K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

Kang, Soowon; Choi, Woohyeok; Park, Cheul Young; Cha, Narae; Kim, Auk; Khandoker, Ahsan H.; Hadjileontiadis, Leontios; Kim, Heepyung; Jeong, Yong Hoon; Lee, Uichin

doi:10.1038/s41597-023-02248-2

Cited by 6 publications

(3 citation statements)

References 82 publications

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“…Emotions are complex aspects of the human experience 1 that are recognized, analyzed, and comprehended in the field of Affective Computing (AC) 2 . Recent research in AC has been frequently addressing influence of context in which emotions arise as a determining factor 3 .…”

Section: Background and Summarymentioning

confidence: 99%

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EmoWear: Wearable Physiological and Motion Dataset for Emotion Recognition and Context Awareness

Rahmani,

Symons,

Sobhani

et al. 2024

Sci Data

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The EmoWear dataset provides a bridge to explore Emotion Recognition (ER) via Seismocardiography (SCG), the measurement of small cardio-respiratory induced vibrations on the chest wall through Inertial Measurement Units (IMUs). We recorded Accelerometer (ACC), Gyroscope (GYRO), Electrocardiography (ECG), Blood Volume Pulse (BVP), Respiration (RSP), Electrodermal Activity (EDA), and Skin Temperature (SKT) data from 49 participants who watched validated emotionally stimulating video clips. They self-assessed their emotional valence, arousal, and dominance, as well as extra questions about the video clips. Also, we asked the participants to walk, talk, and drink, so that researchers can detect gait, voice, and swallowing using the same IMU. We demonstrate the effectiveness of emotion stimulation with statistical methods and verify the quality of the collected signals through signal-to-noise ratio and correlation analysis. EmoWear can be used for ER via SCG, ER during gait, multi-modal ER, and the study of IMUs for context-awareness. Targeted contextual information include emotions, gait, voice activity, and drinking, all having the potential to be sensed via a single IMU.

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Section: Background and Summarymentioning

confidence: 99%

“…Among AC datasets that incorporate IMU sensor data, none are made for investigation of SCG in the field of AC. BIRAFFE2 25 offers ACC data collected from a gamepad; K-EmoCon 36 , K-EmoPhone 2 , and Schmidt et al . 32 collect their ACC data only from the wrist.…”

Section: Background and Summarymentioning

confidence: 99%

EmoWear: Wearable Physiological and Motion Dataset for Emotion Recognition and Context Awareness

Rahmani,

Symons,

Sobhani

et al. 2024

Sci Data

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“…1) Collection Procedure: The data in the K-EmoPhone [22] dataset were collected from 79 participants who used Android phones over the course of one week. The participants were educated through a session explaining details pertaining to the data collection and equipment.…”

Section: B Data Collectionmentioning

confidence: 99%

Design of Contextual Filtered Features for Better Smartphone-User Receptivity Prediction

Alikhanov,

Zhang,

Noh

et al. 2024

IEEE Internet Things J.

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For the successful engagement of users with incoming interventions, the delivery of Just-In-Time (or opportune moment-OM) interventions is of the utmost importance. This can be accomplished with a machine learning model that utilizes user context data. Smartwatch and smartphone interactions further open the space for further improvement by considering the user's state and environmental information. In this work, a novel feature engineering approach for predicting smartphone user receptivity to Just-In-Time (JIT) interventions is proposed. Proposed approach utilizes rich information from user-smartphone interactions and smartwatch sensor data to extract contextually filtered features. The superiority of the proposed feature engineering method is demonstrated by developing a machine learning model that predicts a participant's receptivity prior to administering an Experience Sampling Method (ESM) questionnaire. The proposed approach is evaluated using the KEmoPhone dataset, which contains 3,334 ESM answers collected over the course of one week from 73 participants. To reduce the bias that may result in selecting specific classifier, multiple classifiers are tested and their average ROC-AUC metrics are compared. The results show that the proposed feature engineering method -CFF improves the prediction performance, achieving an ROC-AUC of 56.5% as opposed to 54.7% obtained using conventional features (statistical moments of time series over an 80 min window). Such superior performance is consistent across multiple machine learning classification algorithms. Full research code will be released in jupyter notebooks for facilitating the research in the domain at https://github.com/Jumabek/receptivity upon the publication of this research work.

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K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

Kang

Choi

Park³

et al. 2023

Sci Data

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With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone, a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals’ smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.

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K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

Cited by 6 publications

References 82 publications

EmoWear: Wearable Physiological and Motion Dataset for Emotion Recognition and Context Awareness

EmoWear: Wearable Physiological and Motion Dataset for Emotion Recognition and Context Awareness

Design of Contextual Filtered Features for Better Smartphone-User Receptivity Prediction

K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

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