Design of Sensor Systems for Long Time Electrodermal Activity Monitoring

Vavrinský, Erik; Stopjaková, Viera; Donoval, Martin; Daricek, Martin; Svobodova, Hélèna; Mihalov, Jozef; Hanic, Michal; Tvarožek, V.

doi:10.15598/aeee.v15i2.2021

Cited by 5 publications

(3 citation statements)

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“…Similarly, Van der Mee 2021 (Van der Mee et al 2021) found modest but significant within-subject correlations between palm-based and wrist-based EDA (r=0.31 for SCL and r=0.42 for non-specific SCRs). Vavrinsky et al (2017) developed an 'EDA ring' for integration into smart clothes, and Torniainen et al (2015) demonstrated that a small ring sensor could obtain recordings similar to a laboratory-grade finger EDA measurement, obtaining an average cosine similarity of 83%. For sensor applications integrated into shoes or socks, EDA at the feet correlate well with palmar EDA (Kappeler-Setz et al 2013, Kasos et al 2019, Sanchez-Comas et al 2021.…”

Section: Skin Sites For Wearable Devicesmentioning

confidence: 99%

Current trends and opportunities in the methodology of electrodermal activity measurement

Tronstad¹,

Amini²,

Bach³

et al. 2022

Physiol. Meas.

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Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.

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Section: Skin Sites For Wearable Devicesmentioning

confidence: 99%

Current trends and opportunities in the methodology of electrodermal activity measurement

Tronstad¹,

Amini²,

Bach³

et al. 2022

Physiol. Meas.

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“…We tried to avoid these problems by constructing an EDA Holter in the form of a practical ring [124] (Figure 8), which does not sense the EDA between the fingers like a conventional device, but locally on a small place in an area of 1 x 1 cm (Figure 9a). Such an EDA ring is more suitable for daily wear and the signal artifacts are lower.…”

Section: Electrodermal Activity (Eda)mentioning

confidence: 99%

Application of Modern Multi-Sensor Holter in Diagnosis and Treatment

Vavrinský

Subjak

Donoval

et al. 2020

Sensors

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Modern Holter devices are very trendy tools used in medicine, research, or sport. They monitor a variety of human physiological or pathophysiological signals. Nowadays, Holter devices have been developing very fast. New innovative products come to the market every day. They have become smaller, smarter, cheaper, have ultra-low power consumption, do not limit everyday life, and allow comfortable measurements of humans to be accomplished in a familiar and natural environment, without extreme fear from doctors. People can be informed about their health and 24/7 monitoring can sometimes easily detect specific diseases, which are normally passed during routine ambulance operation. However, there is a problem with the reliability, quality, and quantity of the collected data. In normal life, there may be a loss of signal recording, abnormal growth of artifacts, etc. At this point, there is a need for multiple sensors capturing single variables in parallel by different sensing methods to complement these methods and diminish the level of artifacts. We can also sense multiple different signals that are complementary and give us a coherent picture. In this article, we describe actual interesting multi-sensor principles on the grounds of our own long-year experiences and many experiments.

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“…Nevertheless, there are three main problems: foreign-body sensation when wearing, requiring calibration, and data drift. One reason for the widespread use of inertial sensor units (IMUs) is that wearing foreign body sensations is severely constrained, and researchers are looking for ways to collect physiological data in ways that make subjects more comfortable [12,13]. In recent years, the use of intelligent wearable sensors with integrated flexible sensors to monitor human health state has aroused widespread interest [14,15].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Human Muscle State with Flexible Sensors

Chen

Wang²,

et al. 2022

Journal of Sensors

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Analyzing human muscle states has attracted extensive attention. EMG (electromyography) pattern recognition methods based on these works have been proposed for many years. However, uncomfortable wearing and high prices make it inconvenient for motion tracking and muscle analysis by using robotic arms and inertial sensors in daily life. In this study, we propose to use smart clothes integrated with flexible sensors to collect arm motion data, estimate the kinematic information of continuous arm motion, and predict the EMG signal of each arm muscle. Firstly, the neural network regression model integrated with the LSTM (long short-term memory) module is used to continuously estimate the sensor resistances collected by the smart clothes and the angles collected by Kinect. Then, six types of shoulder and elbow movements’ angles and the corresponding EMG signals of 5 subjects are preprocessed and aligned. The stacked regression model based on extremely randomized trees (extra trees) is used for regression. Our experimental results show that the average estimation absolute error from the sensor resistances to the joint angle is 3.45 degrees, and the absolute percentage error from the joint angle to the EMG signal is only 1.82%.

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Design of Sensor Systems for Long Time Electrodermal Activity Monitoring

Cited by 5 publications

References 0 publications

Current trends and opportunities in the methodology of electrodermal activity measurement

Current trends and opportunities in the methodology of electrodermal activity measurement

Application of Modern Multi-Sensor Holter in Diagnosis and Treatment

Analyzing Human Muscle State with Flexible Sensors

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