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SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning.

Taylor R Mauldin1, Marc E Canby2, Vangelis Metsis3

  • 1Department of Computer Science, Texas State University, San Marcos, TX 78666, USA. trm119@txstate.edu.

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|October 12, 2018
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Summary
This summary is machine-generated.

SmartFall, an Android app, uses smartwatch accelerometer data for real-time fall detection. Deep learning models significantly outperform traditional methods, enhancing privacy and generalizability for user wellbeing.

Keywords:
IoT applicationIoT architecturedeep learningfall detectionrecurrent neural networksmart healthsmartwatch

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Area of Science:

  • * Computer Science
  • * Biomedical Engineering
  • * Internet of Things (IoT)

Background:

  • * Accurate and private fall detection is crucial for elderly care and remote patient monitoring.
  • * Existing systems often rely on cloud-based processing, leading to latency and privacy concerns.

Purpose of the Study:

  • * To develop and evaluate an Android application (SmartFall) for real-time fall detection using smartwatch sensor data.
  • * To compare the performance of Deep Learning models against traditional machine learning algorithms for fall prediction.
  • * To propose a flexible IoT system architecture for continuous health monitoring.

Main Methods:

  • * Utilized accelerometer data from a commodity smartwatch (IoT device) paired with a smartphone.
  • * Implemented and compared Support Vector Machine, Naive Bayes, and Deep Learning algorithms for fall detection.
  • * Trained and validated models on three distinct fall datasets: Smartwatch, Notch, and Farseeing.

Main Results:

  • * Deep Learning models demonstrated superior performance in fall detection across all datasets compared to traditional methods.
  • * The Deep Learning approach automatically extracted relevant features, outperforming manual feature engineering.
  • * The Deep Learning model showed better generalization capabilities for predicting falls in new users.

Conclusions:

  • * SmartFall provides an effective, private, and low-latency solution for fall detection using on-device processing.
  • * Deep Learning is a promising approach for enhancing the accuracy and generalizability of wearable-based fall detection systems.
  • * The presented IoT architecture supports diverse sensor data integration for comprehensive remote wellbeing monitoring.