A robotic system for steel bridge maintenance: Research challenges and system design

This paper presents the research on and development of a robotic system for stripping paint and rust from steel bridges, with the ultimate objective of preventing human exposure to hazardous and dangerous debris (containing rust, paint particles, lead and/or asbestos), relieving human workers from labor intensive tasks and reducing costs associated with bridge maintenance. The robot system design, the key research challenges and enabling technologies and system development are discussed in detail. Research results obtained so far and discussions on some key issues are also presented

Automatic Detection of Sleep Arousal Events from Polysomnographic Biosignals

Manual scoring of arousals is generally conducted by sleep experts in spite of being time-consuming and subjective. Our objective of this study was to develop an algorithm for automatic detection of sleep arousals without distinguishing between the types of arousal and sleep disorder groups. The processed and analysed data multiple overnight Polysomnography (PSG) recordings, consisting of 9 human subjects (6 male, 3 female), with age range of 34-69 and different conditions (4 patients with obstructive sleep apnoeas, 4 healthy and 1 patient with periodic limb movement disorder). PSG biosignals were processed to extract necessary features. Knearest neighbours (KNN) was used as the classifier and performance of algorithm were evaluated by Leave-One-Out Cross-Validation. The average sensitivity, specificity and accuracy of algorithm was 79%, 95.5% and 93%, respectively. These results demonstrate that our algorithm can automatically detect arousals with high accuracy. Furthermore, the algorithm is capable to be upgraded for classification of various types of arousals based upon their origin and characteristics

Classification of healthy subjects and insomniac patients based on automated sleep onset detection

This work aims to investigate new indexes quantitatively differentiate sleep insomnia patients from healthy subjects, in the context of sleep onset fluctuations. Our study included the use of existing PSG dataset, of 20 healthy subjects and 20 insomniac subjects. The differences between normal sleepers and insomniacs was investigated, in terms of dynamics and content of Sleep Onset (SO) process. An automated system was created to achieve this and it consists of six steps: 1) preprocessing of signals 2) feature extraction 3) classification 4) automatic scoring 5) sleep onset detection 6) identification of subject groups. The pre-processing step consisted of the removal of noise and movement artifacts from the signals. The feature extracting step consists of extracting time, frequency and non-linear features of Electroencephalogram (EEG) and Electromyogram (EMG) signals. In the third step, classification was done using ANN (Artificial Neural Networks) classifier. The fourth step consisted of scoring sleep stages (wake, S1, S2, S3 and REM) and produced a hypnogram. In the fifth step, we are detecting sleep onset from our automatic detected hypnogram and identified time of SO reference point and the combination of stages. In the final step we differentiated healthy subjects from insomniac patients based on the parameters calculated in the fifth step

Application of Random Forest Classifier for Automatic Sleep Spindle Detection

Sleep spindle detection using supervised learning methods such as Artificial Neural Networks and Support Vector Machines had been researched in the past. Supervised learning methods such as the above are prone to overfitting problems. In this research paper, we explore the detection of sleep spindles using the Random Forest classifier which is known to over fit data to a much lower extent when compared to other supervised classifiers. The classifier was developed using data from 3 subjects and it was tested on data from 12 subjects from the MASS database. A sensitivity of 71.2% and a specificity of 96.73% was achieved using the random forest classifier

Linear and non-linear interdependence of EEG and HRV frequency bands in human sleep

The characterisation of functional interdependencies of the autonomic nervous system (ANS) stands an evergrowing interest to unveil electroencephalographic (EEG) and Heart Rate Variability (HRV) interactions. This paper presents a biosignal processing approach as a supportive computational resource in the estimation of sleep dynamics. The application of linear, non-linear methods and statistical tests upon 10 overnight polysomnographic (PSG) recordings, allowed the computation of wavelet coherence and phase locking values, in order to identify discerning features amongst the clinical healthy subjects. Our findings showed that neuronal oscillations θ, α and σ interact with cardiac power bands at mid-to-high rank of coherence and phase locking, particularly during NREM sleep stages