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This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the(More)
In this paper we present a methodology for recognizing three fundamental movements of the human forearm (extension, flexion and rotation) using pattern recognition applied to the data from a single wrist-worn, inertial sensor. We propose that this technique could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies(More)
In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during(More)
In this paper we present a methodology as a proof-of-concept for recognizing fundamental movements of the human arm (extension, flexion and rotation of the forearm) involved in 'making-a-cup-of-tea', typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are(More)
This paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated(More)
—This paper presents a wavelet-based low-complexity Electrocardiogram (ECG) compression algorithm for mobile healthcare systems, in the backdrop of real clinical requirements. The proposed method aims at achieving good trade-off between the compression ratio (CR) and the fidelity of the reconstructed signal, to preserve the clinically diagnostic features.(More)
—In recent years, the focus of computing has moved away from performance-centric serial computation to energy-efficient parallel computation. This necessitates run-time optimisation techniques to address the dynamic resource requirements of different applications on many-core architectures. In this paper, we report on intelligent run-time algorithms which(More)
In this paper we present a systematic exploration to determine several EEG based features for classifying three emotional states (happy, fearful and neutral) pertaining to face perception. EEG data were acquired through a 19-channel wireless system from eight adults under two conditions - in a constrained position and involving head-body movements. The(More)
In this paper, we propose a low-complexity architecture design methodology for the Single Channel Independent Component Analysis (SCICA) algorithm targeting pervasive personalized healthcare. SCICA, unlike the conventional ICA, separates the signal from multiple sources using only a single sensor that has tremendous potential for reducing the number of(More)
In this paper we present a systematic exploration to formulate a predictive model of the human cognitive process with the changing environmental conditions at workplace. We select six different environmental conditions with small change in temperature/ventilation representative of realistic work environment having manual control. EEG data were acquired(More)