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lstm_sensitivityfalldetection

MatLab scripts for performing a sensitivity analysis of LSTM networks for fall detection wearable sensors based on the SisFall dataset

https://github.com/scorreiapt/lstm_sensitivityfalldetection

Science Score: 57.0%

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Keywords

artificial-intelligence fall-detection lstm-neural-networks matlab-script sensitivity-analysis
Last synced: 6 months ago · JSON representation ·

Repository

MatLab scripts for performing a sensitivity analysis of LSTM networks for fall detection wearable sensors based on the SisFall dataset

Basic Info
  • Host: GitHub
  • Owner: SCorreiaPT
  • License: apache-2.0
  • Language: MATLAB
  • Default Branch: main
  • Homepage:
  • Size: 40 KB
Statistics
  • Stars: 5
  • Watchers: 1
  • Forks: 2
  • Open Issues: 0
  • Releases: 0
Topics
artificial-intelligence fall-detection lstm-neural-networks matlab-script sensitivity-analysis
Created about 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

MatLab Source Code for Sensitivity Analysis of LSTM Networks for Fall Detection Wearable Sensors

Falling detection concerns identifying and alerting when a person falls or experiences a sudden loss of balance. It is an important safety feature, particularly for elderly or vulnerable individuals, as falls can lead to severe injuries. One applicable technique is employing Long Short-Term Memory (LSTM) networks that can be used for falling detection by processing data from sensors, such as accelerometers or gyroscopes, to identify patterns associated with falls. This MatLab source code demonstrates that a single LSTM layer can be used for this purpose. Still, multiple LSTM layers can improve the system's performance with less processing time, reaching 99.13\% of accuracy with 4.35\% of loss values. Furthermore, this work thoroughly studies the sensitivity of the size of an LSTM cell, demonstrating that it is not necessary to obtain a size close to the timestamp of the features and thus save hardware resources.

Cite: J. P. Matos-Carvalho, S. D. Correia, S. Tomic, "Sensitivity Analysis of LSTM Networks for Fall Detection Wearable Sensors", 2023 6th Conference on Cloud and Internet of Things (CIoT'23), Lisbon, Portugal, 2023, pp. 112-118, doi: 10.1109/CIoT57267.2023.10084906

Owner

  • Name: Sergio Duarte Correia
  • Login: SCorreiaPT
  • Kind: user
  • Location: Portalegre, Portugal
  • Company: Portalegre Polytechnic University

Sérgio Correia received his Diploma in Electrical and Computer Engineering from the University of Coimbra, Portugal, in 2000, the Master's Degree in Industrial

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Correia"
  given-names: "Sérgio D."
  orcid: "https://orcid.org/0000-0003-1111-3513"
- family-names: "Matos-Carvalho"
  given-names: "João P."
  orcid: "https://orcid.org/0000-0001-9409-7736"
title: "Sensitivity Analysis of LSTM Networks for Fall Detection Wearable Sensors"
version: 2.0.4
doi: 
date-released: 2023-01-20
url: "https://github.com/SCorreiaPT/LSTM_SensitivityFallDetection"

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