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

1
Department of Computer Science, Texas State University, San Marcos, TX 78666, USA
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Department of Computer Science, Rice University, Houston, TX 77005, USA
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Department of Computer Science, University of Puerto Rico, San Juan 00927, Puerto Rico
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(10), 3363; https://doi.org/10.3390/s18103363
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)
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
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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.

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