KAIST reads “stress” from smartphone chatting, searching, and payment habits
Reports and Proceedings
Updates every hour. Last Updated: 20-Oct-2026 15:17 ET (20-Oct-2026 19:17 GMT/UTC)
- Professor Uichin Lee’s team in the Department of AI Computing develops a technology that identifies daily stress from repeated behaviors within smartphone apps
- Analysis of data from 26 people in their 20s and 30s shows that personalized models achieve an average accuracy of about 75%
- Analyzing only a few behaviors, such as chatting and payment, still outperforms the conventional approach, showing potential for long-term monitoring
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Scleroderma (systemic sclerosis) is a rare, chronic, autoimmune disease that causes the body to produce too much collagen, leading to the hardening and thickening of the skin and internal organs. For more than 50 years, physicians have relied on manually pinching the skin to assess its thickness, a subjective approach that remains central to the diagnosis of systemic sclerosis, assessment of disease extent and progression, and monitoring of response to treatment. Despite being the accepted standard practice, doctors can score the same patient differently based on their own unique touch and judgement, making it difficult to accurately track changes in skin thickening over time or assess a patient’s response to treatment.
For the first time, researchers at Boston University Chobanian & Avedisian School of Medicine and the Boston University Department of Biomedical Engineering have developed and tested a camera-based technique called spatial frequency domain imaging (SFDI) to assess skin changes in scleroderma patients. SFDI works by shining patterns of harmless light on the skin, without touching it, and measuring how the light bounces back. “We have been developing SFDI for almost 15 years in my lab, but this is the first time anyone has applied this technology to scleroderma” explained Darren Roblyer, PhD, professor of biomedical engineering and co-author on the manuscript.