An FBG-Based Sensing Glove to Measure Dynamic Finger Flexure With an Angular Resolution of 0.1$^{\circ }$ up to Speeds of 80$\mathrm{^{\circ }\text{/s}}$

Wearable Hand Module and Real-Time Tracking Algorithms for Measuring Finger Joint Angles of Different Hand Sizes with High Accuracy Using FBG Strain Sensor

“…Accurate measurement of hand motions is useful for various applications such as rehabilitation, virtual reality (VR) and augmented reality (AR) technology, and robotics [1][2][3][4]. For example, after a stroke, the damaged functions of the brain can be improved through continuous and repetitive finger rehabilitation [5,6]. The hand serves as a conduit for direct interaction between objects in a VR/AR world and users in the real world [7].…”

“…Unlike previous studies that focused on accurately measuring finger joint angles, recent studies have focused on developing a dexterous hand module with a small and repeatable measurement error for various users [6,22,24]. Wise et al used optical fibers to fabricate finger angle tracking gloves with three different sizes so that users can select the right size themselves [25].…”

“…Although they used stretchable sensors, the sensor positions in the hand module were still fixed, so reducing the measurement angle error would be difficult when applied to various hand sizes. Jha et al used a fiber Bragg grating (FBG) sensor to measure the angles of two different hand sizes; they achieved a very small angle error of 0.13 • with a mechanical setup and 0.67 • with a human hand with very small maximum standard deviations of 0.30 and 0.67 • , respectively [6]. However, they did not measure the joint angles of the thumb and little finger in real time.…”

Algoritmo para la estimación del ángulo anteroposterior de una muleta sensorizada

“…El uso de sistemas de captura de movimiento proporciona una captura integral del cuerpo con gran precisión, aunque su puesta en marcha es costosa y el rango de visión es limitado [5]. Porúltimo, el uso de sensores vestibles es mínimamente invasivo y no está limitado en rango, aunque la calibración de los mismos, la unión al paciente y el post-procesamiento presentan retos a tener en cuenta [6].…”

“…El uso de sensores de medida inerciales (IMUs) es habitual en estos dispositivos. Sin embargo,éstos presentan un problema de deriva que puede provocar errores importantes al procesar la señal [6,7]. Por ello, en este trabajo se propone un algoritmo que fusiona los datos que ofrece un giróscopo, un inclinómetro y un sensor de fuerza, con el fin de extraer una estimación del movimiento anteroposterior de la muleta, y con ello caracterizar el movimiento del paciente.…”