/FDS

BIOBOT: A Fall Detection System (FDS) using Artificial Intelligence

Primary LanguageJupyter Notebook

FDS README: Description of the contents of the repository. Author: Yan Bello
You can read a description of this project in the following post:
http://www.spaceminds.com/wp/biobot-a-fall-detection-system-fds-using-artificial-intelligence/

BioBot - Fall Detection System (FDS) Repository:
This repository https://github.com/SpaceMinds/FDS has been setup to publish/ share the code files developed as part of the project of the development and exploration/experimentation with a Fall Detection System (FDS).

  • Deliverable_01: Data preparation and Threshold-based Classifier
  • Deliverable_02: Implementing a K-Nearest Neightbors (KNN) Classifier model
  • Deliverable_03: Implementing a Support Vector Machine (SVM)-like Classifier model
  • Deliverable_04: Implementing a simple Neural Network Model with TensorFlow
  • Deliverable_05: Preparing a balanced dataset using moving windows
  • Deliverable_06: Implementing several models (KNN, SVC, NN/TF-Keras) using a balanced dataset and moving Windows
  • Deliverable_07: Preparing a balanced dataset with moving windows for LSTM RNN Models
  • Deliverable_08: Implementing LSTM RNN Models

    • Files included in this deliverables’ baseline:

  • BioBot_FDS_01_Prepare_Data_Threshold_Classifier (14/10/2018)
  • BioBot_FDS_02_KNN_Model (14/11/2018)
  • BioBot_FDS_03_SVM_Model (14/11/2018)
  • BioBot_FDS_04_Simple_NN_TF Model (14/11/2018)
  • BioBot_FDS_05_Prepare_Moving_Windows_Ds (18/11/2018)
  • BioBot_FDS_YB_06_Models_Using_Balanced_Ds_Moving_Window (20/11/2018)
  • BioBot_FDS_YB_07_DATA_PREP_4_LSTM (01/12/2018)
  • BioBot_FDS_YB_08_IMPLEMENTING_LSTM (04/12/2018)

    • These models and Python codes has been developed by Yan Bello, as part of the Master in Artificial Intelligence (UNIR).

    For the code developed we used the SisFall dataset: A Fall and Movement Dataset. Created by: A. Sucerquia, J.D. López, J.F. Vargas-Bonilla SISTEMIC, Faculty of Engineering, Universidad de Antiquia UDEA. Detailed information about this dataset can be found in this website: http://sistemic.udea.edu.co/en/investigacion/proyectos/english-falls/. Reference paper: Sucerquia A, López JD, Vargas-Bonilla JF. SisFall: A Fall and Movement Dataset. Sensors (Basel). 2017;17(1):198. Published 2017 Jan 20. doi:10.3390/s17010198