A Preliminary Investigation into the Design of an Implantable Optical Blood Glucose Sensor

Abstract A preliminary investigation into the design of a near-infrared (NIR) optical bio-implant for accurate measurement of blood glucose level is reported. The use of an array of electrically pumped vertical-cavity surface-emitting laser (VCSEL) diodes at specific wavelengths for high-power narrow single-frequency emission leads to a high signal-to-noise ratio in the measured NIR absorption spectrum while maximizing the sensor's sensitivity to small absorption changes. The emission wavelengths lie within the combination and first-overtone spectral bands known to be dominated by glucose absorption information. A Quantum well infrared (QWI) photodiode transducer senses the received optical power after passing through the blood sample, followed by an artificial neural network (ANN) for the measurement of glucose in a whole blood matrix. For an independent test set made with fresh bovine blood, the optimal ANN topology for processing the two selected spectral bands yielded a standard error of prediction of 0.42 mM (i.e., 7.56 mg/dl) over the glucose level range of 4−20 mM. The empirical results obtained with a prototype mounted on PCB for blood glucose monitoring are closely correlated with the absorption spectra collected on a Vertex 70 Bruker spectrometer

Pattern recognition based on HD-sEMG spatial features extraction for an efficient proportional control of a robotic arm.

To enable an efficient alternative control of an assistive robotic arm using electromyographic (EMG) signals, the control method must simultaneously provide both the direction and the velocity. However, the contraction variations of the forearm muscles, used to proportionally control the device’s velocity using a regression method, can disturb the accuracy of the classification used to estimate its direction at the same time. In this paper, the original set of spatial features takes advantage of the 2D structure of an 8 × 8 high-density surface EMG (HD-sEMG) sensor to perform a high accuracy classification while improving the robustness to the contraction variations. Based on the HD-sEMG sensor, different muscular activity images are extracted by applying different spatial filters. In order to characterize their distribution specific to each movement, instead of the EMG signals’ amplitudes, these muscular images are divided in sub-images upon which the proposed spatial features, such as the centers of the gravity coordinates and the percentages of influence, are computed. These features permits to achieve average accuracies of 97% and 96.7% to detect respectively 16 forearm movements performed by a healthy subject with prior experience with the control approach and 10 movements by ten inexperienced healthy subjects. Compared with the time-domain features, the proposed method exhibits significant higher accuracies in presence of muscular contraction variations, requires less training data and is more robust against the time of use. Furthermore, two fine real-time tasks illustrate the potential of the proposed approach to efficiently control a robotic arm

Using a new hybrid models for speech and medical pattern classification

International audienc

Comparison the performance of hybrid HMM/MLP and RBF/LVQ ANN models: Application for Speech and Medical Pattern Classification

International audienc