Towards long term monitoring of electrodermal activity in daily life

Manic depression, also known as bipolar disorder, is a common and severe form of mental disorder. The European research project MONARCA aims at developing and validating mobile technologies for multi-parametric, long term monitoring of physiological and behavioral information relevant to bipolar disorder. One aspect of MONARCA is to investigate the long term monitoring of Electrodermal activity (EDA) to support the diagnosis and treatment of bipolar disorder patients. EDA is known as an indicator of the emotional state and the stress level of a person. To realize a long-term monitoring of the EDA, the integration of the sensor system in the shoe or sock is a promising approach. This paper presents a first step towards such a sensor system. In a feasibility study including 8 subjects, we investigate the correlation between EDA measurements at the fingers, which is the most established sensing site, with measurements of the EDA at the feet. The results indicate that 88% of the evoked skin conductance responses (SCRs) occur at both sensing sites. When using an action movie as psychophysiologically activating stimulus, we have found weaker reactivity in the foot than in the hand EDA. The results also suggest that the influence of moderate physical activity on EDA measurements is low and has a similar effect for both recording sites. This suggests that the foot recording location is suitable for recordings in daily life even in the presence of moderate movemen

Active learning for electrodermal activity classification

To filter noise or detect features within physiological signals, it is often effective to encode expert knowledge into a model such as a machine learning classifier. However, training such a model can require much effort on the part of the researcher; this often takes the form of manually labeling portions of signal needed to represent the concept being trained. Active learning is a technique for reducing human effort by developing a classifier that can intelligently select the most relevant data samples and ask for labels for only those samples, in an iterative process. In this paper we demonstrate that active learning can reduce the labeling effort required of researchers by as much as 84% for our application, while offering equivalent or even slightly improved machine learning performance.MIT Media Lab ConsortiumRobert Wood Johnson Foundatio

A Multimodal Perception Framework for Users Emotional State Assessment in Social Robotics

In this work, we present an unobtrusive and non-invasive perception framework based on the synergy between two main acquisition systems: the Touch-Me Pad, consisting of two electronic patches for physiological signal extraction and processing; and the Scene Analyzer, a visual-auditory perception system specifically designed for the detection of social and emotional cues. It will be explained how the information extracted by this specific kind of framework is particularly suitable for social robotics applications and how the system has been conceived in order to be used in human-robot interaction scenarios

Measuring the effects of cognitive stress and relaxation using a wearable smart ring

Abstract. Prolonged stress is known to be a risk factor for various kinds of diseases, such as cardiovascular diseases. If stress could be easily measured, it would enable monitoring of stress and help people make better choices to achieve a healthier lifestyle. In this study, a polysomnography system as well as a wearable smart ring were used to measure the responses of central and autonomic nervous systems from ten healthy test subjects (five male and five female), aged 23–26. The responses were measured in two conditions: cognitive stress induced by a mental calculation task and relaxation induced by a focused attention meditation exercise. Power spectral densities of two electroencephalography frequency bands, alpha and beta, were calculated to represent the central nervous system response. The autonomic nervous system response was measured using heart rate, heart rate variability and peripheral (finger) temperature. In cognitive stress, alpha and beta bands both showed higher activity, increasing by 53.26% and 94.70%, respectively. Heart rate also increased by 19.33%, while heart rate variability decreased by 25.65% and peripheral temperature change was 0.77℃ lower. Results show that the changes in autonomic nervous system responses acquired by the smart ring correlate with the changes in central nervous system responses acquired by the polysomnography system. This suggests that a smart ring could be used for an indirect measurement of human stress level. Follow-up studies with larger sample sizes are needed to confirm the findings of this study and to determine the most suitable features for representation of human stress level.Kognitiivisen stressin ja rentoutumisen vaikutusten mittaaminen älysormuksella. Tiivistelmä. Pitkittynyt stressi toimii riskitekijänä lukuisille sairauksille, kuten sydän- ja verisuonitaudeille. Stressin vaivaton mittaaminen mahdollistaisi stressitason seuraamisen, mikä vuorostaan auttaisi ihmisiä tekemään parempia valintoja terveellisemmän elämäntyylin puolesta. Tässä tutkimuksessa käytettiin polysomnografialaitteistoa sekä puettavaa älysormusta keskushermoston ja autonomisen hermoston vasteiden mittaamiseen kymmeneltä terveeltä koehenkilöltä (viisi miestä ja viisi naista), iältään 23–26. Vasteet mitattiin kahdessa tilassa: päässälaskutehtävän aikaansaamassa kognitiivisessa stressissä sekä hengitykseen keskittyvän meditaatioharjoituksen aikaansaamassa rentoutumisessa. Kahdelle elektroenkefalografian taajuuskaistalle, alfalle ja beetalle, laskettiin tehon spektritiheydet kuvastamaan keskushermoston vastetta. Lisäksi laskettiin syke, sykevälivaihtelu sekä ääreislämpötila (sormen lämpötila) kuvastamaan autonomisen hermoston vastetta. Kognitiivisessa stressissä sekä alfa- että beetakaistan aktiivisuus kasvoi, alfalla 53,26 % ja beetalla 94,70 %. Myös syke nousi 19,33 %, kun taas sykevälivaihtelu pieneni 25,65 % ja ääreislämpötilan muutos oli 0,77 ℃ pienempi. Tulokset osoittavat, että älysormuksella mitatut autonomisen hermoston vasteen muutokset korreloivat polysomnografialaitteistolla mitattujen keskushermoston vasteen muutosten kanssa. Tämä antaa ymmärtää, että älysormusta voitaisiin käyttää ihmisen stressitason epäsuoraan mittaamiseen. Suuremman kokoluokan jatkotutkimuksia tarvitaan varmistamaan tämän tutkimuksen löydökset sekä määrittämään sopivimmat fysiologiset piirteet kuvastamaan ihmisen stressitasoa

