# Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting

arXiv: https://arxiv.org/abs/2403.13841 · HTML: https://mlsanity.com/paper/2403.13841/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances](https://mlsanity.com/paper/2203.06935/) (2022)
- [Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection](https://mlsanity.com/paper/2402.10862/) (2024)
- [ZotCare: A Flexible, Personalizable, and Affordable mHealth Service Provider](https://mlsanity.com/paper/2307.01905/) (2023)

## Built on by (most cited first, 7 of 7)

- [HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations](https://mlsanity.com/paper/2409.19487/) (2024)
- [Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis](https://mlsanity.com/paper/2406.16252/) (2024)
- [Personalized Counterfactual Framework: Generating Potential Outcomes from Wearable Data](https://mlsanity.com/paper/2508.14432/) (2025)
- [Transformer-Based Decomposition of Electrodermal Activity for Real-World Mental Health Applications](https://mlsanity.com/paper/2506.06378/) (2025)
- [Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data](https://mlsanity.com/paper/2602.15478/) (2026)
- [Personalized Digital Health Modeling with Adaptive Support Users](https://mlsanity.com/paper/2605.02004/) (2026)
- [Explainable Prediction from Mobile Sensing Data through LLM-guided Concept Integration](https://mlsanity.com/paper/2609.11995/) (2026)

## Abstract (arXiv)

Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial. Current studies are primarily centered on immediate short-term affect detection using data from wearable and mobile devices. These studies typically focus on objective sensory measures, often neglecting other forms of self-reported information like diaries and notes. In this paper, we propose a multimodal deep learning model for affect status forecasting. This model combines a transformer encoder with a pre-trained language model, facilitating the integrated analysis of objective metrics and self-reported diaries. To validate our model, we conduct a longitudinal study, enrolling college students and monitoring them over a year, to collect an extensive dataset including physiological, environmental, sleep, metabolic, and physical activity parameters, alongside open-ended textual diaries provided by the participants. Our results demonstrate that the proposed model achieves predictive accuracy of 82.50% for positive affect and 82.76% for negative affect, a full week in advance. The effectiveness of our model is further elevated by its explainability.