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Abstract
Peripheral arterial disease (PAD) causes blockage of the arteries, altering the blood flow to the lower limbs. This blockage can cause the individual with PAD to feel severe pain in the lower limbs. The main contribution of this research is the discovery of a solution that allows the automatic detection of the onset of claudication based on data analysis from patients' smartphones. For the data-collection procedure, 40 patients were asked to walk with a smartphone on a thirty-meter path, back and forth, for six minutes. Each patient conducted the test twice on two different days. Several machine learning models were compared to detect the onset of claudication on two different datasets. The results suggest that we can identify the onset of claudication using inertial sensors with a best case accuracy of 92.25% for the Extreme Gradient Boosting model.

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A fast calculation method was used to obtain the spectral optical properties of human normal and pathological (chromophobe renal cell carcinoma) kidney tissues. Using total transmittance, total reflectance and collimated transmittance spectra acquired from ex vivo kidney samples, the spectral optical properties of both tissues, namely the absorption, the scattering and the reduced scattering coefficients, as well as the scattering anisotropy, dispersion and light penetration depth, were calculated between 200 and 1000 nm. Analysis of the mean absorption coefficient spectra of the kidney tissues showed that both contain melanin and lipofuscin, and that 83 % of the melanin in the normal kidney converts into lipofuscin in the pathological kidney.

Abstract
Listening to internal body sounds, or auscultation, is one of the most popular diagnostic techniques in medicine. In addition to being simple, non-invasive, and low-cost, the information it offers, in real time, is essential for clinical decision-making. This process, usually done by a doctor in the presence of the patient, currently presents three challenges: procedure duration, participants' safety, and the patient's privacy. In this article we tackle these by proposing a new autonomous robotic auscultation system. With the patient prepared for the examination, a 3D computer vision sub-system is able to identify the auscultation points and translate them into spatial coordinates. The robotic arm is then responsible for taking the stethoscope surface into contact with the patient's skin surface at the various auscultation points. The proposed solution was evaluated to perform a simulated pulmonary auscultation in six patients (with distinct height, weight, and skin color). The obtained results showed that the vision subsystem was able to correctly identify 100% of the auscultation points, with uncontrolled lighting conditions, and the positioning subsystem was able to accurately position the gripper on the corresponding positions on the human body. Patients reported no discomfort during auscultation using the described automated procedure.

Abstract
In the context of biometrics, matching confidence refers to the confidence that a given matching decision is correct. Since many biometric systems operate in critical decision-making processes, such as in forensics investigations, accurately and reliably stating the matching confidence becomes of high importance. Previous works on biometric confidence estimation can well differentiate between high and low confidence, but lack interpretability. Therefore, they do not provide accurate probabilistic estimates of the correctness of a decision. In this work, we propose a probabilistic interpretable comparison (PIC) score that accurately reflects the probability that the score originates from samples of the same identity. We prove that the proposed approach provides optimal matching confidence. Contrary to other approaches, it can also optimally combine multiple samples in a joint PIC score which further increases the recognition and confidence estimation performance. In the experiments, the proposed PIC approach is compared against all biometric confidence estimation methods available on four publicly available databases and five state-of-the-art face recognition systems. The results demonstrate that PIC has a significantly more accurate probabilistic interpretation than similar approaches and is highly effective for multi-biometric recognition. The code is publicly-available1. © 2023 IEEE.