BandReader - A Mobile Application for Data Acquisition from Wearable Devices in Affective Computing Experiments

Kutt, Krzysztof; Nalepa, Grzegorz J.; Giżycka, Barbara; Jemioło, Paweł; Adamczyk, Marcin

doi:10.1109/hsi.2018.8431271

Cited by 16 publications

(8 citation statements)

References 17 publications

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“…However, their manufacturers often use lower-quality sensors to reduce the cost of such instruments. To address the need for comparing those devices and using them in studies, we created a software solution called BandReader, described in detail in [35] (see Figure 1), which enables us to collect data from wristbands and other non-medical platforms using Bluetooth technology and then broadcast the acquired statistics to all listening applications and devices.…”

Section: Results In the Analysis Of Emotional Context With Wearablmentioning

confidence: 99%

Analysis and Use of the Emotional Context with Wearable Devices for Games and Intelligent Assistants

Nalepa

Kutt

Giżycka

et al. 2019

Sensors

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In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. Furthermore, as a test-bed application for our work, we selected computer games. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. This framework provides means for implementing adaptive systems using mobile devices with wearable sensors.

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Section: Results In the Analysis Of Emotional Context With Wearablmentioning

confidence: 99%

Analysis and Use of the Emotional Context with Wearable Devices for Games and Intelligent Assistants

Nalepa

Kutt

Giżycka

et al. 2019

Sensors

Self Cite

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“…However, not all wearables have the same level of accuracy in getting the physiological signals [ 29 ]. There are devices ranges [ 10 , 26 ]: Expensive devices with high accuracy sensors, principally used for healthcare purposes, for instance, include the Emphatica E4 [ 11 , 13 , 28 , 31 ]. Medium range devices with precise sensors have more extensive use, targeted to high-performance athletes, mainly for fitness and sports, and these include, for example, Garmin and Microsoft Band [ 10 , 12 , 32 , 33 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are devices ranges [ 10 , 26 ]: Expensive devices with high accuracy sensors, principally used for healthcare purposes, for instance, include the Emphatica E4 [ 11 , 13 , 28 , 31 ]. Medium range devices with precise sensors have more extensive use, targeted to high-performance athletes, mainly for fitness and sports, and these include, for example, Garmin and Microsoft Band [ 10 , 12 , 32 , 33 ]. Affordable devices for all audiences are used for general purposes, for instance, the Xiaomi Mi Band [ 10 , 34 , 35 , 36 , 37 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Medium range devices with precise sensors have more extensive use, targeted to high-performance athletes, mainly for fitness and sports, and these include, for example, Garmin and Microsoft Band [ 10 , 12 , 32 , 33 ]. Affordable devices for all audiences are used for general purposes, for instance, the Xiaomi Mi Band [ 10 , 34 , 35 , 36 , 37 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wearables of a different range were also used, mainly medium and high-end. Most of these studies took biosignal measurements in controlled experiments [ 9 , 10 , 11 , 12 , 13 ]. As wristbands evolve, they integrate more sensors and with better accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

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The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.

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BandReader - A Mobile Application for Data Acquisition from Wearable Devices in Affective Computing Experiments

Cited by 16 publications

References 17 publications

Analysis and Use of the Emotional Context with Wearable Devices for Games and Intelligent Assistants

Analysis and Use of the Emotional Context with Wearable Devices for Games and Intelligent Assistants

Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

Multivariate Ovulation Window Detection at OvuFriend

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