Spatio-temporal parameters of ataxia gait dataset obtained with the Kinect.

Ataxic syndromes include several rare, inherited and acquired conditions. One of the main issues is the absence of specific, and sensitive automatic evaluation tools and digital outcome measures to obtain a continuous monitoring of subjects' motor ability. Gait evaluation was performed by Kinect v2 in a cohort of young participant affected by ataxia syndrome. The dataset is composed of the spatio-temporal parameters calculated by the skeleton acquired by the Kinect sensor, by the diagnosis of each participant, and by the total score of the clinical scale SARA. These parameters have been previously validated and corrected as requested by the Bland-Altman test.

Validation of low-cost system for gait assessment in children with ataxia.

BACKGROUND: Ataxic syndromes include several rare, inherited and acquired conditions. One of the main issues is the absence of specific, and sensitive automatic evaluation tools and digital outcome measures to obtain a continuous monitoring of subjects' motor ability. OBJECTIVES: This study aims to test the usability of the Kinect system for assessing ataxia severity, exploring the potentiality of clustering algorithms and validating this system with a standard motion capture system. METHODS: Gait evaluation was performed by standardized gait analysis and by Kinect v2 during the same day in a cohort of young patient (mean age of 13.8±7.2). We analyzed the gait spatio-temporal parameters and we looked at the differences between the two systems through correlation and agreement tests. As well, we tested for possible correlations with the SARA scale as well. Finally, standard classification algorithm and principal components analysis were used to discern disease severity and groups. RESULTS: We found biases and linear relationships between all the parameters. Significant correlations emerged between the SARA and the Speed, the Stride Length and the Step Length. PCA results, highlighting that a machine learning approach combined with Kinect-based evaluation shows great potential to automatically assess disease severity and diagnosis. CONCLUSIONS: The spatio-temporal parameters measured by Kinect cannot be used interchangeably with those parameters acquired with standard motion capture system in clinical practice but can still provide fundamental information. Specifically, these results might bring to the development of a novel system to perform easy and quick evaluation of gait in young patients with ataxia, useful for patients stratification in terms of clinical severity and diagnosis.

An efficient unsupervised fetal QRS complex detection from abdominal maternal ECG.

Non-invasive fetal heart rate is of great relevance in clinical practice to monitor fetal health state during pregnancy. To date, however, despite significant advances in the field of electrocardiography, the analysis of abdominal fetal ECG is considered a challenging problem for biomedical and signal processing communities. This is mainly due to the low signal-to-noise ratio of fetal ECG and difficulties in cancellation of maternal QRS complexes, motion and electromyographic artefacts. In this paper we present an efficient unsupervised algorithm for fetal QRS complex detection from abdominal multichannel signal recordings combining ICA and maternal ECG cancelling, which outperforms each single method. The signal is first pre-processed to remove impulsive artefacts, baseline wandering and power line interference. The following steps are then applied: maternal ECG extraction through independent component analysis (ICA); maternal QRS detection; maternal ECG cancelling through weighted singular value decomposition; enhancing of fetal ECG through ICA and fetal QRS detection. We participated in the Physionet/Computing in Cardiology Challenge 2013, obtaining the top official scores of the challenge (among 53 teams of participants) of event 1 and event 2 concerning fetal heart rate and fetal interbeat intervals estimation section. The developed algorithms are released as open-source on the Physionet website.

A personal monitoring architecture to detect muscular fatigue in elderly.

Muscle fatigue and exercise intolerance are common and frequent symptoms complained by patients with neuromuscular disease. Muscle fatigue would occur when the intended physical activity can no longer be continued or is perceived as involving excessive effort and discomfort. Except for several rare myopathies with specific metabolic derangements leading to exercise-induced muscle fatigue, most studies fail to identify precise pathogenic mechanism of fatigue in this population of patients. On the other hand, apart from canonical examples of neuromuscular diseases, a number of conditions in which muscle apparatus can be involved is known to occur with high prevalence among certain people categories, such as elderly or people undergoing immobilization. In these cases exercise intolerance and muscle fatigue can be severely incapacitating in common daily activities. An objective and smart, unobtrusive techniques, able to objectively measure fatigue phenomenon, would be useful in monitoring muscle function in both NMD patients and patients with secondary skeletal muscle involvement. In this study, we report a novel, non-invasive assistive architecture for the elderly to assess muscle fatigue by biomedical sensors (surface electromyography) using wireless platform during exercise in an ergonomic platform.