The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
StressSense: detecting stress in unconstrained acoustic environments using smartphones
Open accessAbstract
Stress can have long term adverse effects on individuals' physical and mental well-being. Changes in the speech production process is one of many physiological changes that happen during stress. Microphones, embedded in mobile phones and carried ubiquitously by people, provide the opportunity to continuously and non-invasively monitor stress in real-life situations. We propose StressSense for unobtrusively recognizing stress from human voice using smartphones. We investigate methods for adapting a one-size-fits-all stress model to individual speakers and scenarios. We demonstrate that the StressSense classifier can robustly identify stress across multiple individuals in diverse acoustic environments: using model adaptation StressSense achieves 81% and 76% accuracy for indoor and outdoor environments, respectively. We show that StressSense can be implemented on commodity Android phones and run in real-time. To the best of our knowledge, StressSense represents the first system to consider voice based stress detection and model adaptation in diverse real-life conversational situations using smartphones.
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Cites
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Cited by
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T. (2014) · Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2632048.2632054 · Cited by 1,116
Self-citationPersonal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802
Review on Psychological Stress Detection Using Biosignals
Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., & Tsiknakis, M. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2927337 · Cited by 764
Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616650285 · Cited by 515
Self-citationCanzian, L., & Musolesi, M. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2805845 · Cited by 477
Metadata
- Authors
- Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., Gatica-Perez, D., & Choudhury, T.
- Venue
- Proceedings of the 2012 ACM Conference on Ubiquitous Computing
- Publisher
- ACM
- Published
- 2012-09-05
- Pages
- 351-360
- OpenAlex
- W2144279833
- Cited by
- 511
- References
- 31
- FWCI
- 85.46
- Type
- conference paper
- Language
- en
- Open access
- green
- Funders
- Division of Information and Intelligent Systems (IIS 1202141)
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- Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., Gatica-Perez, D., & Choudhury, T. (2012). StressSense: detecting stress in unconstrained acoustic environments using smartphones. Proceedings of the 2012 ACM Conference on Ubiquitous Computing. https://doi.org/10.1145/2370216.2370270
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