_{Imperial College London
Dyson School of Design Engineering}

Detecting Subclinical Social Anxiety Using Physiological Data from a Wrist-worn Wearable: A Small-Scale Feasibility Study

1. Overview

This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including Heart Rate (HR), Skin Temperature (ST) and Electrodermal Activity (EDA). This repository contains the full dataset and code needed to recreate the classification models and reproduce the results within the paper; as well as this the Python notebooks contain functions that enable further experimentation.

1.1. Nature of the dataset

The dataset consists of Heart Rate (HR) data, Skin Temperature (ST) data and Electrodermal Activity (EDA) data. This physiological data was collected using an E4 Empatica wearable device.

This data was collected during impromptu speech tasks involving young adults (N = 12) with self-reported subclinical social anxiety (measured by the widely used self-reported version of the Liebowitz Social Anxiety Scale, LSAS-SR).

1.2. Description of the ML experiments

Using the pre-processed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine, Decision Tree, Random Forest and K-Nearest Neighbours (KNN) were used to develop models for three different contexts. Furthermore, the following experiments were carried out using datasets with all modalities combined, as well as datasets with only singular modalities.

Experiment (1) investigates whether models can be trained to classify between baseline and socially anxious states.

Experiment (2) investigates whether models can be trained to differentiate between baseline, anticipation anxiety and reactive anxiety states.

Experiment (3) investigates whether models can be trained to classify between social anxiety experienced by individuals with differing social anxiety severity.

Results & Evaluation The models were then evaluated using 10-fold cross validation as a performance index. Confusion matrices were also used to examine the classification accuracies per class.

Note: In order to reproduce the results in the report, please set the argument repeat = True in the functions within the Supervised_Machine_Learning_Notebook.ipynb.

2. Repository structure

2.1. Data Preprocessing Folder

The repository contains Jupyter notebooks used to preprocess each participant's data in the data_preprocessing folder, within this folder there is a folder for each participant; each participant folder contains csv files of raw data and a notebook that preprocesses the data, called Data_Preprocessing.ipynb. Each participant folder also contains datasets for each experiment e.g experiment_1.csv that consist of previously preprocessed data so that the main notebook Supervised_Machine_Learning_Notebook.ipynb can execute without running each participant's preprocessing notebook.

data_preprocessing/
├── 1/                                  # folder for participant 1
│   ├── Data_Preprocessing.ipynb        # preprocessing notebook
│   ├── experiment_1.csv                # previously outputted preprocessed dataset for experiment (1)
│   ├── experiment_2.csv                # previously outputted preprocessed dataset for experiment (2)
│   ├── experiment_3.csv                # previously outputted preprocessed dataset for experiment (3)
│   ├── HR.csv                          # HR dataset for participant 1
│   ├── EDA.csv                         # EDA dataset for participant 1
│   └── ST.csv                          # ST dataset for participant 1
│
├── 2/ ...                              # folder for participant 2

2.2. Supervised ML (Data Processing) Folder

The repository contains a main Jupyter notebook called Supervised_Machine_Learning_Notebook.ipynb used for executing the three different supervised ML experiments. The notebook is in the data_processing folder. The folder also contains datasets for each ML experiment e.g. experiment_1_randomised_data.csv, these datasets consist of participants' preprocessed data that have been previously randomised. These datasets are used when an individual desires to reproduce the models and results from the report, this can be intiated when the argument repeat = True in any of the functions within Supervised_Machine_Learning_Notebook.ipynb; this was done because an actively randomised dataset may result in slightly differing results, compared to reusing the exact dataset used to create the results.

data_processing/                                  
├── Supervised_Machine_Learning_Notebook.ipynb      # supervised ML notebook
├── experiment_1_randomised_data.csv                # previously randomised dataset for experiment (1)
├── experiment_2_randomised_data.csv                # previously randomised dataset for experiment (2)
└── experiment_3_randomised_data.csv                # previously randomised dataset for experiment (3)   

3. Instructions

You can view the notebooks with their outputs in this Github without running the notebooks. However, if you would like to run the notebooks firsthand, you must first download a zip file of this repository, then you must download Anaconda Navigator. Following the download, you must launch Anaconda Navigator and click 'Jupyter Notebook'. Once Jupyter Notebook is launched, navigate to where the extracted zip folder is and open a notebook of your choice. You must then download each dependency (see below). After downloading the dependencies you should be able to run any of the notebooks.

Dependencies:

The notebooks utilise NumPy, Matplotlib, Seaborn, Scikit-learn and Pandas dependencies, therefore they must be downloaded before using the notebooks. Note: Matplotlib version 3.1.0 was installed as there is a bug in the latest version that affects the rendering of seaborn plots

 pip install numpy
 pip install matplotlib==3.1.0
 pip install seaborn
 pip install scikit-learn
 pip install pandas

4. References

This project was powered by:

• Scikit Learn
• Matplotlib
• Pandas
• NumPy
• Seaborn

5. Quick Links

Supervised ML notebook

An example of a preprocessing notebook