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.

scholarly journals Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Kalliopi Kyriakou ◽  
Bernd Resch
Keyword(s):  
Spatial Analysis ◽  
Urban Areas ◽  
Hot Spot ◽  
Low Cost ◽  
Field Studies ◽  
Physiological Signals ◽  
Sensor Data ◽  
Skin Response ◽  
Density Analysis ◽  
Physiological Sensors

Abstract. Over the last years, we have witnessed an increasing interest in urban health research using physiological sensors. There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, most of the studies focus mainly on the analysis of the physiological signals and disregard the spatial analysis of the extracted geo-located emotions. Methodologically, the use of hotspot maps created through point density analysis dominates in previous studies, but this method may lead to inaccurate or misleading detection of high-intensity stress clusters. This paper proposes a methodology for the spatial analysis of moments of stress (MOS). In a first step, MOS are identified through a rule-based algorithm analysing galvanic skin response and skin temperature measured by low-cost wearable physiological sensors. For the spatial analysis, we introduce a MOS ratio for the geo-located detected MOS. This ratio normalises the detected MOS in nearby areas over all the available records for the area. Then, the MOS ratio is fed into a hot spot analysis to identify hot and cold spots. To validate our methodology, we carried out two real-world field studies to evaluate the accuracy of our approach. We show that the proposed approach is able to identify spatial patterns in urban areas that correspond to self-reported stress.

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 A Multiprotocol Wireless Sensor Network for High Performance Sport Applications

2018 ◽  
Vol 1 (4) ◽  
pp. 52 ◽  
Author(s):  
Vincenzo Bonaiuto ◽  
Paolo Boatto ◽  
Nunzio Lanotte ◽  
Cristian Romagnoli ◽  
Giuseppe Annino
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
High Performance ◽  
Wearable Sensors ◽  
Real Life ◽  
Wireless Sensor ◽  
Sport Equipment ◽  
Wide Range ◽  
Physiological Sensors

The use of a network of wearable sensors placed on the athlete or installed into sport equipment is able to offer, in a real sport environment rather than in the unspecific spaces of a laboratory, a valuable real-time feedback to the coach during practice. This is made possible today by the coordinate use of a wide range of kinematic, dynamic, and physiological sensors. Using sensors makes training more effective, improves performance assessment, and can help in preventing injuries. In this paper, a new wireless sensor network (WSN) system for elite sport applications is presented. The network is made up of a master node and up to eight peripheral nodes (slave nodes), each one containing one or more sensors. The number of nodes can be increased with second level slave nodes; the nature of sensors varies depending on the application. Communication between nodes is made via a high performance 2.4 GHz transceiver; the network has a real-life range in excess of 100 m. The system can therefore be used in applications where the distance between nodes is long, for instance, in such sports as kayaking, sailing, and rowing. Communication with user and data download are made via a Wi-Fi link. The user communication interface is a webpage and is therefore completely platform (computer, tablet, smartphone) and operating system (Windows, iOS, Android, etc.) independent. A subset of acquired data can be visualized in real time on multiple terminals, for instance, by athlete and coach. Data from kayaking, karting, and swimming applications are presented.

scholarly journals CNN and LSTM-Based Emotion Charting Using Physiological Signals

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4551
Author(s):  
Muhammad Najam Dar ◽  
Muhammad Usman Akram ◽  
Sajid Gul Khawaja ◽  
Amit N. Pujari
Keyword(s):  
Real World ◽  
Affective Computing ◽  
Short Term Memory ◽  
Low Cost ◽  
Wearable Sensors ◽  
Classification Performance ◽  
Physiological Signals ◽  
Significant Feature ◽  
High Arousal ◽  
Emotion Elicitation

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

Real-life Field Studies of the NOx Removing Properties of Photocatalytic Surfaces in Roskilde and Copenhagen Airport, Denmark

2020 ◽  
Vol 01 ◽  
Author(s):  
Henrik Jensen ◽  
Pernille D. Pedersen
Keyword(s):  
Air Quality ◽  
High Stability ◽  
Reference Site ◽  
Real Life ◽  
Field Studies ◽  
The Real ◽  
Before And After ◽  
Parking Lot ◽  
Photocatalytic Concrete

Aims: To evaluate the real-life effect of photocatalytic surfaces on the air quality at two test-sites in Denmark. Background: Poor air quality is today one of the largest environmental issues, due to the adverse effects on human health associated with high levels of air pollution, including respiratory issues, cardiovascular disease (CVD), and lung cancer. NOx removal by TiO2 based photocatalysis is a tool to improve air quality locally in areas where people are exposed. Methods: Two test sites were constructed in Roskilde and Copenhage airport. In Roskilde, the existing asphalt at two parking lots was treated with TiO2 containing liquid and an in-situ ISO 22197-1 test setup was developed to enable in-situ evaluation of the activity of the asphalt. In CPH airport, photocatalytic concrete tiles were installed at the "kiss and fly" parking lot, and NOx levels were continuously monitored in 0.5 m by CLD at the active site and a comparable reference site before and after installation for a period of 2 years. Results: The Roskilde showed high stability of the photocatalytic coating with the activity being largely unchanged over a period of 2 years. The CPH airport study showed that the average NOx levels were decreased by 12 % comparing the before and after NOx concentrations at the active and reference site. Conclusion: The joined results of the two Danish demonstration projects illustrate a high stability of the photocatalytic coating as well as a high potential for improvements of the real-life air quality in polluted areas.

scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

scholarly journals Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

2021 ◽  
Author(s):  
Amarildo Likmeta ◽  
Alberto Maria Metelli ◽  
Giorgia Ramponi ◽  
Andrea Tirinzoni ◽  
Matteo Giuliani ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Real Life ◽  
User Preferences ◽  
Inverse Reinforcement Learning ◽  
Water Release ◽  
Reward Function ◽  
Model Free ◽  
Conflicting Objectives ◽  
Multiple Experts

AbstractIn real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

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