Overview

GLOBEM not only supports flexible and rapid evaluation of the existing behavior modeling methods, but also provides easy-to-extend templates for researchers to develop and prototype their own behavior modeling algorithms.

It splits the whole pipeline into three independent modules:

• The feature preparation module defines behavior features used by the algorithm.
• The model computation module defines how a behavior model is going to be trained. These two modules are determined by the core algorithm.
• The configuration module provides the flexibility to adjust hyperparameters in the algorithm.

Researchers and developers can re-use or re-purpose any of these modules to develop new algorithms within the pipeline. GLOBEM separates the configuration setup from the model definition, supporting easy testing and ablation studies of hyperparameters and different features.

Moreover, GLOBEM provides a series of generalization evaluation tasks to enable rapid testing of any algorithm.

Quick Start#

Try the plaform with one line of command, assuming Anaconda/miniconda is already installed on the machine. Please find the details of the setup and examples explained in the rest of tutorial.

git clone https://github.com/UW-EXP/GLOBEM.git
cd GLOBEM
/bin/bash run.sh

Available Algorithms#

The platform currently supports the task of depression detection and closely reimplements the following algorithms:

• Traditional Machine Learning Algorithm

  • Trajectories of depression: Unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis, by Canzian et al., 2015
  • Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study, by Saeb et al., 2015
  • Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data, by Farhan et al., 2016
  • Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild, by Wahle et al., 2016
  • Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning, by Lu et al., 2018
  • Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing, by Wang et al., 2018
  • Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students, by Xu et al., 2019
  • Leveraging Collaborative-Filtering for Personalized Behavior Modeling: A Case Study of Depression Detection among College Students, by Xu et al., 2021
  • Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing, by Chikersal et al., 2021

• Deep Learning Based Domain Generalization Algorithm

  • Empirical Risk Minimization (ERM)
  • Data Manipulation
    • Mixup, by Zhang et al., 2018
  • Representation Learning
    • Domain-Adversarial Neural Network (DANN), by Ganin et al., 2017
    • Invariant Risk Minimization (IRM), by Arjovsky et al., 2020
    • Common Specific Decomposition (CSD), by Piratla et al., 2020
  • Learning Strategy
    • Meta-Learning for Domain Generalization (MLDG), by Li et al., 2017
    • Model-Agnostic Learning of Semantic Features (MASF), by Dou et al., 2019
    • Siamese Network, by Koch et al., 2015
    • Reorder, by Xu et al., 2022