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We investigate magnetic, angular rate, and gravity (MARG) sensor modules for deriving shoulder, elbow and lumbar joint angles of the human body. We use three tri-axial MARG sensors, placed proximal to the wrist and elbow and centrally on the chest, and employ a quaternion-based Unscented Kalman Filter technique to estimate orientations from the sensor data, from which joint angles are calculated based on a simple model of the arm. Tests reveal that the method has the potential to accurately derive specific angles. When compared with a camera-based system, a root mean square difference error between 5° - 15° was observed. [ABSTRACT FROM AUTHOR]

Highlights: [•] Using ICT, we built a system that employs a Nao robot to elicit joint attention (JA) with children and to capture social engagement cues. [•] Interaction with Nao depends on the partner: better in typically developing (TD) children than children with ASD. [•] Multimodal JA induction is more efficient in both TD and ASD. [•] The 3D spatial world gaze exploration shows less accuracy and the trunk position shows less stability in ASD compared to TD. [Copyright &y& Elsevier]

Abstract: In this paper, an incommensurate fractional order (FO) model has been proposed to generate ECG like waveforms. Earlier investigation of ECG like waveform generation is based on two identical Van-der Pol (VdP) family of oscillators, which are coupled by time delays and gains. In this paper, we suitably modify the three state equations corresponding to the nonlinear cross-product of states, time delay coupling of the two oscillators and low-pass filtering, using the concept of fractional derivatives. Our results show that a wide variety of ECG like waveforms can be simulated from the proposed generalized models, characterizing heart conditions under different physiological conditions. Such generalization of the modelling of ECG waveforms may be useful to understand the physiological process behind ECG signal generation in normal and abnormal heart conditions. Along with the proposed FO models, an optimization based approach is also presented to estimate the VdP oscillator parameters for representing a realistic ECG like signal. [Copyright &y& Elsevier]

• Arrhythmia is caused due to cumulative effect of disruptive phase relationship. • Heart electrical activities from different parts lead to desynchronised operation. • Phase space diagrams based on a 5 s ECG window with different time-delay are used for self-similarity analysis. • Features extracted from different parts of the phase space diagram are used to classify four types of arrhythmia. • Average computation time for one 5 s window of ECG is 1.9 s, showing potential for developing a real-time system. Among different cardiac arrhythmias, Ventricular Arrhythmias (VA) are fatal and life-threatening. Therefore, the detection and classification of VA is crucial task for cardiologists. However, in some cases, the ECG morphologies of two kinds of VA - Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) are similar and difficult to distinguish by human eyes. In this study, we present a low computational complexity arrhythmia classifier with high accuracy based on Phase Space Reconstruction (PSR). It is used to classify normal electrocardiogram (ECG), atrial fibrillation (AF), VT, VF and VT followed by VF. The Creighton University Ventricular Tachyarrhythmia Database (CUDB), Physikalisch-Technische Bundesanstalt Diagnostic Database (PTBDB), MIT-BIH Atrial Fibrillation Database (MIT-BIH AFDB) from PhysioNet databank and University Hospital Southampton database (UHSDB) are used for evaluation and comparison of the proposed algorithm. Two PSR diagrams were plotted based on a window length of 5 s ECG with two different time delays and the PSR-based features were extracted from them using the box-counting technique. This process was applied on 122 records with more than 5500 windows of ECG signals. The results show an average sensitivity of 98.73%, specificity of 99.71% and accuracy of 99.56%. The average computational time of our proposed method for one 5 s window processing is 1.9 s and therefore has the potential in real-time arrhythmia classification applications. [ABSTRACT FROM AUTHOR]

Background Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective. New method In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet Packet Transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms. Results Artifact cleaning performance has been measured using Root Mean Square Error ( RMSE ) and Artifact to Signal Ratio ( ASR )—an index similar to traditional Signal to Noise Ratio ( SNR ), and also by observing normalized power distribution topography over the scalp. Comparison with existing method(s) Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact. Conclusions Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts. [ABSTRACT FROM AUTHOR]

Aim To develop a statistical index based on the phase space reconstruction (PSR) of the electrocardiogram (ECG) for the accurate and timely diagnosis of ventricular tachycardia (VT) and ventricular fibrillation (VF). Methods Thirty-two ECGs with sinus rhythm (SR) and 32 ECGs with VT/VF were analyzed using the PSR technique. Firstly, the method of time delay embedding were employed with the insertion of delay "***" in the original time-series X(t), which produces the Y(t) = X(t - π). Afterwards, a PSR diagram was reconstructed by plotting Y(t) against X(t). The method of box counting was applied to analyze the behavior of the PSR trajectories. Measures as mean (μ), standard deviation (σ) and coefficient of variation (CV = σ/μ), kurtosis (β) for the box counting of PSR diagrams were reported. Results During SR, CV was always < 0.05, while with the onset of arrhythmia CV increased > 0.05. A similar pattern was observed with β, where < 6 was considered as the cut-off point between SR and VT/VF. Therefore, the upper threshold for SR was considered CVth = 0.05 and βth < 6. For optimisation of the accuracy, a new index (J ) was proposed: J=CV/CVth+(1-W)β/βth During SR the upper limit of J was the value of 1. Furthermore CV, β and J crossed the cut-off point timely before the onset of arrhythmia (average time: 4 min 31 s; SD: 2 min 30 s); allowing sufficient time for preventive therapy. Conclusion The J index improved ECG utility for arrhythmia monitoring and detection utility, allowing the prompt and accurate diagnosis of ventricular arrhythmias. [ABSTRACT FROM AUTHOR]

Highlights: [•] Input light stimulus has been predicted from measured plant electrical signal. [•] Linear and nonlinear estimators are used for the forward and inverse modelling. [•] Nonlinear Hammerstein–Wiener estimators give best prediction of the input stimulus. [•] The start/end time of the light impulses and the light intensities are estimated. [•] The proposed technique may be regarded as a first step for plant based biosensor. [ABSTRACT FROM AUTHOR]