BackgroundAnalysis of gait features provides important information during the treatment of neurological disorders, including Parkinson’s disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space.Methods The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson’s disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data.ResultsThe main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson’s disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications.ConclusionsDiscussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.
Neurophysiological experiments support the hypothesis of the presence of critical dynamics of brain activity. This is also manifested by power law of electroencephalography (EEG) power spectra, which can be described by the relation 1/f(alpha). This dependence is a result of internal interactions between parts of the brain and is probably required for optimal processing of information. In Alzheimer's disease, changes in the functional organization of the brain occur, which may be manifested by changes in the alpha coefficient. We compared the average values of alpha for 19 electrodes in the resting EEG record in 110 patients with moderate to severe Alzheimer's disease (Mini-Mental State Examination [MMSE] score = 10-19) with 110 healthy controls. Statistically, the most significant differences are present in the prefrontal areas. In addition to the prefrontal and frontal areas, the largest separation value in the evaluation of receiver operating characteristic (ROC) curves was recorded in the temporal area. The coefficient alpha has few false-positive results in the optimal operating point of the ROC curve, and is thereby highly specific for Alzheimer's disease.
An advanced statistical analysis of patients’ faces after specific surgical procedures that temporarily negatively affect the patient’s mimetic muscles is presented. For effective planning of rehabilitation, which typically lasts several months, it is crucial to correctly evaluate the improvement of the mimetic muscle function. The current way of describing the development of rehabilitation depends on the subjective opinion and expertise of the clinician and is not very precise concerning when the most common classification (House–Brackmann scale) is used. Our system is based on a stereovision Kinect camera and an advanced mathematical approach that objectively quantifies the mimetic muscle function independently of the clinician’s opinion. To effectively deal with the complexity of the 3D camera input data and uncertainty of the evaluation process, we designed a three-stage data-analytic procedure combining the calculation of indicators determined by clinicians with advanced statistical methods including functional data analysis and ordinal (multiple) logistic regression. We worked with a dataset of 93 distinct patients and 122 sets of measurements. In comparison to the classification with the House–Brackmann scale the developed system is able to automatically monitor reinnervation of mimetic muscles giving us opportunity to discriminate even small improvements during the course of rehabilitation.
The total yield of ergosterol produced by the fermentation of the yeast Saccharomyces cerevisiae depends on the final amount of yeast biomass and the ergosterol content in the cells. At the same time ergosterol purity-defined as percentage of ergosterol in the total sterols in the yeast-is equally important for efficient downstream processing. This study investigated the development of both the ergosterol content and ergosterol purity in different physiological (metabolic) states of the microorganism S. cerevisiae with the aim of reaching maximal ergosterol productivity. To expose the yeast culture to different physiological states during fermentation an on-line inference of the current physiological state of the culture was used. The results achieved made it possible to design a new production strategy, which consists of two preferable metabolic states, oxidative-fermentative growth on glucose followed by oxidative growth on glucose and ethanol simultaneously. Experimental application of this strategy achieved a value of the total efficiency of ergosterol production (defined as product of ergosterol yield coefficient and volumetric productivity), 103.84 × 10 g L h , more than three times higher than with standard baker's yeast fed-batch cultivations, which attained in average 32.14 × 10 g L h . At the same time the final content of ergosterol in dry biomass was 2.43%, with a purity 86%. These results make the product obtained by the proposed control strategy suitable for effective down-stream processing. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:838-848, 2017.
During the years 2001 and 2002 we conducted hydrochemical monitoring of intensively managed pond to evaluate the impact of high pig slurry doses on eutrophication. Pig slurry application was carried out in colder period of the year (February–April) via tube system with sludge pump from nearby piggery. Our results showed that pig slurry application do not permanently affect the water quality of Jarohněvický pond. When the correct application is made slurry is effectively utilized by biomass for the growth, which prevents surface and underground waters to be polluted as in the case of incorrect application on agricultural land.Critical point of this technology in terms of water management is the way of pig slurry application and the exact dose. Even extremely high slurry doses (16.1 kg . m−2 in 2001 and 15.6 kg . m−2 in 2002) used in Jarohněvický pond did not negatively affected pond ecosystem. Only higher amount of organisms that increased natural fish production was recorded. It is necessary to implement this ameliorative intervention in colder period of the year considering higher hazard of variations in decisive hydrochemical parameters at higher water temperature.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.