Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher classification accuracy incorporating a limited number of bio-sensors. In the domain of machine learning, ensemble learning methods have been successfully applied to solve different types of real-world machine learning problems which require improved classification accuracies. Emphasising on that, this research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals. As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis. The first three feature extraction methods are well-known ECG based feature extraction techniques mentioned in the literature, and the fourth technique is a novel method proposed in this study. The machine learning procedure of this investigation evaluates the performance of a set of well-known ensemble learners for emotion classification and further improves the classification results using feature selection as a prior step to ensemble model training. Compared to the best performing single biosensor based model in the literature, the developed ensemble learner has the accuracy gain of 10.77%. Furthermore, the developed model outperforms most of the multiple biosensor based emotion recognition models with a significantly higher classification accuracy gain.
Sleep apnea is a breathing disorder where a person repeatedly stops breathing in sleep. Early detection is crucial for infants because it might bring long term adversities. The existing accurate detection mechanism (pulse oximetry) is a skin contact measurement. The existing non-contact mechanisms (acoustics, video processing) are not accurate enough. This paper presents a novel algorithm for the detection of sleep apnea with video processing. The solution is non-contact, accurate and lightweight enough to run on a single board computer. The paper discusses the accuracy of the algorithm on real data, advantages of the new algorithm, its limitations and suggests future improvements.
Mapping tasks to cores in an Multiprocessor Systemon-Chip (MPSoC) to meet constraints is widely investigated. Thus far the data flow graphs used for binding have been limited to acyclic graphs or have been single rate. In this paper we generalize the approach by allowing DFGs to be cyclic and multi rate. We further improve energy consumption by setting frequency per core in a Globally Asynchronous and Locally Synchronous (GALS) architecture (by the distribution of slack). A design flow is proposed with these two approaches to form a latency constrained and energy efficient binding. A generalized solution is proposed, compared to state-of-the-art, using improvements in formulation, data structures and heuristics. Eight benchmarks are experimented upon for mesh and pipeline architectures. Our heuristics achieve significant simulation speedup compared to the state-of-the-art and provide a solution which is 2.5% lower (26% worst case) than the optimal, but the solution is obtained 40x quicker (average case). Such a speedup allows us to rapidly explore a large design space.
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