Comprehensive Observation and its Role in Self-Awareness; An Emotion Recognition System Example

TaheriNejad, Nima; Jantsch, Axel; Pollreisz, David

doi:10.15439/2016f588

Cited by 26 publications

(20 citation statements)

References 13 publications

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“…attention to the observation (monitoring) part of the process. In 2016, TaheriNejad et al published a paper [29] which highlighted this aspect and elaborated on different elements of observation and their potential effect on self-awareness and the overall performance of the system. Since then, several publications have appeared in the literature which demonstrated this effect in various applications [13,[26][27][28][30][31][32][33].…”

Section: Background and Related Workmentioning

confidence: 99%

“…In this work, we study two aspects of self-awareness, namely confidence and data reliability, and the interplay between the two as well as their effect on the overall performance of the system. Moreover, we have tried to formalize these concepts, which were initially described in [29] only conceptually, in order to establish a more uniform understanding of these concepts.…”

Section: Self-awareness Propertiesmentioning

confidence: 99%

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Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

et al. 2019

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Cardiovascular diseases are one of the world's major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However, the chance of survival of an outpatient could be increased if a mobile EWS system would monitor them during their daily activities to be able to alert in case of danger. Because of limited healthcare professional supervision of this health condition assessment, a mobile EWS system needs to have an acceptable level of reliability-even if errors occur in the monitoring setup such as noisy signals and detached sensors. In earlier works, a data reliability validation technique has been presented that gives information about the trustfulness of the calculated EWS. In this paper, we propose an EWS system enhanced with the self-aware property confidence, which is based on fuzzy logic. In our experiments, we demonstrate that-under adverse monitoring circumstances (such as noisy signals, detached sensors, and non-nominal monitoring conditions)-our proposed Self-Aware Early Warning Score (SA-EWS) system provides a more reliable EWS than an EWS system without self-aware properties.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Self-awareness Propertiesmentioning

confidence: 99%

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

et al. 2019

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“…A design to break down and upgrade information unwavering quality and consistency. Specifically, to present a various levelled operator based information certainty assessment framework to distinguish incorrect or unimportant fundamental flag estimations [17]. Remote monitoring what's a more, symptomatic framework that gives an all-encompassing point of view of patients and their health conditions.…”

Section: Related Workmentioning

confidence: 99%

“…In any case, observing has gotten little consideration up until now. As of late, TaheriNejad et al [17] distributed an investigation on different components of perception and their potential job in mindfulness. This prompted additionally look into which demonstrated the advantages of these components, for example, information consistent quality, consideration various applications.…”

Section: System Algorithmsmentioning

confidence: 99%

Data Science: Prediction and Analysis of Data using Multiple Classifier System

Hosamani¹,

Vimala²

2019

IJCERT

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In modern times, with the trending technology, for classification of Big Data it is ubiquitous that Deep Neural network algorithms are used. The experiment was carried out considering relatively smaller data. In this paper, we propose, a model Multiple Classifier System, in which the different classifiers are ensembled. We have ensembled different classifiers like LR, LDA, KNN, CART, NB, and SVM. To check the performance of the Multiple Classifier System, we have used Iris flower dataset. When the neural networks and the Multiple Classifier System was compared with the performance, the MCS has shown graduation increase in the results.

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“…In addition, the authors consider the energy efficiency and dependability of the system via adjusting the priorities of the sensory data collection. In [6], the observation process, which transforms raw data into a high-quality description of the system about itself and its environment, is improved. This is done by measuring different parameters, such as, the confidence of the system and its relevance, as discussed on emotion recognition systems.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Self-Aware Wearable Systems in Epileptic Seizure Detection

Forooghifar

Aminifar

Alonso

2018

2018 21st Euromicro Conference on Digital System Design (DSD)

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Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. A recently proposed concept for overcoming this challenge is self-awareness, which increases system's knowledge of itself and the surrounding environment. This is precisely what health monitoring wearable systems require to adapt to different situations. To demonstrate the impact of introducing self-awareness in wearable technologies, we consider the epileptic seizure detection problem, as a case study. Epilepsy affects around 1% of the world's population, which can dramatically degrade the quality of life and represents a major public health issue. As a result, detection of epileptic seizures has become more important over the past decades. In this paper, we aim to introduce a new generation of self-aware wearable systems to decrease energy consumption and improve their seizures detection capabilities by introducing the notion of self-awareness in such systems. These techniques include switching to low-power mode to reduce the energy consumption and machine-learning model enhancement to improve detection quality. We incorporated our proposed techniques in the machine learning module, which detects epileptic seizures by monitoring the cardiac and respiratory systems. We evaluated the performance of our approach based on an epilepsy database of more than 141 hours, provided by the Lausanne University Hospital (CHUV). Our self-aware wearable system achieves 36% reduction in computational complexity and 10.51% improvement in detection performance.

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Comprehensive Observation and its Role in Self-Awareness; An Emotion Recognition System Example

Cited by 26 publications

References 13 publications

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

Data Science: Prediction and Analysis of Data using Multiple Classifier System

Self-Aware Wearable Systems in Epileptic Seizure Detection

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