WearableSensorData

This repository provides the codes and data used in our paper "Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art", where we implement and evaluate several state-of-the-art approaches, ranging from handcrafted-based methods to convolutional neural networks. Also, we standardize a large number of datasets, which vary in terms of sampling rate, number of sensors, activities, and subjects.

Requirements

• Scikit-learn
• Keras (Recommended version 2.1.2)
• Tensorflow (Recommended version 1.3.0)
• Python 3

Quick Start

1. Clone this repository
2. Run

   python <Catal2015|...|ChenXue2015>.py data/<SNOW|FNOW|LOTO|LOSO>/<MHEALTH|USCHAD|UTD-MHAD1_1s|UTD-MHAD2_1s|WHARF|WISDM>.npz
   

   For example

   python Catal2015.py data/LOSO/MHEALTH.npz
   

Data Format

The raw signal provided by the original dataset was segmented by using a temporal sliding window of 5 seconds. Its format is (number of samples, 1, temporal window size, number of sensors)

Contributing

Contributions to this repository are welcome. Examples of things you can contribute:

• Implementation of other methods. See template_hancrafted.py and template_convNets.py
• Accuracy Improvements.
• Reporting bugs.

The table below shows the mean accuracy achieved by the methods using the Leave-One-Subject-Out (LOSO) as validation protocol. The symbol 'x' denotes which was not possible to execute the method on the respective dataset.

| Method | MHEALTH | PAMAP2 | USCHAD | UTD-MHAD1 | UTD-MHAD2 | WHARF | WISDM | Mean Accuracy | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | Kwapisz et al. | 90.41 | 71.27 | 70.15 | 13.04 | 66.67 | 42.19 | 75.31 | 61.29 | | Catal et al. | 94.66 | 85.25 | 75.89 | 32.45 | 74.67 | 46.84 | 74.96 | 69.29 | | Kim et al. | 93.90 | 81.57 | 64.20 | 38.05 | 64.60 | 51.48 | 50.22 | 63.43 | | Chen and Xue | 88.67 | 83.06 | 75.58 | x | x | 61.94 | 83.89 | 78.62 | | Jiang and Yin | 51.46 | x | 74.88 | x | x | 65.35 | 79.97 | 67.91 | | Ha et al. | 88.34 | 73.79 | x | x | x | x | x | 81.06 | | Ha and Choi | 84.23 | 74.21 | x | x | x | x | x | 79.21| | Mean Accuracy | 84.52 | 78.19 | 72.14 | 27.84 | 68.64 | 53.55 | 72.87 | x |

Please cite our paper in your publications if it helps your research.

@article{Jordao:2018,
author    = {Artur Jordao,
Antonio Carlos Nazare,
Jessica Sena and
William Robson Schwartz},
title     = {Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art},
journal   = {arXiv},
year      = {2018},
eprint    = {1806.05226},
}