Gait asymmetry assessment through Eigen-Gait components on dissimilarity maps.

Motor impairments caused by neurological diseases have an important impact on gait, particularly on the coordination between left and right lower limbs. Deviation from normal gait is often measured to assess this impact on gross motor functions, and to monitor the progress of patients during rehabilitation. The concept of gait dissimilarity map is introduced to represent bilateral raw gait signals, while accounting for their respective spatiotemporal dynamics.

A novel gait quality measure for characterizing pathological gait based on Hidden Markov Models.

This study addresses the characterization of normal gait and pathological deviations caused by neurological diseases. We focus on the angular knee kinematics in the sagittal plane and we propose to exploit Hidden Markov Models to build a statistical model of normal gait. Such model provides a log-likelihood score that quantifies gait quality.

Self-Modulation of Gamma-Band Synchronization through EEG-Neurofeedback Training in the Elderly.

Background: Electroencephalography (EEG) stands as a pivotal non-invasive tool, capturing brain signals with millisecond precision and enabling real-time monitoring of individuals' mental states. Using appropriate biomarkers extracted from these EEG signals and presenting them back in a neurofeedback loop offers a unique avenue for promoting neural compensation mechanisms. This approach empowers individuals to skillfully modulate their brain activity.

Clinical Gait Analysis: Characterizing Normal Gait and Pathological Deviations Due to Neurological Diseases.

This study addresses the characterization of normal gait and pathological deviations induced by neurological diseases, considering knee angular kinematics in the sagittal plane. We propose an unsupervised approach based on Dynamic Time Warping (DTW) to identify different normal gait profiles (NGPs) corresponding to real cycles representing the overall behavior of healthy subjects, instead of considering an average reference, as done in the literature. The obtained NGPs are then used to measure the deviations of pathological gait cycles from normal gait with DTW.

On Extracting Digitized Spiral Dynamics' Representations: A Study on Transfer Learning for Early Alzheimer's Detection.

This work proposes a decision-aid tool for detecting Alzheimer's disease (AD) at an early stage, based on the Archimedes spiral, executed on a Wacom digitizer. Our work assesses the potential of the task as a dynamic gesture and defines the most pertinent methodology for exploiting transfer learning to compensate for sparse data. We embed directly in spiral trajectory images, kinematic time functions.

Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline.

This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer's disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity.

A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG.

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer's disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks.

Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures.

We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropy-based data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks.

Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework.

This study addresses the problem of Alzheimer's disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer's disease (AD) patients, and patients with other pathologies.

On Hunting Animals of the Biometric Menagerie for Online Signature.

Individuals behave differently regarding to biometric authentication systems. This fact was formalized in the literature by the concept of Biometric Menagerie, defining and labeling user groups with animal names in order to reflect their characteristics with respect to biometric systems. This concept was illustrated for face, fingerprint, iris, and speech modalities.