In this research, an emotion recognition system is developed based on valence/arousal model using electroencephalography (EEG) signals. EEG signals are decomposed into the gamma, beta, alpha and theta frequency bands using discrete wavelet transform (DWT), and spectral features are extracted from each frequency band. Principle component analysis (PCA) is applied to the extracted features by preserving the same dimensionality, as a transform, to make the features mutually uncorrelated. Support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN) are used to classify emotional states. The cross- validated SVM with radial basis function (RBF) kernel using extracted features of 10 EEG channels, performs with 91.3% accuracy for arousal and 91.1% accuracy for valence, both in the beta frequency band. Our approach shows better performance compared to existing algorithms applied to the "DEAP" dataset.

The main objective of this study is the realization of advanced methods of automatic evaluation and continuous monitoring tools for the support to the medical evaluation of Parkinson’s disease (PD) and to the follow up of the patient also through the presentation and classification of data through a universally recognized clinical scale. We have developed an embedded wearable device integrating a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer, realizing a sensor with embedded data fusion and aggregation algorithms. Two different classification and detection algorithms are implemented to provide a comprehensive description and objective quantification of the two most investigated PD’s symptoms as tremor and freezing of gait (FoG). The system is usable in clinical and diagnostic settings showing its effectiveness above all in patients home monitoring through the upload in Cloud and the real-time processing of the acquired data. In the latter case, it allows the detection and objectification of PD symptoms manifested by the patient in everyday life, thereby providing the doctor with essential information for the dosage and effective personalization of drug therapy. For the system validation, a series of tests were carried out according to a defined experimental protocol on a sample of PD patients and healthy subjects with the presence of a video recording system and under the supervision of a neurologists team. The results obtained show a system’s accuracy of 97,7% for tremor classification and 99.7% for FoG events detection.

Tremor is one of the most important symptom in Parkinson's disease, which has been assessed clinically by neurologists as part of UPDRS scale. In this paper, we have implemented a supervised learning pattern recognition system to assess UPDRS of each Parkinson patient tremor to fill the absence of a reliable diagnosis and monitoring system for Parkinson patients. In our system a simple noninvasive method based on the recorded acceleration through the smartphone have been used for data acquisition. The results show high accuracy in the classifier block and neural network. A tight correlation between UPDRS scale and acceleration values reveals 91 percent accuracy by neural network with two hidden layers.

Movement monitoring in patients with Parkinson’s disease (PD) is critical for quantifying disease progression and assessing how a subject responds to medication administration over time. In this work, we propose a continuous monitoring system based on a single wearable sensor placed on the lower back and an algorithm for gait parameters evaluation. In order to preliminarily validate the proposed system, seven PD subjects took part in an experimental protocol in preparation for a larger randomized controlled study. We validated the feasibility of our algorithm in a constrained environment through a laboratory scenario. Successively, it was tested in an unsupervised environment, such as the home scenario, for a total of almost 12 h of daily living activity data. During all phases of the experimental protocol, videos were shot to document the tasks. The obtained results showed a good accuracy of the proposed algorithm. For all PD subjects in the laboratory scenario, the algorithm for step identification reached a percentage error low of 2%, 99.13% of sensitivity and 100% of specificity. In the home scenario the Bland–Altman plot showed a mean difference of −3.29 and −1 between the algorithm and the video recording for walking bout detection and steps identification, respectively.

Advanced Encryption Standard (AES) is a widely used Symmetric-key algorithm. A small characteristics improvement of AES circuits can significantly affect the system's characteristics. The Substitution box (S-box) is the only nonlinear transformation of the AES algorithm, which dominates the complexity of any hardware implementation of the AES. This paper proposes an area-power and energy-efficient design of the S-box based on multiple optimizations including simplifying the input-output true table rather than the direct implementation of the Galois Field inversions GF (28) equations. The gate-level and architecture optimization of the proposed S-box reduces the area of squaring, multiplication with λ, and multiplicative inverse over the GF (24) blocks, using minimum number of XOR gates. The power, area, and power-delay product (PDP) improvement in the selected technologies, 90nm, 45nm, and 32 nm are, respectively; 18-75%, 18-31%, and 30-60%, accounting for a respective improvement of AES, when compared with conventional S-box substituted AES block.