Murilo Ferreira Marques dos Santos

Flexible Optical Fiber Bending Transducer for Application in Glove-Based Sensors

The development of a low-cost and flexible optical fiber transducer for measurement of angular displacements is reported. The light intensity attenuation due to fiber microbending losses is correlated to the variations in flexing angle, yielding to a sensitivity of 1.80°. The device was also mounted in a fabric glove to the monitoring of flexion and abduction movements of index and thumb fingers. Once calibrated by a simple procedure, the glove-based system was capable to measure the angular positions with average errors <;5° and 7° for interphalangeal and metacarpophalangeal joints, respectively. Additionally, the repeatability analysis resulted in average range and standard deviations of 8.06° and 3.45°, respectively. The optical fiber sensor provides a low-cost alternative to the real-time monitoring of hand posture, and can be suitable for applications in human-robot and human-computer interactions.

Optical fiber specklegram sensor analysis by speckle pattern division

An optical fiber specklegram sensor interrogation method based on speckle pattern fragmentation is presented. The acquired specklegram images are divided in a square grid, creating sub-images that are further processed by a correlation technique, allowing the quantification of localized changes in the specklegrams. The methodology was tested on the assessment of linear displacements using a microbending transducer, by evaluating different grid sizes. For a 5×5 grid, a 2.53  mm-1 sensitivity over a 0.27 mm range was obtained, representing an extension of 237.5% in comparison to the standard interrogation technique. Therefore, the presented technique allows enhancing the sensor dynamic range without modifying the experimental setup.

Development of an optical fiber transducer applied to the measurement of finger movements

A flexible and low-cost optical fiber transducer based on light attenuation by microbending was designed for the measurement of angular displacements. The transducer was tested for predetermined rotations, presenting a higher sensitivity for angles >10° by spacing the periodicity of the deformers by 2 mm. In addition, the performance on the measurement of angles <10° was also enhanced by the specklegram analysis, yielding to a linear response. Furthermore, the glove-mounted sensor was applied on the detection of the proximal interphalangeal joint, by performing the calibration by artificial neural networks, resulting in calculated angle values compatible to the nominal ones.

Identification of hand postures by force myography using an optical fiber specklegram sensor

The identification of hand postures based on force myography (FMG) measurements using a fiber specklegram sensor is reported. The microbending transducers were attached to the user forearm in order to detect the radial forces due to hand movements, and the normalized intensity inner products of output specklegrams were computed with reference to calibration positions. The correlation between measured specklegrams and postures was carried out by artificial neural networks, resulting in an overall accuracy of 91.3% on the retrieval of hand configuration.

Development of a glove-based optical fiber sensor for applications in human-robot interaction

A glove-based optical fiber sensor for the measurement of finger movements aiming HRI applications was developed. The device presented good response on the detection of angular displacements of finger joints, being suitable for further utilization in teleoperation and gesture-based robot navigation.

Evaluation of Thumb-Operated Directional Pad Functionalities on a Glove-Based Optical Fiber Sensor

The implementation of directional pad functionalities on a glove-based optical fiber sensor by monitoring the thumb posture is reported. Multimode fiber bending transducers were attached to the glove to measure the flexion/extension and adduction/abduction movements of thumb joints. Then, the optical signals corresponding to each transducer were correlated to the relative finger position during the manipulation of a directional pad by using artificial neural networks. The estimation of thumb stationary location over the pad yielded a maximum absolute error of 4.3 mm, whereas the dynamic evaluation of finger trajectories resulted in errors lower than 5.5 mm. Furthermore, it was demonstrated that the glove sensor is able to identify the correct direction with 96.67% accuracy when applied as an eight-direction digital controller.

Optical Fiber Specklegram Sensor for Measurement of Force Myography Signals

The development of an optical fiber specklegram sensor for the assessment of force myography signals is reported. The device consists of microbending transducers attached to the user forearm by means of Velcro straps. The muscular stimuli generated in response to hand movements cause the fibers to be mechanically pressed by the deformer structures, resulting in light modulation. The optical signals are acquired and processed according to specklegram analysis, by computing the normalized intensity inner product of speckles for two reference postures. Finally, the average values are correlated to the fingers configurations by means of artificial neural networks. The system was evaluated for a set of 11 static gestures, making it possible to detect subtle variations in the spatial distribution of forces impressed by the forearm flexor and extensor muscles. Moreover, the technique was tested for different subjects, yielding an average accuracy of 89.9% on the estimation of fingers configurations, even with the utilization of a reduced number of transducers.

Development of an optical fiber FMG sensor for the assessment of hand movements and forces

The development of an optical fiber specklegram sensor for detection of force myography (FMG) signals produced by hand forces and movements is presented. The optomechanical transducers are attached to the user forearm, causing variations on speckle field intensities, which are processed and then correlated to particular hand postures. The results indicated the viability to identify changes on hand configurations and forces, in which the force retrieval can be implemented via artificial neural networks after a previous calibration. The fiber FMG sensor can be further applied on human-system interfaces, as well as integrated to glove-based sensors in order to provide a robust detection of hand movements and forces.

Optical fiber tactile sensor for user interfaces

The development of a tactile sensor based on optical fiber specklegram analysis is reported. The fibers are interposed by deformer plates and the variations on light intensity due to microbending effect are correlated to the normal displacements, yielding 1.9 mm-1 sensitivity. Moreover, the spatial information about applied loads over a 30 × 30 mm2 frame can be retrieved by analyzing the specklegrams variations for 3 fibers, making possible to design tactile interfaces for applications in user-system interaction.

Optical Classification of Quartz Lascas by Artificial Neural Networks

A gradation method based on quartz lascas (lumps) transparency level is proposed. The samples were irradiated by transmitting light, and the images histograms were processed by artificial neural networks. Additionally, the results were compared to conventional classification methods, including density and visual analysis. The network designed with backpropagation architecture using 4 hidden layers of 10 neurons yielded to a relative error <24% in relation to manual classification, indicating a good agreement to the miners criteria. Furthermore, the implementation of competitive learning with 5 neurons resulted in correct discrimination of samples regarding their optical characteristics with a completely non-subjective approach.