scholarly journals Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1849 ◽  
Author(s):  
Yekta Said Can ◽  
Niaz Chalabianloo ◽  
Deniz Ekiz ◽  
Cem Ersoy
Keyword(s):  
Detection System ◽  
Wearable Sensors ◽  
Real Life ◽  
Extraction Methods ◽  
Wearable Devices ◽  
Physiological Data ◽  
Stress Detection ◽  
Negative Effects ◽  
Extreme Stress ◽  
High Level

The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.

scholarly journals Feasibility of continuous fever monitoring using wearable devices

2020 ◽  
Author(s):  
Benjamin Smarr ◽  
Kirstin Aschbacher ◽  
Sarah M. Fisher ◽  
Anoushka Chowdhary ◽  
Stephan Dilchert ◽  
...  
Keyword(s):  
Large Scale ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Temperature Sensors ◽  
Physiological Data ◽  
High Temporal Resolution ◽  
The Novel ◽  
Illness Onset ◽  
Novel Coronavirus ◽  
Peripheral Temperature

Abstract Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.

scholarly journals Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3805 ◽  
Author(s):  
Kalliopi Kyriakou ◽  
Bernd Resch ◽  
Günther Sagl ◽  
Andreas Petutschnig ◽  
Christian Werner ◽  
...  
Keyword(s):  
Real World ◽  
Traffic Congestion ◽  
Expert Knowledge ◽  
Physiological Stress ◽  
Wearable Sensors ◽  
Real Life ◽  
Field Studies ◽  
Physiological Signals ◽  
Stress Detection ◽  
Physiological Sensors

There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.

Automatic Seizure Detection using Fully Convolutional Nested LSTM

2020 ◽  
Vol 30 (04) ◽  
pp. 2050019 ◽  
Author(s):  
Yang Li ◽  
Zuyi Yu ◽  
Yang Chen ◽  
Chunfeng Yang ◽  
Yue Li ◽  
...  
Keyword(s):  
Detection Rate ◽  
Short Term Memory ◽  
Detection System ◽  
Real Life ◽  
Seizure Detection ◽  
False Detection Rate ◽  
False Detection ◽  
Convolutional Network ◽  
Eeg Data ◽  
High Level

The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are based on hand-wrought feature extraction. This study proposes an end-to-end automatic seizure detection system based on deep learning, which does not require heavy preprocessing on the EEG data or feature engineering. The fully convolutional network with three convolution blocks is first used to learn the expressive seizure characteristics from EEG data. Then these robust EEG features pertinent to seizures are presented as an input to the Nested Long Short-Term Memory (NLSTM) model to explore the inherent temporal dependencies in EEG signals. Lastly, the high-level features obtained from the NLSTM model are fed into the softmax layer to output predicted labels. The proposed method yields an accuracy range of 98.44–100% in 10 different experiments based on the Bonn University database. A larger EEG database is then used to evaluate the performance of the proposed method in real-life situations. The average sensitivity of 97.47%, specificity of 96.17%, and false detection rate of 0.487 per hour are yielded. For CHB–MIT Scalp EEG database, the proposed model also achieves a segment-level sensitivity of 94.07% with a false detection rate of 0.66 per hour. The excellent results obtained on three different EEG databases demonstrate that the proposed method has good robustness and generalization power under ideal and real-life conditions.

scholarly journals Feasibility of continuous fever monitoring using wearable devices

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Benjamin L. Smarr ◽  
Kirstin Aschbacher ◽  
Sarah M. Fisher ◽  
Anoushka Chowdhary ◽  
Stephan Dilchert ◽  
...  
Keyword(s):  
Large Scale ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Temperature Sensors ◽  
Physiological Data ◽  
High Temporal Resolution ◽  
The Novel ◽  
Illness Onset ◽  
Novel Coronavirus ◽  
Peripheral Temperature

AbstractElevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.

scholarly journals DBD-RCO: Derivative Based Detection and Reverse Combinatorial Optimization to improve heart beat detection for wearable devices

2017 ◽  
Author(s):  
Andrea Bizzego ◽  
Cesare Furlanello
Keyword(s):  
Combinatorial Optimization ◽  
Wearable Sensors ◽  
Real Life ◽  
Wearable Devices ◽  
Heart Beat ◽  
The Novel ◽  
Signal Processing Algorithms ◽  
Life Context ◽  
Movement Artifacts ◽  
Heart Physiology

