# GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization

arXiv: https://arxiv.org/abs/2211.02733 · HTML: https://mlsanity.com/paper/2211.02733/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 145 references that are in the map.
- First posted on arXiv 4 Nov 2022.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Monash Time Series Forecasting Archive](https://mlsanity.com/paper/2105.06643/) (2021)
- [Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data](https://mlsanity.com/paper/1909.00889/) (2019)
- [Episodic Training for Domain Generalization](https://mlsanity.com/paper/1902.00113/) (2019)
- [Simultaneous Deep Transfer Across Domains and Tasks](https://mlsanity.com/paper/1510.02192/) (2015)
- [Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization](https://mlsanity.com/paper/2104.05833/) (2021)
- [Efficient Domain Generalization via Common-Specific Low-Rank Decomposition](https://mlsanity.com/paper/2003.12815/) (2020)
- [AdaRNN: Adaptive Learning and Forecasting of Time Series](https://mlsanity.com/paper/2108.04443/) (2021)
- [Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models](https://mlsanity.com/paper/1909.09020/) (2019)
- [Learning Causal Semantic Representation for Out-of-Distribution Prediction](https://mlsanity.com/paper/2011.01681/) (2020)
- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Meta-Learning for Domain Generalization in Semantic Parsing](https://mlsanity.com/paper/2010.11988/) (2020)
- [HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data](https://mlsanity.com/paper/2111.08536/) (2021)
- [WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series](https://mlsanity.com/paper/2203.09978/) (2022)
- [Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization](https://mlsanity.com/paper/2002.05660/) (2020)
- [Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables](https://mlsanity.com/paper/2105.06637/) (2021)
- [How Does COVID-19 impact Students with Disabilities/Health Concerns?](https://mlsanity.com/paper/2005.05438/) (2020)
- [Examining Needs and Opportunities for Supporting Students Who Experience Discrimination](https://mlsanity.com/paper/2111.13266/) (2021)

## Built on by (most cited first, 28 of 28)

- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data](https://mlsanity.com/paper/2401.06866/) (2024)
- [Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis](https://mlsanity.com/paper/2309.12368/) (2023)
- [MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention](https://mlsanity.com/paper/2309.16639/) (2023)
- [LifeSim: Long-Horizon User Life Simulator for Personalized Assistant Evaluation](https://mlsanity.com/paper/2603.12152/) (2026)
- [MindGuard: Towards Accessible and Sitgma-free Mental Health First Aid via Edge LLM](https://mlsanity.com/paper/2409.10064/) (2024)
- [Phase-driven Domain Generalizable Learning for Nonstationary Time Series](https://mlsanity.com/paper/2402.05960/) (2024)
- [Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention](https://mlsanity.com/paper/2403.05584/) (2024)
- [Using Self-supervised Learning Can Improve Model Fairness](https://mlsanity.com/paper/2406.02361/) (2024)
- [Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Models](https://mlsanity.com/paper/2602.02039/) (2026)
- [Wearable Foundation Models Should Go Beyond Static Encoders](https://mlsanity.com/paper/2603.19564/) (2026)
- [A Systematic Review of NeurIPS Dataset Management Practices](https://mlsanity.com/paper/2411.00266/) (2024)
- [Explaining GPTs' Schema of Depression: A Machine Behavior Analysis](https://mlsanity.com/paper/2411.13800/) (2024)
- [Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model](https://mlsanity.com/paper/2309.09357/) (2023)
- [SeSaMe: A Framework to Simulate Self-Reported Ground Truth for Mental Health Sensing Studies](https://mlsanity.com/paper/2403.17219/) (2024)
- [Imputation Matters: A Deeper Look into an Overlooked Step in Longitudinal Health and Behavior Sensing Research](https://mlsanity.com/paper/2412.06018/) (2024)
- [A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting using Smartphone Sensing Data](https://mlsanity.com/paper/2601.03603/) (2026)
- [SensorPersona: An LLM-Empowered System for Continual Persona Extraction from Longitudinal Mobile Sensor Streams](https://mlsanity.com/paper/2604.06204/) (2026)
- [HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices](https://mlsanity.com/paper/2607.21019/) (2026)
- [ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality](https://mlsanity.com/paper/2310.16242/) (2023)
- [Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling](https://mlsanity.com/paper/2508.17090/) (2025)
- [AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data](https://mlsanity.com/paper/2510.02558/) (2025)
- [Student Mental Health Screening via Fitbit Data Collected During the COVID-19 Pandemic](https://mlsanity.com/paper/2601.16324/) (2026)
- [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)
- [A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health](https://mlsanity.com/paper/2606.14604/) (2026)
- [Evidence-Grounded Mapping of Multimodal Human Sensing Psychological Transdiagnostic Dimensions](https://mlsanity.com/paper/2608.24903/) (2026)
- [On-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health](https://mlsanity.com/paper/2609.11961/) (2026)

## Abstract (arXiv)

Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single population within a short period, without measuring the cross-dataset generalizability of these algorithms. We present the first multi-year passive sensing datasets, containing over 700 user-years and 497 unique users' data collected from mobile and wearable sensors, together with a wide range of well-being metrics. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms' generalizability across different users and years. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our results indicate that both prior depression detection algorithms and domain generalization techniques show potential but need further research to achieve adequate cross-dataset generalizability. We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.