Measuring electrodermal activity to capture engagement in an afterschool maker program

In this paper, we describe a new approach for exploring individual participants\u27 engagement in youth maker activities. Participants were outfitted with wearable first person point-of-view still-image cameras and wrist-based electrodermal sensors. The researchers analyzed the recorded electrodermal data stream for surges in skin conductivity and compared them with the corresponding photographs based on their time-stamp. In following with prior work, these surges were interpreted as moments of engagement. A comparison sample was created to look at moments that lacked this psychophysiological marker. Results indicated that the two participants had both shared and divergent engagement with activities such as soldering, assembling, and programming

Age-Related Changes in Attention During Motor Learning

Theories of motor learning predict that humans require high levels of attention to perform new motor tasks, but little to no attention for those that are well-learned. Thus, practicing a task may decrease the amount of attention required to perform it. To test this theoretical relationship between attention and task practice, we used a physiological proxy for attention known as electrodermal activity (EDA). We hypothesized that 1) EDA (proxy for attention) would decrease over the course of training and that 2) attention would be higher overall in older adults than in younger adults when performing the same task. This second hypothesis was based on the tendency for older adults to require more attention than younger adults during a given motor task. Two groups of participants (young adult- n=S; mean± SD age=22.4 ± 2.1 yrs vs. older adult- n=S; age=75.8 ± 5.2 yrs) practiced 150 trials of a novel upper extremity task over three days. During each trial, we measured 1) task performance, defined as movement time (seconds, ors) and 2) EDA (μSiemens, or μS) using wrist-worn sensors. Contrary to our first hypothesis, EDA increased with practice, suggesting that additional training may be necessary to reduce the task\u27 s attentional requirements. Results did, however, support our second hypothesis, with higher EDA in older adults compared to younger adults throughout practice. This suggests that older adults may use more attention than younger adults to perform a given task in order to compensate for other age-related declines in sensorimotor function

Assessment of Biosignals for Managing a Virtual Keyboard

In this paper we propose an assessment of biosignals for handling an application based on virtual keyboard and automatic scanning. The aim of this work is to measure the effect of using such application, through different interfaces based on electromyography and electrooculography, on cardiac and electrodermal activities. Five people without disabilities have been tested. Each subject wrote twice the same text using an electromyography interface in first test and electrooculography in the second one. Each test was divided into four parts: instruction, initial relax, writing and final relax. The results of the tests show important differences in the electrocardiogram and electrodermal activity among the parts of tests.Junta de Andalucía p08-TIC-363

Wavelet-based motion artifact removal for electrodermal activity

Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data

Gamification or Gaming Techniques Applied to Pedagogy Foundations of the Cognitive Neuroscience Applied to the Education

The game in addition to a ludic activity has didactic applications in different stages of the learning process of a subject The game has components and pedagogic cultural social emotional and neurocognitive significances which position it as an educational resource of excellence when designing teaching strategies The aim of this article was to describe the foundations of the gamification applied to teaching from the perspective of the cognitive neuroscience with a focus in the recent developments which provide the studies of neuroimages and neurophysiology and its utilization in the classroom environmen

A Lifelogging Platform Towards Detecting Negative Emotions in Everyday Life using Wearable Devices

Repeated experiences of negative emotions, such as stress, anger or anxiety, can have long-term consequences for health. These episodes of negative emotion can be associated with inflammatory changes in the body, which are clinically relevant for the development of disease in the long-term. However, the development of effective coping strategies can mediate this causal chain. The proliferation of ubiquitous and unobtrusive sensor technology supports an increased awareness of those physiological states associated with negative emotion and supports the development of effective coping strategies. Smartphone and wearable devices utilise multiple on-board sensors that are capable of capturing daily behaviours in a permanent and comprehensive manner, which can be used as the basis for self-reflection and insight. However, there are a number of inherent challenges in this application, including unobtrusive monitoring, data processing, and analysis. This paper posits a mobile lifelogging platform that utilises wearable technology to monitor and classify levels of stress. A pilot study has been undertaken with six participants, who completed up to ten days of data collection. During this time, they wore a wearable device on the wrist during waking hours to collect instances of heart rate (HR) and Galvanic Skin Resistance (GSR). Preliminary data analysis was undertaken using three supervised machine learning algorithms: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Decision Tree (DT). An accuracy of 70% was achieved using the Decision Tree algorithm