A system for improving fall detection performance using critical phase fall signal and a neural network

We present a system for improving fall detection performance using a short time min-max feature based on the specificsignatures of critical phase fall signal and a neural network as a classifier. Two subject groups were tested: Group A involvingfalls and activities by young subjects; Group B testing falls by young and activities by elderly subjects. The performance wasevaluated by comparing the short time min-max with a maximum peak feature using a feed-forward backpropagation networkwith two-fold cross validation. The results, obtained from 672 sequences, show that the proposed method offers a betterperformance for both subject groups. Group B’s performance is higher than Group A’s. The best performances are 98.2%sensitivity and 99.3% specificity for Group A, and 99.4% sensitivity and 100% specificity for Group B. The proposed systemuses one sensor for a body’s position, without a fixed threshold for 100% sensitivity or specificity and without additionalprocessing of posture after a fall

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Thailand Research Fun

Implementation of a real-time automatic onset time detection for surface electromyography measurement systems using NI myRIO

For using surface electromyography (sEMG) in various applications, the process consists of three parts: an onset time detection for detecting the first point of movement signals, a feature extraction for extracting the signal attribution, and a feature classification for classifying the sEMG signals. The first and the most significant part that influences the accuracy of other parts is the onset time detection, particularly for automatic systems. In this paper, an automatic and simple algorithm for the real-time onset time detection is presented. There are two main processes in the proposed algorithm; a smoothing process for reducing the noise of the measured sEMG signals and an automatic threshold calculation process for determining the onset time. The results from the algorithm analysis demonstrate the performance of the proposed algorithm to detect the sEMG onset time in various smoothing-threshold equations. Our findings reveal that using a simple square integral (SSI) as the smoothing-threshold equation with the given sEMG signals gives the best performance for the onset time detection. Additionally, our proposed algorithm is also implemented on a real hardware platform, namely NI myRIO. Using the real-time simulated sEMG data, the experimental results guarantee that the proposed algorithm can properly detect the onset time in the real-time manner

Capacitive biosensor for quantification of trace amounts of DNA

A flow injection capacitive biosensor system to detect trace amounts DNA has been developed based on the affinity binding between immobilized histone and DNA. Histones from calf thymus and shrimp were immobilized on gold electrodes covered with self-assembled monolayer (SAM) of thioctic acid. Each of these histones was used to detect DNA from calf thymus, shrimp and Escherichia coli. The studies indicated that histones can bind better with DNA from the same source and give higher sensitivity than the binding with DNA from different sources. Under optimum conditions, both histones from calf thymus and shrimp provided the same lower detection limit of 10(-5) ng l(-1) for DNA from different sources, i.e., calf thymus, shrimp and E. coli. The standard curve for the affinity reaction between calf thymus histone and DNA shows sigmoidal behavior and two linear ranges, 10(-5) to 10(-2) ng l(-1) and 10(-1) to 10(2) ng l(-1), could be obtained. The immobilized histones were stable and after regeneration good reproducibility of the signal could be obtained up to 43 times with a %R.S.D. of 3.1. When applied to analyze residual DNA in crude protein extracted from white shrimp recoveries were obtained between 80% and 116%. (c) 2009 Elsevier B.V. All rights reserved

Effects of Window Size and Contraction Types on the Stationarity of Biceps Brachii Muscle EMG Signals

International audienceIn order to analyze surface electromyography (EMG) signals, it is necessary to use techniques based on time (temporal) domain or frequency (spectral) domain. However, these techniques are based on the mathematical assumption of signal stationarity. On the other hand, EMG signal stationarity varies depending on analysis window size and contraction types. So in this paper, a suitable window size for an analysis of EMG during static and dynamic contractions was investigated using a stationarity test, the modified reverse arrangement test. More than 90% of the signals measured during static contraction can be considered as stationary signals for all window sizes. On average, a window size of 375 ms provides the most stationary information, 94.29% of EMG signals for static muscle contraction. For dynamic muscle contraction, the percentage of stationary signals decreased as the window size was increased. If the threshold of 80% stationarity was set to validate stationarity for each window size, a suitable window size should be 250 ms or lesser. For a real-time application that a size of analysis window plus processing time should be less than 300 ms, a window size of 250 ms is suggested for both contraction types