A low-power multi-physiological monitoring processor for stress detection

Attaran, Nasrin; Brooks, Justin

doi:10.1109/icsens.2016.7808776

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…KNN is a simple and powerful classification method helping classify diseases with physiological signals. For examples, Personal system of detecting stress level is developed using physiological signals such as ECG, Chest Expansion, SpO2 and Electrodermal Activity (EDA) [22]. Using ANN classifier to analyze stockwell transform based on Electroencephalographic (EEG) signal as a non-invasive measurement in classifying mental diseases performs the best among other classifiers [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Thai Elderly People Based on Control Ability of Sugar Consumption

Temdee

Tania

2019

2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC)

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Nowadays, the number of Thai elders is rapidly increasing among world elderly population, how to keep their health is a major concern. Cardiovascular Diseases (CVDs) which are severe disease for Thai have higher mortality than cancers, and elderly people have a higher possibility to predispose CVDs. Hence, the risk factors of CVDs should be addressed. Obesity, as one of risk factors of CVDs, seriously affects Thai elders' wellbeing; excessive sugar consumption is a way leading to overweight and obesity. The amount of consumed sugar by Thai is much higher than the standard sugar consumption, and it also could cause many other diseases. Therefore, this paper proposes a classification method for elderly group who have the potential to control their blood sugar in order to prevent them from sugar overconsumption. This paper explored machine learning algorithms to find an appropriate classification method for elderly data. Artificial neuron network and K-nearest neighbors are applied for classifying elderly groups. Glycated hemoglobin (HbA1c) and fasting plasma glucose (FPG) are the noninvasive measurements of evaluating blood sugar, based on the two measurements, the 242 data from 121 elderly people are divided into two groups which are controllable group and uncontrollable group. The result indicates that artificial neuron network is more suitable for the dataset with 70.59% accuracy as compared to the accuracy of K-nearest neighbors.

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Section: Literature Reviewmentioning

confidence: 99%

Classification of Thai Elderly People Based on Control Ability of Sugar Consumption

Temdee

Tania

2019

2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC)

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“…Others, however, are designed to be small-factor and to be carried around by the user. In this type of systems, one of the main concerns is to make its architecture low-power and have a low-area footprint, as addressed in [3].…”

Section: Related Workmentioning

confidence: 99%

X3S: A multi-modal approach to monitor and assess stress through human-computer interaction

Gonçalves

Carneiro

Pêgo

et al. 2018

ComSIS

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There have been a variety of research approaches that have examined the stress issues related to human-computer interaction including laboratory studies, cross-sectional surveys, longitudinal case studies and intervention studies. A critical review of these studies indicates that there are important physiological, biochemical, somatic and psychological indicators of stress that are related to work activities where human-computer interaction occurs. In a medical or biological context, stress is a physical, mental, or emotional factor that causes bodily or mental tension, which can cause or influence the course of many medical conditions including psychological conditions such as depression and anxiety. In these cases, individuals are under an increasing demand for performance, driving them to be under constant pressure, and consequently to present variations in their levels of stress. To mitigate this condition, this paper proposes to add a new dimension in human-computer interaction through the development of a distributed multi-modal framework approach entitled X3S, which aims to monitor and assess the psychological stress of computer users during high-end tasks, in a non-intrusive and non-invasive way, through the access of soft sensors activity (e.g. task performance and human behaviour). This approach presents as its main innovative key the capacity to validate each stress model trained for each individual through the analysis of cortisol and stress assessment survey data. Overall, this paper discusses how groups of medical students can be monitored through their interactions with the computer. Its main aim is to provide a stress marker that can be effectively used in large numbers of users and without inconvenience.

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“…Stress monitoring is one of the emerging areas, where researchers have been exploring different biomarkers to measure the stress level of individuals. Examples include detecting the electrodermal activity (EDA) from skin to measure the change in resistance with sweating, or detecting the heart rate variability (HRV) in response to increased stress levels [2,3]. Electromyography (EMG), which is a tool to record and evaluate the electrical activity produced by our skeletal muscles, is a potentially useful technique for this application.…”

Section: Introductionmentioning

confidence: 99%

EMG-based Real Time Facial Gesture Recognition for Stress Monitoring

Orguc

Khurana

Stanković

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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An electromyogram (EMG) signal acquisition system capable of real time classification of several facial gestures is presented. The training data consist of the facial EMG collected from 10 individuals (5 female/5 male). A custom-designed sensor interface integrated circuit (IC) consisting of an amplifier and an ADC, implemented in 65nm CMOS technology, has been used for signal acquisition [1]. It consumes 3.8nW power from a 0.3V battery. Feature extraction and classification is performed in software every 300ms to give real-time feedback to the user. Discrete wavelet transforms (DWT) are used for feature extraction in the time-frequency domain. The dimensionality of the feature vector is reduced by selecting specific wavelet decomposition levels without compromising the accuracy, which reduces the computation cost of feature extraction in embedded implementations. A support vector machine (SVM) is used for the classification. Overall, the system is capable of identifying several jaw movements such as clenching, opening the jaw and resting in real-time from a single channel EMG data, which makes the system suitable for providing biofeedback during sleeping and awake states for stress monitoring, bruxism, and several orthodontic applications such as temporomandibular joint disorder (TMJD).

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A low-power multi-physiological monitoring processor for stress detection

Cited by 11 publications

References 6 publications

Classification of Thai Elderly People Based on Control Ability of Sugar Consumption

Classification of Thai Elderly People Based on Control Ability of Sugar Consumption

X3S: A multi-modal approach to monitor and assess stress through human-computer interaction

EMG-based Real Time Facial Gesture Recognition for Stress Monitoring

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