EDA-signaalin automaattinen virheensuodatus

Tiivistelmä. Tässä kandidaatintyössä tutkin ihmiskehosta mitatun sähkönjohtavuussignaalin eli EDA-signaalin automaattista virheensuodatusta. EDA-signaali on kytköksissä ihmisen parasympaattiseen hermostoon, joten sen avulla voidaan tulkita ihmisen tunnetiloja ja vireystilaa. Tavallinen EDA-signaali etenee muutaman sekunnin mittaisissa nousevissa ja laskevissa aalloissa. Tässä tutkielmassa käyttämäni data on kerätty Empatica E4 -rannekkeella, joka mittaa EDA-signaalin lisäksi myös kiihtyvyysdataa. Koska mittalaite asetetaan koehenkilön käteen, se on altis henkilön liikkeiden aiheuttamille virheille. Nämä virheet saattavat näkyä signaalissa sekä nollaan pudonneena signaalina että epätavallisen jyrkkinä piikkeinä. EDA-signaalin keräämistä, sen ongelmia ja virheensuodatusta on käsitelty kirjallisuudessa ja siihen on esitetty erilaisia ratkaisuja. Tässä tutkielmassa käyn läpi aiheeseen liittyviä artikkeleita ja esittelen muutamia ratkaisuehdotuksia virhepiikkien eli niin sanottujen artefaktien automaattiseksi tunnistamiseksi. Esittelen myös oman ratkaisuni, joka perustuu signaalin pätkien luokitteluun normaaleiksi tai todennäköisesti virheellisiksi muodon ja amplitudin perusteella. Leimoja on neljää eri tyyppiä: 0 — normaalia signaalia; 1 — signaali on pudonnut alle minimiamplitudin; 2 — signaalissa on liikkumisesta johtuva jyrkkä piikki; ja 3 — signaalissa on tuntemattomasta syystä johtuva jyrkkä piikki. Luokittelussa käytetään apuna EDA-signaalin kanssa samanaikaisesti kerättyä kiihtyvyysdataa. Analysoitu ja luokiteltu signaali esitetään graafisesti väriä leiman mukaan vaihtavalla viivalla. Esittelen lyhyesti ohjelmani tuottamia tuloksia ja arvioin niiden oikeellisuutta. Lopuksi esitän ehdotuksia siitä, kuinka työtä voisi jatkaa, ja arvioin ratkaisematta jääneitä ongelmia.Automatic error detection in EDA signal. Abstract. This is a bachelor’s thesis on automatic detection of artefacts in EDA signal (Electodermal Activity). EDA measures electrical characteristics of human skin. These characteristics are connected to the parasympathetic nerve system and thus they reflect emotions and arousal of the person being monitored. My data is collected using Empatica E4 wristband and it consists of several hours of EDA and acceleration data from three different days. Wristbands are liable to errors due to rapid movement of the test person, and these errors can be seen in the signal as steep peaks or very low amplitude level. Filtering these errors from EDA signal has been discussed in several articles, many of which also provide solutions to this problem. In this thesis I present some of these articles. I also suggest my own solution, which is a Python program that measures amplitude and derivative of the signal. Concurrently gathered acceleration data is used in determining whether the signal is erroneous due to rapid movements of the test person. Every sample of the signal is being labeled based on these properties. There are four different labels: 0 — normal signal; 1 — amplitude of the signal is too low; 2 — fast change in signal level due to movement of the test person; and 3 — fast change in signal level due to unknown reason. The program creates metadata, which contains information about proportions of the different labels. Lastly, labeled signal is presented as a multicoloured line. After discussing my methodology I present and assess the results yielded by my program. In the last section I discuss unsolved problems and propose possible themes for future work

