An optical fiber specklegram chemical sensor based on a concatenated multimode-no-core-multimode structure is reported. The 30 mm no-core section is immersed in the analyzed liquid so the number of excited modes is modulated by the surrounding refractive index value, affecting the interference of the several propagating modes and consequently the output specklegram, whose changes are quantified by the zero mean normalized cross-correlation coefficient. The sensor was evaluated on the measurement of ethanol-water solutions, resulting in 18.7 RIU-1 sensitivity (5×10-4 resolution) for the 1.33 to 1.36 refractive index interval, besides 0.07°C-1 temperature sensitivity within the 26 to 32°C range. Moreover, the system was also tested on liquid level measurements, yielding 0.05 mm-1 sensitivity and 0.2 mm resolution. The results are comparable to previously reported multimodal interferometers based on more complicated interrogation schemes, indicating that the specklegram sensor is a feasible alternative for the sensitive and straightforward measurement of liquid samples.

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.

Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer.

Optical myography stands as one of many techniques to assess hand postures. As a combination of computer vision and muscular activity analysis, it differs from conventional gesture recognition techniques, such as instrumented gloves or optical tracking, by not relying on the existence of a healthy hand, so it is able to detect the hand motion as well as the motion intent. In this aspect, optical myography is like well-established myographic approaches, such as surface electromyography or force myography, but it is simpler, more comfortable, and inexpensive. This recent technology is hereby evaluated upon the construction of a feasible and low-cost sensor that monitors both the front and the back of the forearm. The results are organized into two sections, the first validates the sensor and the second evaluates its performance as a predictor of 8 static postures, including the thumb and the fingers motion. In the end, the sensor proved to be comparable to more mature techniques with an F-score of ~92.2% and ~71.5% for front and back-side analysis, respectively.