Patients with Parkinson's disease (PD) may have difficulties in speaking because of reduced coordination of the muscles that control breathing, phonation, articulation, and prosody. Symptoms that may occur are weakening of the volume of the voice, voice monotony, changes in the quality of the voice, the speed of speech, uncontrolled repetition of words, and difficult speech intelligibility. To date, evaluation of the speech intelligibility is performed based on the unified PD rating scale. Specifically, section 3.1 (eloquence) of the cited scale provides the specialist with some tips to evaluate the patient's speech ability. With the aim of evaluating the speech intelligibility by measuring the variation in parameters in an objective manner, we show that a speech-to-text (STT) system could help specialists to obtain an accurate and objective measure of speech, phrase, and word intelligibility in PD. STT systems are based on methodologies and technologies that enable the recognition and translation of spoken language into text by computers and computerized devices. We decided to base our study on Google STT conversion. We expand Voxtester, a software system for digital assessment of voice and speech changes in PD, in order to perform this study. No previous studies have been presented to address the mentioned challenges based on STT. The experiments here presented are related with detection/classification between pathological speech from patients with PD and regular speech from healthy control group. The results are very interesting and are an important step toward assessing the intelligibility of the speech of PD patients.

Medical practice for determining haemoglobin (Hb) concentration, which is important for anemic patients in need of blood transfusion, requires a blood sample. In this paper, we propose a non-invasive approach to Hb estimation based on the image analysis of a specific conjunctival region. Our goal is to develop a device that is not expensive and simple to use for assessing the anemic condition; this device could be used by the physician to decide whether to take a blood sample or even by a patient at home to decide whether to inform a physician; in this way, we can avoid having the patient go frequently to the laboratory to take a blood sample. This device also allows us to rapidly screen for Anemia in a large number of persons, for example, groups of girls who are at the beginning of the menses. Here, we detail the prototype of our device and the methodology for extracting key information from the color values of the acquired image. Tests conducted on 113 anemic and healthy persons show a strong correlation between the real Hb value obtained through blood sampling and the value estimated by our device. A k-nearest neighbor classification algorithm for assessing the (non)anemic condition yielded good results and helps doctors avoid a significant number of blood tests.

Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient to meet the body's physiological needs. It affects billions of people worldwide. An early diagnosis of this disease could prevent the advancement of other disorders. Traditional methods used to detect anemia consist of venipuncture, which requires a patient to frequently undergo laboratory tests. Therefore, anemia diagnosis using noninvasive and cost-effective methods is an open challenge. The pallor of the fingertips, palms, nail beds, and eye conjunctiva can be observed to establish whether a patient suffers from anemia. This article addresses the above challenges by presenting a novel intelligent system, based on machine learning, that supports the automated diagnosis of anemia. This system is innovative from different points of view. Specifically, it has been trained on a dataset that contains eye conjunctiva photos of Indian and Italian patients. This dataset, which was created using a very strict experimental set, is now made available to the Scientific Community. Moreover, compared to previous systems in the literature, the proposed system uses a low-cost device, which makes it suitable for widespread use. The performance of the learning algorithms utilizing two different areas of the mucous membrane of the eye is discussed. In particular, the RUSBoost algorithm, when appropriately trained on palpebral conjunctiva images, shows good performance in classifying anemic and nonanemic patients. The results are very robust, even when considering different ethnicities.

A technique for number comparison in the residue number system is presented, and its theoretical validity is proved. The proposed solution is based on using a diagonal function to obtain a magnitude order of the numbers. In a first approach the function is computed using a suitable extra modulus. In the final implementation of the technique the extra modulus has been inserted in the set of moduli of the residue system, avoiding redundancy. The technique is compared with other approaches.