Development of Galvanic Skin Response Sensor System to Measure Mental Stress

Stress is a very normal reaction when a person is feel threaten or upset in some way. Generally, stress happen when a person ability is not able to meet with the situational demands. It can be either positive or negative. Stress can give a person to have high concentration to face certain circumstances as well as better reaction to handle difficult situation. On the other side, it can also cause sickness to a person if one is suffer from overstress. A person will have health issues like the risk of depression and neurological disorders like stroke if mental stress level is too high. Thus, a device is necessary to measure the stress level so that the stress level can be regulate before it is out of control. Although there are several of methods in the market that are able to measure the stress level, but there are all expensive and complicated. Thus, this project will be focusing on developing a Galvanic Skin Response Sensor that are able to measure stress level based on the skin conductivity. GSR sensor is an economical tool to measure stress level with simpler analysis. Complete methodology and findings are shown as we go through the report

Ubicomp for animal welfare: envisioning smart environments for kenneled dogs

Whilst the ubicomp community has successfully embraced a number of societal challenges for human benefit, including healthcare and sustainability, the well-being of other animals is hitherto underrepresented. We argue that ubicomp technologies, including sensing and monitoring devices as well as tangible and embodied interfaces, could make a valuable contribution to animal welfare. This paper particularly focuses on dogs in kenneled accommodation, as we investigate the opportunities and challenges for a smart kennel aiming to foster canine welfare. We conducted an in-depth ethnographic study of a dog rehoming center over four months; based on our findings, we propose a welfare centered framework for designing smart environments, integrating monitoring and interaction with information management. We discuss the methodological issues we encountered during the research and propose a smart ethnographic approach for similar projects

Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study

Process model comprehension is essential in order to understand the Five Ws (i.e., who, what, where, when, and why) pertaining to the processes of organizations. However, research in this context showed that a proper comprehension of process models often poses a challenge in practice. For this reason, a vast body of research exists studying the factors having an influence on process model comprehension. In order to point research towards a neuro-centric perspective in this context, the paper at hand evaluates the appropriateness of measuring the electrodermal activity (EDA) during the comprehension of process models. Therefore, a preliminary test run and a feasibility study were conducted relying on an EDA and physical activity sensor to record the EDA during process model comprehension. The insights obtained from the feasibility study demonstrated that process model comprehension leads to an increased activity in the EDA. Furthermore, EDA-related results indicated significantly that participants were confronted with a higher cognitive load during the comprehension of complex process models. In addition, the experiences and limitations we have learned in measuring the EDA during the comprehension of process models are discussed in this paper. In conclusion, the feasibility study demonstrated that the measurement of the EDA could be an appropriate method to obtain new insights in process model comprehension

Development of Galvanic Skin Response Sensor System to Measure Mental Stress

Stress is a very normal reaction when a person is feel threaten or upset in some way. Generally, stress happen when a person ability is not able to meet with the situational demands. It can be either positive or negative. Stress can give a person to have high concentration to face certain circumstances as well as better reaction to handle difficult situation. On the other side, it can also cause sickness to a person if one is suffer from overstress. A person will have health issues like the risk of depression and neurological disorders like stroke if mental stress level is too high. Thus, a device is necessary to measure the stress level so that the stress level can be regulate before it is out of control. Although there are several of methods in the market that are able to measure the stress level, but there are all expensive and complicated. Thus, this project will be focusing on developing a Galvanic Skin Response Sensor that are able to measure stress level based on the skin conductivity. GSR sensor is an economical tool to measure stress level with simpler analysis. Complete methodology and findings are shown as we go through the report