Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare.

Abstract: The patients of diabetes require to observe and control their glycemic profile through continuous glucose level monitoring. The blood glucose measurement is possible through invasive, minimally invasive and non-invasive methods. Invasive method is traditional method for instant glucose measurement where glucose is measured by taking blood samples from the body. However, the repeated finger pricking increases the risk of blood-related infections and trauma. Hence, the development of non-invasive real time device is essential for smart healthcare to manage glucose-insulin balance. The paper presents machine learning models for non-invasive glucose measurement. So, various machine learning algorithms including Logistic Regression, KNN, Gaussian Naive Bayes, Linear Regression, Multi-polynomial Regression, Neural Network, XGBoost, Decision Tree, Random Forest and Support Vector Machine are applied on two dataset which are PIDD (UCI repository) and iGLU dataset (iGLU device). The comparative analysis is carried out where accuracy, training time, recall, precision, f-1 score and AUC curve is measured for classification algorithms. For regression algorithms, measures like accuracy, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used for comparison purpose. Random forest with 84% accuracy and 68% recall, 76% precision and 72% f1-score for PIDD and Decision tree with 70% accuracy, 8% mean absolute error (MAE) and 8.5% root mean square error (RMSE) for iGLU dataset gives best results. Clark grid analysis has also been done where all the values fall under zone A which gives 100% accuracy and the device is useful for medication purpose. The proposed work has been also compared with similar methods and the proposed work has excellent results in terms of MAD, mARD, RMSE and AvgE. The device would be ideal as non-invasive solution for continuous glucose monitoring.

Advanced Energy Kernel-Based Feature Extraction Scheme for Improved EMG-PR-Based Prosthesis Control Against Force Variation.

The EMG signal is a widely focused, clinically viable, and reliable source for controlling bionics and prosthesis devices with the aid of machine-learning algorithms. The decisive step in the EMG pattern recognition (EMG-PR)-based control scheme is to extract the features with minimum neural information loss. This article proposes a novel feature extraction method based on advanced energy kernel-based features (AEKFs). The proposed method is evaluated on a scientific dataset which contains six types of upper limb motion with three different force variations. Furthermore, the EMG signal is acquired for eight upper limb gestures for the testing algorithm on the DSP processor. The efficiency of the proposed feature set has been investigated using classification accuracy (CA), Davies-Bouldin (DB) index-based separability measurement, and time complexity as performance metrics. Moreover, the proposed AEKF features, along with the LDA classifier, have been implemented on the DSP processor (ARM cortex M4) for real-time viability. Offline metrics comparison with the existing approaches prove that AEKF features exhibit lower time complexity along with a higher CA of 97.33%. The algorithm is tested on the DSP processor and CA is reported ≈ 92 %. MATLAB 2015a has been deployed in Intel Core i7, 3.40-GHz RAM for all offline analyses.

AutoDep: automatic depression detection using facial expressions based on linear binary pattern descriptor.

The psychological health of a person plays an important role in their daily life activities. The paper addresses depression issues with the machine learning model using facial expressions of the patient. Some research has already been done on visual based on depression detection methods, but those are illumination variant. The paper uses feature extraction using LBP (Local Binary Pattern) descriptor, which is illumination invariant. The Viola-Jones algorithm is used for face detection and SVM (support vector machine) is considered for classification along with the LBP descriptor to make a complete model for depression level detection. The proposed method captures frontal face from the videos of subjects and their facial features are extracted from each frame. Subsequently, the facial features are analyzed to detect depression levels with the post-processing model. The performance of the proposed system is evaluated using machine learning algorithms in MATLAB. For the real-time system design, it is necessary to test it on the hardware platform. The LBP descriptor has been implemented on FPGA using Xilinx VIVADO 16.4. The results of the proposed method show satisfactory performance and accuracy for depression detection comparison with similar previous work.

A Novel Time-Domain based Feature for EMG-PR Prosthetic and Rehabilitation Application.

EMG signal is widely accepted in human-machine interaction applications, such as prosthesis control and rehabilitation devices. The existing feature extraction methods struggle to separate a variety of EMG based activities. In the proposed work, a novel feature defined as PAP (peak average power) has been proposed. This feature has been validated for NinaPro database which includes isometric, isotonic, grasp and finger force based upper limb motions. Further, the comparison of classification accuracy has been performed with well-known time domain based features. Significant classification performance enhancement has been observed in terms of accuracy with LDA and QDA techniques. In this experiment, three datasets have been created and analysis was performed. Consequently, the results show an average enhancement of 17.60%, 7.52% and 15.37% using the proposed approach for LDA in dataset-1, dataset-2, and dataset-3 respectively. Similarly for the same datasets, when QDA is used the proposed approach overrules the existing techniques with the average enhanced performance of 13.52%, 12.72%, and 15.40%. All the analysis has been done using MATLAB 2015a in the i7 core.