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dc.contributor.authorMauldin, Taylor R.
dc.contributor.authorCanby, Marc E.
dc.contributor.authorMetsis, Vangelis
dc.contributor.authorNgu, Anne H. H.
dc.contributor.authorRivera, Coralys Cubero
dc.date.accessioned2019-08-06T17:54:19Z
dc.date.available2019-08-06T17:54:19Z
dc.date.issued2018-10-09
dc.identifier.citationMauldin, T. R., Canby, M. E., Metsis, V., Ngu, A. H. H., Rivera, C. C. (2018). Smartfall: A smartwatch-based fall detection system using deep learning. Sensors, 18(10) : 3363.en_US
dc.identifier.issn1424-8220
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/8473
dc.description.abstractThis 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.en_US
dc.formatText
dc.format.extent19 pages
dc.format.medium1 file (.pdf)
dc.language.isoen_USen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.sourceSensors, 2018, Vol. 18, No. 10 : 3363
dc.subjectIoT applicationen_US
dc.subjectIoT architecture
dc.subjectDeep learning
dc.subjectFall detection
dc.subjectRecurrent neural network
dc.subjectSmart health
dc.subjectSmartwatch
dc.titleSmartFall: A Smartwatch-Based Fall Detection System Using Deep Learningen_US
txstate.documenttypeArticle
dc.identifier.doihttps://doi.org/10.3390/s18103363
txstate.departmentComputer Science


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