RESUMEN
Gait is a firsthand reflection of health condition. This belief has inspired recent research efforts to automate the analysis of pathological gait, in order to assist physicians in decision-making. However, most of these efforts rely on gait descriptions which are difficult to understand by humans, or on sensing technologies hardly available in ambulatory services. This paper proposes a number of semantic and normalized gait features computed from a single video acquired by a low-cost sensor. Far from being conventional spatio-temporal descriptors, features are aimed at quantifying gait impairment, such as gait asymmetry from several perspectives or falling risk. They were designed to be invariant to frame rate and image size, allowing cross-platform comparisons. Experiments were formulated in terms of two databases. A well-known general-purpose gait dataset is used to establish normal references for features, while a new database, introduced in this work, provides samples under eight different walking styles: one normal and seven impaired patterns. A number of statistical studies were carried out to prove the sensitivity of features at measuring the expected pathologies, providing enough evidence about their accuracy. Graphical Abstract Graphical abstract reflecting main contributions of the manuscript: at the top, a robust, semantic and easy-to-interpret feature set to describe impaired gait patterns; at the bottom, a new dataset consisting of video-recordings of a number of volunteers simulating different patterns of pathological gait, where features were statistically assessed.
Asunto(s)
Marcha/fisiología , Visión Ocular/fisiología , Bases de Datos como Asunto , Femenino , Humanos , Masculino , Reproducibilidad de los ResultadosRESUMEN
Different kinds of challenge can alter spontaneous ongoing electroencephalographic (EEG) rhythms in animal models, thus providing paradigms to evaluate treatment effects in drug discovery. The effects of challenges represented by pharmacological agents, hypoxia, sleep deprivation and transcranial magnetic stimulation (TMS) on EEG rhythms are here reviewed to build a knowledge platform for innovative translational models for drug discovery in Alzheimer's disease (AD). It has been reported that antagonists of cholinergic neurotransmission cause synchronisation of spontaneous ongoing EEG rhythms in terms of enhanced power of EEG low frequencies and decreased power of EEG high frequencies. Acetylcholinesterase inhibitors and serotonergic drugs may restore a normal pattern of EEG desynchronisation. Sleep deprivation and hypoxia challenges have also been reported to elicit abnormal synchronisation of spontaneous ongoing EEG rhythms in rodents. The feasibility and reproducibility of TMS have been demonstrated in rodents but information on a consistent modulation of EEG after TMS manipulation is very limited. Transgenic mice over-expressing human amyloid precursor protein complementary DNAs (cDNAs) harbouring the 'Swedish' mutation and PS-1 cDNAs harbouring the A264E mutation, which recapitulate some of the pathological features of AD, exhibit alterations of spontaneous ongoing EEG rhythms at several low and high frequencies. This does not appear, however, to be a consequence of beta-amyloid deposition in the brain. The present review provides a critical evaluation of changes of spontaneous ongoing EEG rhythms due to the experimental manipulations described above, in order to stimulate the promote more adherent models fitting dynamics in humans.