constructed by a radial basis function neural network. (3) A coordinate-indexed virtual keyboard allows the users to directly access the letters on the keyboard at a practical speed. The system serves as a viable tool for natural and affordable communication for handicapped aphasiacs through continuous finger language input.

Bend sensors fundamental characteristic is to furnish an electrical resistance value related to the angle they are bent. This feature can be successfully exploited to realize wearable systems capable to measure human static and dynamic postures. In particular some efforts have been made to determine finger joint movements of human hands and it has been demonstrated the feasibility of using the so called data glove system as a goniometric device. The repeatability of such system is quite good for general purposes but it is still not sufficient for specific applications (for instance in virtual surgery). So here we introduce a novel application method of bend sensors and demonstrate how it can be useful to improve the system repeatability.

The present paper reports the results of the development of a novel joint angular sensor conceived for integration in tendon-driven robotic hands and in data gloves used in virtual reality systems. The sensor is based on a couple LED/photodiode, mounted to two contiguous phalanges of a University of Bologna (UB) hand finger. When the joint between the considered phalanges flexes, the photocurrent measured by the photodetector changes with the angular displacement. An experimental model of the sensor is set up in order to select the optimal positioning of the components over the phalanges and an optical motion capture system is used to calibrate the sensor. The complete characterization of the sensor in terms of repeatability, linearity and noise presented in the paper together with its low cost confirm that the sensor can be effectively exploited both in feedback control loops for robotic systems as well as in data acquisition systems for virtual reality applications.

In this work we used the HiTEg data glove to measure the skill of a physician or physician student in the execution of a typical surgical task: the suture. The aim of this project is to develop a system that, analyzing the movements of the hand, could tell if they are correct. To collect a set of measurements, we asked 18 subjects to performing the same task wearing the sensory glove. Nine subjects were skilled surgeons and nine subjects were non-surgeons, every subject performed ten repetitions of the same task, for two sessions, yielding to a dataset of 36 instances. Acquired data has been processed and classified with a neural network. A feature selection has been done considering only the features that have less variance among the expert subjects. The cross-validation of the classifier shows an error of 5.6%.

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