Title | ||
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Passive Detection of Perceived Stress Using Location-driven Sensing Technologies at Scale |
Abstract | ||
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Much research argues that feeling overwhelmed by stress and for prolonged periods can lead to severe mental illness such as early onset depression and anxiety among many others. Recovering from severe stress to a normal state is much easier, in terms of the length of time and treatment required, compared to when more serious conditions have manifested [1]. Unfortunately, existing stress monitoring applications either require dedicated applications to be installed on the user's mobile device or use various mobile and wearable sensors [2, 4, 6, 7]; thus are not scalable to large number of users. Our goal is to provide a community-wide "safety net" that will automatically and non-intrusively detect individuals exhibiting signs of excessive stress without them installing any dedicated app. YouTube Demo Link https://youtu.be/LKQvIX4W6L0
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Year | DOI | Venue |
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2019 | 10.1145/3307334.3328574 | Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services |
Keywords | DocType | ISBN |
machine learning, mental health, stress, wifi indoor localisation | Conference | 978-1-4503-6661-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Camellia Zakaria | 1 | 0 | 1.01 |
Youngki Lee | 2 | 832 | 70.33 |
Rajesh Balan | 3 | 19 | 2.31 |