Application for pre-processing and visualization of electrodermal activity wearable data

Suoja, Kari; Liukkonen, Jari; Jussila, Jari; Salonius, Henna; Venho, Niina; Sillanpää, Virpi; Vuori, Vilma; Helander, Nina

doi:10.1007/978-981-10-5122-7_24

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…a customer support situation • researchers can use the application to collect affective experiences in a variety of context, as well as, to collect data to further develop theoretical and conceptual models • individuals can use the mobile application to collect qualitative data on their wellbeing and to combine it with data collected by sensors, such as e.g. the heart rate variability [16] and electrodermal activity [38] The main design principles derived from the study include real time capture of peak/end experiences, as well as, simplicity and ease of use of the application. Real time capture of peak/end experiences enables cost-efficient measuring of success of events and supports planning of events.…”

Section: Discussionmentioning

confidence: 99%

“…This way, compared to the alpha version, the stored data can be easily combined with data collected with different kind of sensors, e.g. Moodmetric electrodermal activity ring that the company has experimented in B2B encounters [38]. An additional feature of selecting the touchpoint from four alternative locations was added to the tailored version of the event organizer.…”

Section: Second Adr Cyclementioning

confidence: 99%

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Design of Mobile Application for Self-reporting Affective Experiences

Jussila

Sillanpää

Helander

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Affective experiences have a major role in value creation during customer encounters both in business to consumer (B2C) and business to business (B2B) domains. However, understanding and effectively identifying affective experiences is challenging. Based on a practical need of a case company, the aim of this study was to develop an ensemble artifact for measuring affective experiences during customer encounters. Following action design research method, we designed the artifact in two cycles. First cycle involved creating a poster to capture affective experiences in an event setting. In second cycle, a mobile application was developed for identifying affective experiences. In a pilot study the mobile application was used to interview 73 individuals during customer encounters at various touchpoints of the customer event by four interviewers. The study reports the preliminary findings, evaluates the artifact development process through the lenses of design science research process model, and identifies the future research directions.

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Section: Discussionmentioning

confidence: 99%

Section: Second Adr Cyclementioning

confidence: 99%

Design of Mobile Application for Self-reporting Affective Experiences

Jussila

Sillanpää

Helander

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…An open source application called MoodmetricDataViz has been developed for visualization of electrodermal activity data that works with the open data described in previous chapter. An earlier release [35] was updated to include support for (database exports from) Android devices, and visualizations for extended time periods (from 24 hours to one week). The purpose of the open source solution is to offer a complementary tool for those researchers that are familiar with R and wish to develop their own solutions from data extracted from mobile devices.…”

Section: Open Source Application For Visualization Of Electrodermal Amentioning

confidence: 99%

“…Recently wearable EDA devices have become available, which make EDA measurement appealing for both psychological research and clinical use [8]. Wearable EDA devices have been introduced in many shapes and forms, including rings [35,39], gloves [26], wristbands [16], smartwatches [30], and socks [23]. In psychological research, wearable sensors allow experiments to take place in more ecologically valid settings [2], while in health care wearable sensors enable continuous physiological monitoring at a relatively low cost [8,29].…”

Section: Introductionmentioning

confidence: 99%

Towards ecosystem for research and development of electrodermal activity applications

Jussila

Venho

Salonius

et al. 2018

Proceedings of the 22nd International Academic Mindtrek Conference

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Electrodermal activity is one of the most studied psychophysiological markers of the functioning of the autonomic nervous system and it has been applied in psychophysiological research for over 100 years. However, electrodermal activity measurement has not been largely applied in clinical research until now due to it being limited to laboratory environment before the entry of wearable devices. The aim of this study is to speed up research and development of electrodermal activity applications based on wearable device measurements. In order to reach that goal an ecosystem model is proposed that includes open data, open source, application programming interface and software development kit as central components. To illustrate the ecosystem model a case study of Moodmetric is presented.

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“…Djurić-Jovičić et al (17) developed SENSY system of interactive shoes inertial measurement units (IMU) that measure force distribution and stride parameters in gait analysis. Moodmetric® ring measures electro dermal activity of the skin, as an indicator of sympathetic activation (18) and the appropriate application, estimates the level of physiological and psychological arousal (19).…”

Section: Introductionmentioning

confidence: 99%

Deep learning approaches for human activity recognition using wearable technology

Janković¹,

Savić²,

Novičić³

et al. 2018

Medicinski podmladak

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The need for long-term monitoring of individuals in their natural environment has initiated the development of a various number of wearable healthcare sensors for a wide range of applications: medical monitoring in clinical or home environments, physical activity assessment of athletes and recreators, baby monitoring in maternity hospitals and homes etc. Neural networks (NN) are data-driven type of modelling. Neural networks learn from experience, without knowledge about the model of phenomenon, but knowing the desired "output" data for the training "input" data. The most promising concept of machine learning that involves NN is the deep learning (DL) approach. The focus of this review is on approaches of DL for physiological activity recognition or human movement analysis purposes, using wearable technologies. This review shows that deep learning techniques are useful tools for health condition prediction or overall monitoring of data, streamed by wearable systems. Despite the considerable progress and wide field of applications, there are still some limitations and room for improvement of DL approaches for wearable healthcare systems, which may lead to more robust and reliable technology for personalized healthcare. Keywords: deep learning, human activity monitoring, human machine interface, wearable sensors, smart sensors, multimodal interface Medicinski podmladak / Medical Youth Janković M. et al. Deep learning approaches for human activity recognition using wearable technology. MedPodml 2018, 69(3):14-24 15Potreba za dugotrajnim praćenjem osoba u njihovom svakodnevnom okruženju inicirala je razvoj velikog broja nosivih senzora (integrisanih u delove garderobe) s različitom primenom, kao što su medicinsko praćenje u kliničkim i kućnim uslovima, procena fizičke aktivnosti sportista i rekreativaca, praćenje beba u porodilištima i kućama i sl. Neuralne mreže (NM) predstavljaju tip modelovanja zasnovan na velikom broju podataka. Ove mreže uče na osnovu iskustva, bez poznavanja modela fenomena, ali znajući šta su željeni "izlazni" podaci za obučavajuće "ulazne" podatke. Koncept mašinskog učenja koji najviše obećava i uključuje NM jeste duboko učenje (DU). Fokus ovog preglednog rada je u pristupima DU u cilju prepoznavanja fizioloških aktivnosti i analize ljudskih pokreta primenom "odevne tehnologije". Ovaj rad pokazuje da su tehnike dubokog učenja korisne alatke za predikciju zdravstvenih stanja ili celokupno praćenje podataka koji se šalju sa "odevnih" senzora. Uprkos značajnom napretku i obećavajućoj oblasti primene, i dalje postoje ograničenja i prostor za unapređenje pristupa DU za "odevne" zdravstvene sisteme koji će dovesti do njihove pouzdane primene i omogućiti personalizovanu zdravstvenu zaštitu.Ključne reči: duboko učenje, praćenje ljudske aktivnosti, čovek-mašina interfejs, odevni senzori, pametni senzori, multimodalni interfejs

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Application for pre-processing and visualization of electrodermal activity wearable data

Cited by 8 publications

References 6 publications

Design of Mobile Application for Self-reporting Affective Experiences

Design of Mobile Application for Self-reporting Affective Experiences

Towards ecosystem for research and development of electrodermal activity applications

Deep learning approaches for human activity recognition using wearable technology

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