6 papers
Menta: A Small Language Model for On-Device Mental Health Prediction
Tianyi Zhang, Xiangyuan Xue, Lingyan Ruan +6
Mental health conditions affect hundreds of millions globally, yet early detection remains limited. While large language models (LLMs) have shown promise in mental health applicati…
Behavioral Indicators of Loneliness: Predicting University Students' Loneliness Scores from Smartphone Sensing Data
Qianjie Wu, Tianyi Zhang, Hong Jia +1
Loneliness is a critical mental health issue among university students, yet traditional monitoring methods rely primarily on retrospective self-reports and often lack real-time beh…
AWARE Narrator and the Utilization of Large Language Models to Extract Behavioral Insights from Smartphone Sensing Data
Tianyi Zhang, Miu Kojima, Simon D'Alfonso
Smartphones, equipped with an array of sensors, have become valuable tools for personal sensing. Particularly in digital health, smartphones facilitate the tracking of health-relat…
AutoJournaling: A Context-Aware Journaling System Leveraging MLLMs on Smartphone Screenshots
Tianyi Zhang, Shiquan Zhang, Le Fang +3
Journaling offers significant benefits, including fostering self-reflection, enhancing writing skills, and aiding in mood monitoring. However, many people abandon the practice beca…
Predicting Affective States from Screen Text Sentiment
Songyan Teng, Tianyi Zhang, Simon D'Alfonso +1
The proliferation of mobile sensing technologies has enabled the study of various physiological and behavioural phenomena through unobtrusive data collection from smartphone sensor…
Leveraging LLMs to Predict Affective States via Smartphone Sensor Features
Tianyi Zhang, Songyan Teng, Hong Jia +1
As mental health issues for young adults present a pressing public health concern, daily digital mood monitoring for early detection has become an important prospect. An active res…