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dc.contributor.advisorNgu, Anne H. H.
dc.contributor.authorCoyne, Shaun ( )
dc.date.accessioned2020-07-16T21:08:24Z
dc.date.available2020-07-16T21:08:24Z
dc.date.issued2020-05
dc.identifier.citationCoyne, S. (2020). Continuous personalized fall detection and data collection (Unpublished thesis). Texas State University, San Marcos, Texas.en_US
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/12103
dc.descriptionPresented to the Honors Committee of Texas State University in Partial Fulfillment of the Requirements for Graduation in the University Honors Program, May 2020.
dc.description.abstractFalls are very real problems for elderly people. Falling is the leading cause of injury and death in older Americans according to the Centers for Disease Control and Prevention. Currently there is no software system for reliably detecting falls for elder people that is unobtrusive, affordable, and easily accessible. While the idea of using a wrist worn smart watch to collect accelerometer data to predict falls has been proposed before, the fall detection algorithms are all trained using simulated fall data from healthy young adults. Any system trained for fall detection that utilizes only a simulated dataset cannot be recommended for real-world application as it does not reflect the characteristics of fall from the target population. We propose a solution that will move fall detection into the real world by being capable of collecting real data from elder patients unobtrusively. This system utilizes an online database and a GPU server to collect labeled data from each user and retrain the model specific to that user dynamically. This allows the system to learn the patterns in a user’s linear acceleration data combined with a pre-existing synthetic dataset to determine if a user has fallen or not. That is, it will be capable of personalizing the model to each user to improve precision while maintaining the recall. Additionally, it is capable of reliably collecting all linear acceleration data (falls or activities of daily life) from a user via positive and negative feedback data. This will help to provide future research with real-world fall datasets to create even more reliable models. This system is built to scale using Couchbase, Tensorflow, and commodity smartwatch and smartphone devices.en_US
dc.formatText
dc.format.extent67 pages
dc.format.medium1 file (.pdf)
dc.language.isoen_USen_US
dc.subjectFall detectionen_US
dc.subjectRNNen_US
dc.subjectDeep learningen_US
dc.subjectCouchbaseen_US
dc.subjectData collectionen_US
dc.subjectLabeled dataseten_US
dc.subjectUser feedbacken_US
dc.subjectAutomationen_US
dc.subjectContinuous trainingen_US
dc.subjectActivity recognitionen_US
dc.titleContinuous Personalized Fall Detection and Data Collectionen_US
txstate.documenttypeThesis
thesis.degree.departmentHonors College
thesis.degree.disciplineComputer Science
thesis.degree.grantorTexas State University
txstate.departmentHonors College


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