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A Reproducible Stress Prediction Pipeline with Mobile Sensor Data

Authors:

Jumabek Alikhanov,

Uichin LeeAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 8, Issue 3

Article No.: 143, Pages 1 - 35

https://doi.org/10.1145/3678578

Published: 09 September 2024 Publication History

Abstract

Recent efforts to predict stress in the wild using mobile technology have increased; however, the field lacks a common pipeline for assessing the impact of factors such as label encoding and feature selection on prediction performance. This gap hinders replication, especially because of a lack of common guidelines for reporting results or privacy concerns that limit access to open codes and datasets. Our study introduces a common pipeline based on a comprehensive literature review and offers comprehensive evaluations of key pipeline factors, promoting independent reproducibility. Our systematic evaluation aimed to validate the findings of previous studies. We identified overfitting and distribution shifts across users as the major reasons for performance limitations. We used K-EmoPhone, a public dataset, for experimentation and a new public dataset---DeepStress---to validate the findings. Furthermore, our results suggest that researchers should carefully consider temporal order in cross-validation settings. Additionally, self-report labels for target users are key to enhancing performance in user-independent scenarios.

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Cited By

Han YZhang PPark MLee U(2024)Systematic Evaluation of Personalized Deep Learning Models for Affect RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997248:4(1-35)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699724
Zhang QLan YGuo KWang D(2024)Lipwatch: Enabling Silent Speech Recognition on Smartwatches using Acoustic SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596148:2(1-29)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659614
Zhang QLiu KWang D(2024)Sensing to Hear through MemoryProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595988:2(1-31)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659598

Index Terms

A Reproducible Stress Prediction Pipeline with Mobile Sensor Data
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

Index terms have been assigned to the content through auto-classification.

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 8, Issue 3

August 2024

1782 pages

EISSN:2474-9567

DOI:10.1145/3695755

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Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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New York, NY, United States

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Published: 09 September 2024

Published in IMWUT Volume 8, Issue 3

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Cited By

Han YZhang PPark MLee U(2024)Systematic Evaluation of Personalized Deep Learning Models for Affect RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997248:4(1-35)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699724
Zhang QLan YGuo KWang D(2024)Lipwatch: Enabling Silent Speech Recognition on Smartwatches using Acoustic SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596148:2(1-29)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659614
Zhang QLiu KWang D(2024)Sensing to Hear through MemoryProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595988:2(1-31)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659598

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