While inertial measurement unit (IMU)-based systems have shown their potential in quantifying medically significant gait parameters, it remains to be determined whether they can provide accurate and reliable parameter...
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While inertial measurement unit (IMU)-based systems have shown their potential in quantifying medically significant gait parameters, it remains to be determined whether they can provide accurate and reliable parameters both across various walking conditions and in healthcare settings. Using an IMU-based system we previously developed, with one IMU module on each subject's heel, we quantify the gait parameters of 55 men and 46 women, all healthy and aged 40-65, in normal, dual-task, and fast walking conditions. We evaluate their intra-session reliability, and we establish a new reference database of such parameters showing good to excellent reliability. ICC(2,1) assesses relative reliability, while SEM% and MDC% assess absolute reliability. The reliability is excellent for all spatiotemporal gait parameters and the stride length (SL) symmetry ratio (ICC >= 0.90, SEM% <= 4.5%, MDC% <= 12.4%) across all conditions. It is good to excellent for the fast walking performance (FWP) indices of stride (Sr), stance (Sa), double-support (DS), and step (St) times;gait speed (GS);and the GS normalized to leg length (GSn1) and body height (GSn2) (ICC >= 0.91, |SEM%| <= 10.0%, |MDC%| <= 27.6%). Men have a higher swing time (Sw) and SL across all conditions. The following parameters are gender-independent: (1) Sa, DS, GSn1, GSn2;(2) the symmetry ratios of SL and GS, as well as Sa% and Sw% (representing Sa and Sw as percentages of Sr);and (3) the FWPs of Sr, Sa, Sw, DS, St, cadence, Sa% and Sw%. Our results provide reference values with new insights into gender FWP comparisons rarely reported in the literature. The advantages and reliability of our IMU-based system make it suitable in medical applications such as prosthetic evaluation, fall risk assessment, and rehabilitation.
The paper presents the development and real time application of an original closed-loop individual cylinder AFR control system, based on a spectral analysis of the lambda sensor signal. The observation that any type o...
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The paper presents the development and real time application of an original closed-loop individual cylinder AFR control system, based on a spectral analysis of the lambda sensor signal. The observation that any type of AFR disparity between the various cylinders is reflected in a specific harmonic content of the AFR signal spectrum, represents the starting point of the project. The results observed on a 4 cylinder Spark Ignition engine are encouraging, since in the investigated engine operating conditions the controller is able to reduce AFR inequality below 0.01 lambda. The paper also shows how the proposed controller can be applied to other engine configurations. (C) 2009 Elsevier Ltd. All rights reserved.
This paper presents an algorithm for online detection of arcing in low-voltage (230 V, 50 Hz) distribution systems. Electromagnetic radiation has been used as a feature for discrimination between arc and nonarc signal...
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This paper presents an algorithm for online detection of arcing in low-voltage (230 V, 50 Hz) distribution systems. Electromagnetic radiation has been used as a feature for discrimination between arc and nonarc signals. The output of the electromagnetic radiation sensor has been analyzed in the spectral domain using the log-spectral distance metric for the detection of arcing. For validation of this method, a testbench comprising an arc generation setup has been developed in the laboratory. The algorithm has been tested against arc and arc-mimicking normal signals using the testbench to validate its effectiveness. Further, the algorithm is implemented online using xPC target (an embedded platform) for studying the feasibility and accuracy of its online implementation. A system based on this method can continuously monitor the health of a distribution network and, thus, can be very helpful in safeguarding against fire hazards due to electric arcing.
Image-based swallowing assessment tools like videofluoroscopy and endoscopy allow experts manual investigation of a few individual swallows. However, these tools are expensive and can only be used by clinicians. Syste...
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Image-based swallowing assessment tools like videofluoroscopy and endoscopy allow experts manual investigation of a few individual swallows. However, these tools are expensive and can only be used by clinicians. Systems which utilize easily attachable, inexpensive and non-invasive sensors at the throat could be a real progress for diagnosis and therapy. This contribution investigates the use of a combined electromyography (EMG) and bioimpedance (BI) measurement at the throat to automatically detect swallowing events. The absolute value of the measured BI completely describes the swallowing process, i.e. the closure of the larynx. There is a typical reproducible drop in BI during a swallow. The muscle activity needed for the laryngeal movement during a swallow is measured using EMG. The presented algorithm involves a valley detection in order to perform a segmentation of the BI signal. Additionally, only BI valleys that coincide with EMG activity are selected for feature extraction. In the second part of the algorithm, extracted features of the BI and integrated EMG are fed into a support vector machine (SVM) which is able to separate BI valleys related to swallowing events from valleys which are not caused by swallowing. The detection algorithm has been tested on data from nine healthy subjects. The data set contained 1370 swallows of different bolus sizes and consistency and was effected by other movements and speech. The combined BI/EMG segmentation detected 99.3% of all swallowing events. The subsequently applied classifier showed a sensitivity of 96.1% and a specificity of 97.1% for the test data.
This paper proposes a data-driven method to minimize objective functions which can be measured in practice but are difficult to model. In the proposed method, the objective is learned directly from training data using...
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This paper proposes a data-driven method to minimize objective functions which can be measured in practice but are difficult to model. In the proposed method, the objective is learned directly from training data using random feature expansions. On the theoretical side, it is shown that the learned objective does not suffer from artificial local minima far away from the minima of the true objective if the random basis expansions are fit well enough in the uniform sense. The method is also tested on a real-life application, the tuning of an optical beamforming network. It is found that, in the presence of small model errors, the proposed method outperforms the classical approach of modeling from first principles and then estimating the model parameters.
Motivated by data-driven design of fault detection system, a recursive algorithm is developed for updating the parity relation within the framework of subspace identification methods (SIM). The presented recursive alg...
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Motivated by data-driven design of fault detection system, a recursive algorithm is developed for updating the parity relation within the framework of subspace identification methods (SIM). The presented recursive algorithm benefits from the signalprocessing methods for noise subspace tracking to reduce the computationally burdensome updating of singular value decomposition (SVD), which is a crucial step in SIM. Based on the recursive updated parity relation, the residual generator for the fault detection purpose is constructed and the issues of residual evaluation and threshold computation are discussed further. The adaptive process monitoring scheme that integrates the aforementioned issues, is proposed and tested on the laboratory three-tank-system.
This paper describes an event-based approach to the design and implementation of a supervisory system applied to a biotechnological process. Process variables are continually analysed in order to identify signal trend...
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This paper describes an event-based approach to the design and implementation of a supervisory system applied to a biotechnological process. Process variables are continually analysed in order to identify signal trends which conform significative events. An heuristic model of the process under supervision is characterised as a discrete-event system (DES). Plant events generate transitions on the DES model, thus reflecting the dynamics of the continuous system underneath. This DES model is used as an state observer of the plant. This approach is applied to a yeast production batch process monitoring.
This paper investigates the use of algorithms based on recursive least squares, polynomial filtering and the estimation of the instantaneous frequency of a periodic signal for studying the vibrations of rotating machi...
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This paper investigates the use of algorithms based on recursive least squares, polynomial filtering and the estimation of the instantaneous frequency of a periodic signal for studying the vibrations of rotating machinery by synchronous analysis. This allows digital sampling, at equal angular displacements, rather than at equal time intervals.
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