ABSTRACT
Preventive and accurate assessment of bladder voiding dysfunctions necessitates measuring the amount of liquid encapsulated within urinary bladder walls in a non-invasive and real-time manner. The real-time monitoring of urine levels helps patients with urological disorders such as Nocturnal Enuresis (NE) by preventing the occurrence of enuresis via a pre-void stage alerting system. Although some advances have been achieved toward developing a non-invasive approach for determining the amount of accumulated urine inside the bladder, there is still a lack of an easy-to-implement technique which is suitable to embed in a wearable pre-warning device. This study aims to develop a machine-learning empowered technique to quantify to what extent an individual's bladder is filled by observing the filling-voiding pattern of a patient over a training period. In this experiment, a pulse-echo sonar element is used to generate ultrasound pulses while the probe surface is positioned perpendicular to the bladder's position. From the reflected echoes, four features which show sufficient sensitiveness and therefore could be modulated noticeably by different levels of liquid encased in the bladder, are extracted. The extracted features are then fed into a novel intelligent decision support system- known as FECOC - which is based on hybridization of fuzzy inference systems (FIS) and error correcting output codes (ECOC). The proposed scheme tends to achieve better results when examined in real case studies.
Subject(s)
Nocturnal Enuresis , Wearable Electronic Devices , Humans , Machine Learning , Urinary Bladder/diagnostic imaging , UrinationABSTRACT
In the past few years, the technology of automated guided vehicles (AGVs) has notably advanced. In particular, in the context of factory and warehouse automation, different approaches have been presented for detecting and localizing pallets inside warehouses and shop-floor environments. In a related research paper Mohamed et al., 2018, we show that an AGVs can detect, localize, and track pallets using machine learning techniques based only on the data of an on-board 2D laser rangefinder. Such sensor is very common in industrial scenarios due to its simplicity and robustness, but it can only provide a limited amount of data. Therefore, it has been neglected in the past in favor of more complex solutions. In this paper, we release to the community the data we collected in Ref. Mohamed et al., 2018 for further research activities in the field of pallet localization and tracking. The dataset comprises a collection of 565 2D scans from real-world environments, which are divided into 340 samples where pallets are present, and 225 samples where they are not. The data have been manually labelled and are provided in different formats.
ABSTRACT
The aim of the present study was to explore the possible change in eye movement performance in a group of dyslexic and non-dyslexic children reading four lines of a text with different font sizes and spaces between the words. Fifteen dyslexic children from 7 to 12â¯years old and two groups of fifteen non-dyslexic children, respectively reading and chronological age-matched group, participated in this study. Horizontal eye movements from both eyes were recorded by a video-system (EyeBrain T2®) while the children were reading a text. Three different texts were used with different font sizes and spaces between words. Results showed that increasing font size and character spacing significantly reduced duration of the fixation and increased the number and amplitude of prosaccades in all groups of children tested. Interestingly, while reading texts in which the letters were larger and more spaced (Texts 2 and 3), the duration of fixations in dyslexic and in non-dyslexic children groups decreased, becoming similar to those reported in the non-dyslexic children group. We suggest that large letter spacing between words could be employed in schools to help dyslexic children in order to ameliorate their reading performance.