AbstractThe diffusion of wearable sensors enables the monitoring of heart physiology in real-life contexts. Wearable technology is characterized by important advantages but also by technical limitations that affect the quality of the collected signals in terms of movement artifacts and presence of noise. Therefore specific signal processing algorithms are required to cope with the lower quality and different characteristic of signals collected with wearable sensor units. Here we propose and validate a pipeline to detect heartbeats in cardiac signals, extract the Inter Beat Intervals (IBI) and compute the Heart Rate Variability (HRV) indicators from wearable devices. In particular, we describe the novel Derivative-Based Detection (DBD) algorithm to estimate the beat position in Blood Volume Pulse (BVP) signals and the Reverse Combinatorial Optimization (RCO) algorithm to identify and correct IBI extraction errors. The pipeline is first validated on data from clinical-grade sensors, then on a benchmark dataset including examples of movement artifacts in a real-life context. The accuracy of the DBD algorithm is assessed in terms of precision and recall of the detection; error in the IBI values is quantified by root mean square error. The reliability of HRV indicators is evaluated by the Bland-Altman ratio. The DBD algorithm performs better than a state-of-art algorithm for both medical-grade and wearable devices. However, as already found in similar studies, worse reliability is found with the BVP signal in computing frequency domain HRV indicators, in particular with wearable devices.

A Review of Different Techniques of Age Estimation from Human Face

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Khemchandra Patel ◽  
Dr. Kamlesh Namdev
Keyword(s):  
Age Estimation ◽  
Real Life ◽  
Extraction Methods ◽  
Human Face ◽  
Age Changes ◽  
Missing Children ◽  
Different Types ◽  
Age Variation ◽  
Human Faces ◽  
Facial Age

Age changes cause major variations in the appearance of human faces. Due to many lifestyle factors, it is difficult to precisely predict how individuals may look with advancing years or how they looked with "retreating" years. This paper is a review of age variation methods and techniques, which is useful to capture wanted fugitives, finding missing children, updating employee databases, enhance powerful visual effect in film, television, gaming field. Currently there are many different methods available for age variation. Each has their own advantages and purpose. Because of its real life applications, researchers have shown great interest in automatic facial age estimation. In this paper, different age variation methods with their prospects are reviewed. This paper highlights latest methodologies and feature extraction methods used by researchers to estimate age. Different types of classifiers used in this domain have also been discussed.

Enabling Batteryless Wearable Devices by Transferring Power Through The Human Body

2021 ◽  
Vol 24 (3) ◽  
pp. 30-34
Author(s):  
Rishi Shukla ◽  
Neev Kiran ◽  
Rui Wang ◽  
Jeremy Gummeson ◽  
Sunghoon Ivan Lee
Keyword(s):  
Energy Source ◽  
Low Power ◽  
Human Body ◽  
Wearable Sensors ◽  
Primary Energy ◽  
Wearable Devices ◽  
Ultra Low Power ◽  
The Past ◽  
Key Barrier

Over the past few decades, we have witnessed tremendous advancements in semiconductor and MEMS technologies, leading to the proliferation of ultra-miniaturized and ultra-low-power (in micro-watt ranges) wearable devices for wellness and healthcare [1]. Most of these wearable sensors are battery powered for their operation. The use of an on-device battery as the primary energy source poses a number of challenges that serve as the key barrier to the development of novel wearable applications and the widespread use of numerous, seamless wearable sensors [5].

scholarly journals Respecting freedom in the absence of compassion: realistic behavior by public officials for the COVID-19 vaccination program

2021 ◽  
Author(s):  
Dalmacito A Cordero
Keyword(s):  
Real Life ◽  
Current Situation ◽  
Vaccination Program ◽  
Negative Effects ◽  
Public Officials ◽  
The Government ◽  
Survival Of The Fittest

Abstract The virtue of compassion is a valid antidote to lighten the burden of negative effects brought by the COVID-19 pandemic. However, real-life situations can attest that this is not always the kind of behavior for some people since the current situation is considered as ‘survival of the fittest.’ In its absence, the respect of freedom by public officials to every citizen is a great substitute most especially in the implementation of the government’s vaccination program. This behavior actualizes every person’s plan of protection without being pressured. This right needs to be provided and not taken away by the government.

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