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.

Phase-Based Gait Prediction after Botulinum Toxin Treatment Using Deep Learning.

Gait disorders in neurological diseases are frequently associated with spasticity. Intramuscular injection of Botulinum Toxin Type A (BTX-A) can be used to treat spasticity. Providing optimal treatment with the highest possible benefit-risk ratio is a crucial consideration.

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.

Treatment Outcome Prediction Using Multi-Task Learning: Application to Botulinum Toxin in Gait Rehabilitation.

We propose a framework for optimizing personalized treatment outcomes for patients with neurological diseases. A typical consequence of such diseases is gait disorders, partially explained by command and muscle tone problems associated with spasticity. Intramuscular injection of botulinum toxin type A is a common treatment for spasticity.

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.

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.

Age-Related Evolution Patterns in Online Handwriting.

Characterizing age from handwriting (HW) has important applications, as it is key to distinguishing normal HW evolution with age from abnormal HW change, potentially triggered by neurodegenerative decline. We propose, in this work, an original approach for online HW style characterization based on a two-level clustering scheme. The first level generates writer-independent word clusters from raw spatial-dynamic HW information.

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.

The multiscenario multienvironment BioSecure Multimodal Database (BMDB).

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data.

A new phase-correlation-based iris matching for degraded images.

In this paper, we present a new phase-correlation-based iris matching approach in order to deal with degradations in iris images due to unconstrained acquisition procedures. Our matching system is a fusion of global and local Gabor phase-correlation schemes. The main originality of our local approach is that we do not only consider the correlation peak amplitudes but also their locations in different regions of the images.

On using the Viterbi path along with HMM likelihood information for online signature verification.

This paper describes a system using two complementary sorts of information issuing from a hidden Markov model (HMM) for online signature verification. At each point of the signature, 25 features are extracted. These features are normalized before training and testing in order to improve the performance of the system.