Digit Recognition From Wrist Movements and Security Concerns with Smart Wrist Wearable IOT Devices

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dc.contributor.authorLeong, Lambert
dc.contributor.authorWiere, Sean
dc.date.accessioned2020-01-04T08:31:29Z
dc.date.available2020-01-04T08:31:29Z
dc.date.issued2020-01-07
dc.description.abstractIn this paper, we investigate a potential security vulnerability associated with wrist wearable devices. Hardware components on common wearable devices include an accelerometer and gyroscope, among other sensors. We demonstrate that an accelerometer and gyroscope can pick up enough unique wrist movement information to identify digits being written by a user. With a data set of 400 writing samples, of either the digit zero or the digit one, we constructed a machine learning model to correctly identify the digit being written based on the movements of the wrist. Our model’s performance on an unseen test set resulted in an area under the receiver operating characteristic (AUROC) curve of 1.00. Loading our model onto our fabricated device resulted in 100% accuracy when predicting ten writing samples in real-time. The model’s ability to correctly identify all digits via wrist movement and orientation changes raises security concerns. Our results imply that nefarious individuals may be able to gain sensitive digit based information such as social security, credit card, and medical record numbers from wrist wearable devices.
dc.format.extent8 pages
dc.identifier.doi10.24251/HICSS.2020.790
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64532
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine Learning and Cyber Threat Intelligence and Analytics
dc.subjectmachine learning
dc.subjectsecurity
dc.subjectwrist wearable
dc.titleDigit Recognition From Wrist Movements and Security Concerns with Smart Wrist Wearable IOT Devices
dc.typeConference Paper
dc.type.dcmiText

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