Next Article in Journal
Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks
Previous Article in Journal
Beamforming Design for Full-Duplex SWIPT with Co-Channel Interference in Wireless Sensor Systems
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(10), 3363; https://doi.org/10.3390/s18103363

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

1
Department of Computer Science, Texas State University, San Marcos, TX 78666, USA
2
Department of Computer Science, Rice University, Houston, TX 77005, USA
3
Department of Computer Science, University of Puerto Rico, San Juan 00927, Puerto Rico
*
Author to whom correspondence should be addressed.
Received: 31 August 2018 / Revised: 4 October 2018 / Accepted: 4 October 2018 / Published: 9 October 2018
(This article belongs to the Section Internet of Things)
Full-Text   |   PDF [592 KB, uploaded 9 October 2018]   |  

Abstract

This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing. View Full-Text
Keywords: fall detection; deep learning; recurrent neural network; smart health; IoT application; IoT architecture; smartwatch fall detection; deep learning; recurrent neural network; smart health; IoT application; IoT architecture; smartwatch
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Mauldin, T.R.; Canby, M.E.; Metsis, V.; Ngu, A.H.H.; Rivera, C.C. SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning. Sensors 2018, 18, 3363.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top