MAUS: A Dataset for Mental Workload Assessment Using Wearable Sensor - Baseline system

Getting started

To start working on this assignment, you should clone this repository into your local machine by using the following command.

git clone https://github.com/rickwu11/MAUS_dataset_baseline_system.git

Dependencies

Baseline system of MAUS requires the following:

• Python (>= 3.5)
• numpy >= 1.19.5
• scipy >= 1.5.4
• pandas >= 1.1.5
• matplotlib >=3.3.4
• statsmodels >= 0.12.2
• pyhrv >= 0.4.0
• biosppy >= 0.7.0
• EMD-signal >= 0.2.15

Dataset downloading

The MAUS dataset can be downloaded from: http://ieee-dataport.org/4216. Extract the .zip file under this folder.

Baseline system running

The extracted features were provided for classification under the folder: ./feature_data

Peak detection, extract inter-beat intervals (IBI)

python3 peak_detection.py --src_data 
   
     --dst_data 
    
      --single_sub 
     
       --sub_id 
      
        --rest_data 
        
       
      
     
    
   

: (str) Raw signal datapath; Default: ./MAUC/Data/Raw_data

: (str) Extract IBI sequence datapath; Default: ./MAUC/Data/

: (bool) Extract IBI sequence from single subject; Default: True

: (str) ID of the single subject; Default: 002

: (bool) Extract resting IBI sequence; Default: False

HRV features extraction

python3 HRV_feature_extraction.py --data 
   

   

: (str) Inter-beat Intervals (IBI) sequence path; Default: ./MAUC/Data/IBI_sequence/

Classification

python3 classification.py --data 
   
     --mode 
    

    
   

: (str) feature data path; Default: ./feature_data

: (str) validation type; Default: LOSO

• LOSO: leave-one-subject-out cross validation
• Mixed: mixed-subject 5-fold cross validation