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

Lin, Bor-Shing; Lee, I-Jung; Chiang, Pei-Ying; Huang, Shih-Yuan; Peng, Chih‐Wei

doi:10.1007/s40846-018-0434-6

Cited by 41 publications

(36 citation statements)

References 15 publications

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“…Previous researchers [ 1 , 3 , 15 ] have assessed data glove technologies, but the margin for error during sensor evaluation is substantial and only limited to static measurements. In addition, it is important that the collected data are trustworthy regarding wearable technologies to eliminate false data being accepted and good data being rejected.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, this had a huge disadvantage of only providing static and course angle verification. Lin et al [ 1 , 15 ] developed two self-made angle measurement devices, with the first device used to validate the static angle of an IMU. This device was limited to rough static movement, and the measuring device had to be observed at each angle whilst reading a basic mathematical protractor.…”

Section: Accuracy Repeatability and Resolutionmentioning

confidence: 99%

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Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion

Henderson¹,

Condell²,

Connolly

et al. 2021

Sensors

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Capturi ng hand motions for hand function evaluations is essential in the medical field. For many allied health professionals, measuring joint range of motion (ROM) is an important skill. While the universal goniometer (UG) is the most used clinical tool for measuring joint ROM, developments in current sensor technology are providing clinicians with more measurement possibilities than ever. For rehabilitation and manual dexterity evaluations, different data gloves have been developed. However, the reliability and validity of sensor technologies when used within a smart device remain somewhat unclear. This study proposes a novel electronically controlled sensor monitoring system (ECSMS) to obtain the static and dynamic parameters of various sensor technologies for both data gloves and individual sensor evaluation. Similarly, the ECSMS was designed to closely mimic a human finger joint, to have total control over the joint, and to have an exceptionally high precision. In addition, the ECSMS device can closely mimic the movements of the finger from hyperextension to a maximum ROM beyond any person’s finger joint. Due to the modular design, the ECSMS’s sensor monitoring board is independent and extensible to include various technologies for examination. Additionally, by putting these sensory devices through multiple tests, the system accurately measures the characteristics of any rotary/linear sensor in and out of a glove. Moreover, the ECSMS tracks the movement of all types of sensors with respect to the angle values of finger joints. In order to demonstrate the effectiveness of sensory devices, the ECSMS was first validated against a recognised secondary device with an accuracy and resolution of 0.1°. Once validated, the system simultaneously determines real angles alongside the hand monitoring device or sensor. Due to its unique design, the system is independent of the gloves/sensors that were tested and can be used as a gold standard to realise more medical equipment/applications in the future. Consequently, this design greatly enhances testing measures within research contact and even non-contact systems. In conclusion, the ECSMS will benefit in the design of data glove technologies in the future because it provides crucial evidence of sensor characteristics. Similarly, this design greatly enhances the stability and maintainability of sensor assessments by eliminating unwanted errors. These findings provide ample evidence for clinicians to support the use of sensory devices that can calculate joint motion in place of goniometers.

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Section: Discussionmentioning

confidence: 99%

Section: Accuracy Repeatability and Resolutionmentioning

confidence: 99%

Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion

Henderson¹,

Condell²,

Connolly

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Sensor gloves have been widely researched in applications such as robotics and VR, and they have gained increased significance in recent decades for medical applications [ 49 ]. This paper emphasizes the importance of research in sensor glove devices for medical and health care domains.…”

Section: Sensors Characteristics and Signal Processing And Outputmentioning

confidence: 99%

“…The small electronic devices provide nine DOF capturing nine distinct types of motion or orientation related data [ 48 ]. These devices, however, require complex programming, it can be costly, and require shielding from magnetic interferences [ 5 , 29 , 49 ]. The MPU-9250 contains a three-axis magnetometer that utilizes Hall sensor technology that is extremely sensitive.…”

Section: Sensors Characteristics and Signal Processing And Outputmentioning

confidence: 99%

“…Several researchers have conducted investigations into contact systems [ 22 , 25 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. However, current research seems to indicate that contact systems are more sought-after for monitoring hand functionality [ 1 , 5 , 9 , 29 , 48 , 49 ]. Several types of sensors have been used to measure the quantitative measurements of interest, with complex architectural and circuit designs.…”

Section: Introductionmentioning

confidence: 99%

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Review of Wearable Sensor-Based Health Monitoring Glove Devices for Rheumatoid Arthritis

Henderson¹,

Condell²,

Connolly

et al. 2021

Sensors

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Early detection of Rheumatoid Arthritis (RA) and other neurological conditions is vital for effective treatment. Existing methods of detecting RA rely on observation, questionnaires, and physical measurement, each with their own weaknesses. Pharmaceutical medications and procedures aim to reduce the debilitating effect, preventing the progression of the illness and bringing the condition into remission. There is still a great deal of ambiguity around patient diagnosis, as the difficulty of measurement has reduced the importance that joint stiffness plays as an RA identifier. The research areas of medical rehabilitation and clinical assessment indicate high impact applications for wearable sensing devices. As a result, the overall aim of this research is to review current sensor technologies that could be used to measure an individual’s RA severity. Other research teams within RA have previously developed objective measuring devices to assess the physical symptoms of hand steadiness through to joint stiffness. Unfamiliar physical effects of these sensory devices restricted their introduction into clinical practice. This paper provides an updated review among the sensor and glove types proposed in the literature to assist with the diagnosis and rehabilitation activities of RA. Consequently, the main goal of this paper is to review contact systems and to outline their potentialities and limitations. Considerable attention has been paid to gloved based devices as they have been extensively researched for medical practice in recent years. Such technologies are reviewed to determine whether they are suitable measuring tools.

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AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

Suo,

Liu,

Wang

et al. 2024

Advanced Science

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Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscle‐sensing wearable device, that in conjunction with machine learning, enables motion‐control‐based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16‐channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower‐limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle‐sensing‐based somatosensory interaction, using the proposed wearable device, for enabling the real‐time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography‐based methods for achieving accurate human motion capture, which can further broaden the applications of motion‐interactive wearable devices for the coming metaverse age.

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

Cited by 41 publications

References 15 publications

Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion

Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion

Review of Wearable Sensor-Based Health Monitoring Glove Devices for Rheumatoid Arthritis

AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

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