1. Introduction
Recently, with the development of virtual reality (VR) interaction, researchers are increasingly keen on flexible, stretchable and wearable sensor devices that can track complex human movements [1,2,3,4,5,6,7,8,9]. Since the human hand has more than 20 degrees of freedom, it has flexible functions in communication and operation [10] and can transmit a large number of information. It is one of the body’s most important organs for communicating with the outside world. Data gloves have become one of the most popular devices of human-computer interaction, widely used in medical, education, games and other fields [11,12,13,14,15].
Currently, some data glove devices based on point tracking and recognition technology [16,17], computer vision technology [18,19], FBG sensor technology [20,21] and inertial sensor technology [22,23] have been proposed. However, data gloves based on the above technologies have different disadvantages. In point tracking and recognition technology, optical, acoustic and electromagnetic marker points are mainly used. However, optical markers are easily obscured during hand movements. Acoustic and electromagnetic marker point technology also have certain limitations, such as susceptibility to electromagnetic interference and low resolution. Data gloves based on computer vision technology need to work in specific system operating environment and ambient light conditions. FBG sensors have high sensitivity and accuracy. However, the complexity of measurement system restricts its development in the field of data glove. The data glove based on inertial sensor has fast response in gesture capture. However, inertial sensors are not suitable for working for a long time due to the accumulated measurement error. In addition, to fully capture the movements and senses of the hand, data gloves based on the above technologies would need to have multiple sensors embedded in each finger. This fusion of sensors increases the complexity of the data glove system and reduces its portability and utility.
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In addition to the traditional technologies mentioned above, the application of elastic fiber optic sensors in data gloves has attracted extensive attention because of their advantages such as stretchable, small size, high sensitivity, fast response and anti-electromagnetic interference [24,25]. So far, elastic optical fiber sensors have been widely used in various practical applications due to the low-cost, scalable, simple and diverse production methods of elastic material fibers [26,27,28,29,30]. Among them, Leber et al. developed a thermo-plastic optical fiber sensor for detecting extreme deformations through the wavelength-dependent changes in light transmission [31]. The fabricated fibers were able to reversibly maintain strains of up to 300% while guiding light. Yang et al. reported a sensor based on dye-doped PDMS fiber [32], which has good durability, reliability and long-term stability. The tensile strain can be measured quantitatively according to the change of light absorption through dye-doped fiber. The sensor has a linear and repeatable response over a wide dynamic range of up to 100%. In 2019, Sheng et al. reported a graphene-supplemented PDMS fiber [33]. The fiber has excellent strain sensing performance, high sensitivity, tensile range up to 150%. In 2022, Gan et al. reported a stretchable optical sensor with strain decoupling capability [34]. The stretchable fiber is made of fluorescent nanoparticles and silicone-based elastomers, which can achieve efficient excitation light transmission and fluorescence collection. These studies have expanded the application of optical sensors such as elastic fiber sensors in the field of wearable devices. However, optical sources used in fiber optic sensors are often disturbed by factors such as current fluctuations, which can not be ignored. This is especially true for mobile devices with their own batteries. As shown in Figure 1a, the aging of the battery reduces the current to the optical source, which leads to the change of light intensity and brings errors to the measurement system. Therefore, for further meet the requirements of wearable devices for sensor stability and reliability, it’s crucial to take effective and easy to implement measures to reduce the system error caused by the unstable optical source. Furthermore, low-cost and tractable flexible sensors with fast response, high sensitivity and super-stability are very necessary for the construction of user-friendly intelligent wearable devices.
Hereby, a low-cost data glove based on a self-compensating elastic optical fiber sensor with self-calibration function is proposed for gesture capture. The tunable and stretchable elastic fiber was fabricated by a simple, economical and controllable method. Figure 1b shows that the elastic optical fiber has good flexibility and stability. It can not only maintain outstanding sensing properties at 10 °C to 50 °C, but also exhibit high stability after deformation such as stretching, bending and indentation. An additional communication-grade plastic fiber (attenuation less than 180 dB/km) is connected to the sensor as a reference signal (Figure 1c). The reference fiber can effectively reduce the error of the sensor system. The structure diagram of the data glove is shown in Figure 1d. The optical fibers are installed in the sensor in a U shape with a bending radius of 5 mm. Compared with the straight fiber, the response sensitivity of the U-shaped fiber to deformation is increased by about 7 times at most. In addition, the sensors are easy to install so that the data gloves can be customized for different hand shapes. To sum up, the production process of the data glove is simple, the sensor component is universal, and the low-cost glove enables real-time monitoring of finger movements(the actual price of each component is recorded in Table S1). This will facilitate the development of data gloves in areas such as motion monitoring, telemedicine and human-computer interaction.
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