General Description

This code is for paper "Fall Detection Based on Multi-Horizon Forecasting"( Currently under review )

A fall detection method with multi-horizon forecasting usring Temporal Fusion Transformers and other deep learning methods.

For deep methods, 1D CNN, single LSTM, stacked LSTM were used.

All models were configured to forecast falls through the window size of data from the perspective of regression instead of classification.

For the last predicted value, the class of the predicted values was classified on the basis of the threshold value.

To verify benchmark performance, we faithfully reproduced the 1D CNN and LSTM-basedmodels, although the model structures were modified to enable regression in both cases because they were tailored to the classification task.

1D CNN model architecture is based on the model structure proposed in 2020 by Kraft et al(paper).

Signle LSTM and stacked LSTM model is based on the model architecture proposed in 2019 by Luna et al (paper).

Multi-horizon forecasting result

Prediction results for the SmartFall, Notch, DLR and MobiAct datasets in order using:

(a)-(d) TFT method, (e)-(h) Single LSTM, (i)-(l) Stacked LSTM, (m)-(p) 1D CNN

Download Dataset

For SmartFall and Notch dataset, I have uploaded zip files in dataset/. You can also download data through the link below.

SmartFall and Notch dataset

dataset url - https://userweb.cs.txstate.edu/~hn12/data/SmartFallDataSet/

paper - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210545/

DLR dataset

dataset url - https://www.dlr.de/kn/en/desktopdefault.aspx/tabid-12705/22182_read-50785/

MobiAct dataset

dataset url - https://bmi.hmu.gr/the-mobifall-and-mobiact-datasets-2/

How to use

Data Preprocess

Please download all datasets through the URL or zip files provided in dataset/.

SmartFall Dataset

1. Put dataset into directory named dataset/SmartFall_Dataset/.
2. For SmartFall dataset, preprocessing codes are included in ipynb files.

Notch Dataset

1. Put dataset into directory named dataset/Notch_Dataset/
2. For Notch dataset, preprocessing codes are included in ipynb files.

DLR Dataset

1. Put dataset into directory named dataset/ARS DLR Data Set/.
2. Use dataset/DLR_preprocess.ipynb for preprocessing. Run all cells in the ipynb file.
3. Save all preprocessed files in dataset/dlr_preprocessed.

MobiAct Dataset

1. Put dataset into directory named dataset/MobiAct_Dataset_v2.0.
2. Use dataset/MobiAct_preprocess.ipynb for preprocessing. Run all cells in the ipynb file.
3. Save all preprocessed files in dataset/mobiact_preprocessed.

For DL Methods

Each file named as DLR.ipynb, MobiAct.ipynb, Notch.ipynb, SmartFall.ipynb is for deep learning methods.

In each ipynb file, you can choose which DL method you want to use(CNN, singleLSTM, stackedLSTM)

For TFT Method

1. Clone https://github.com/google-research/google-research/tree/master/tft
2. Use each file named as dlr_tft.ipynb, mobi_tft.ipynb, notchFall_tft.ipynb and smartFall_tft.ipynb for TFT method.
3. Files named as dlr_tft_wo_bioinfo.ipynb and mobi_tft_no_bioinfo.ipynb are for cases when personal biometric information is removed.