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. 2018 Oct 9;18(10):3363.
doi: 10.3390/s18103363.

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

Taylor R Mauldin  1 , Marc E Canby  2 , Vangelis Metsis  3 , Anne H H Ngu  4 , Coralys Cubero Rivera  5
Affiliations

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

Taylor R Mauldin et al. Sensors (Basel). .

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.

Keywords: IoT application; IoT architecture; deep learning; fall detection; recurrent neural network; smart health; smartwatch.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Architecture of SmartFall IoT system.
Figure 2
Figure 2
SmartFall User Interface.
Figure 3
Figure 3
Different fall signal patterns.
Figure 4
Figure 4
RNN Model Architecture.
Figure 5
Figure 5
Prediction scheme for deep learning.
See this image and copyright information in PMC

References

    1. Adib F., Mao H., Kabelac Z., Katabi D., Miller R.C. Smart Homes That Monitor Breathing and Heart Rate; Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI 2015); Seoul, Korea. 18–23 April 2015; pp. 837–846.
    1. Tacconi C., Mellone S., Chiari L. Smartphone-based applications for investigating falls and mobility; Proceedings of the 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops; Dublin, Ireland. 23–26 May 2011; pp. 258–261.
    1. Chaudhuri S., Thompson H., Demiris G. Fall Detection Devices and their use with Older Adults: A Systematic Review. J. Geriatr. Phys. Ther. 2014;37:178–196. doi: 10.1519/JPT.0b013e3182abe779. - DOI - PMC - PubMed
    1. Microsoft Band 2 Smartwatch. [(accessed on 26 August 2018)]; Available online: https://www.microsoft.com/en-us/band.
    1. Notch: Smart Motion Capture for Mobile Devices. [(accessed on 26 August 2018)]; Available online: https://wearnotch.com/

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