Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique

dc.contributor.authorChoffin, Zachary
dc.contributor.authorJeong, Nathan
dc.contributor.authorCallihan, Michael
dc.contributor.authorOlmstead, Savannah
dc.contributor.authorSazonov, Edward
dc.contributor.authorThakral, Sarah
dc.contributor.authorGetchell, Camilee
dc.contributor.authorLombardi, Vito
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2021-08-25T20:16:02Z
dc.date.available2021-08-25T20:16:02Z
dc.date.issued2021
dc.description.abstractAnkle injuries may adversely increase the risk of injury to the joints of the lower extremity and can lead to various impairments in workplaces. The purpose of this study was to predict the ankle angles by developing a footwear pressure sensor and utilizing a machine learning technique. The footwear sensor was composed of six FSRs (force sensing resistors), a microcontroller and a Bluetooth LE chipset in a flexible substrate. Twenty-six subjects were tested in squat and stoop motions, which are common positions utilized when lifting objects from the floor and pose distinct risks to the lifter. The kNN (k-nearest neighbor) machine learning algorithm was used to create a representative model to predict the ankle angles. For the validation, a commercial IMU (inertial measurement unit) sensor system was used. The results showed that the proposed footwear pressure sensor could predict the ankle angles at more than 93% accuracy for squat and 87% accuracy for stoop motions. This study confirmed that the proposed plantar sensor system is a promising tool for the prediction of ankle angles and thus may be used to prevent potential injuries while lifting objects in workplaces.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.citationChoffin, Z., Jeong, N., Callihan, M., Olmstead, S., Sazanov, E., Thakral, S., Getchell, C., Lombardi, V. (2021): Ankle Angle Prediction Using a Footwear Pressure Sensor and Machine Learning Technique. Sensors. 21(11).
dc.identifier.doi10.3390/s21113790
dc.identifier.orcidhttps://orcid.org/0000-0002-5585-7701
dc.identifier.orcidhttps://orcid.org/0000-0001-7792-4234
dc.identifier.orcidhttps://orcid.org/0000-0003-4610-2208
dc.identifier.orcidhttps://orcid.org/0000-0003-1936-2813
dc.identifier.urihttp://ir.ua.edu/handle/123456789/8057
dc.languageEnglish
dc.language.isoen_US
dc.publisherMDPI
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectankle angle prediction resistive pressure sensor
dc.subjectmachine learning
dc.subjectsmart shoe
dc.subjectBACK-PAIN
dc.subjectChemistry, Analytical
dc.subjectEngineering, Electrical & Electronic
dc.subjectInstruments & Instrumentation
dc.subjectChemistry
dc.subjectEngineering
dc.titleAnkle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Techniqueen_US
dc.typetext
dc.typeArticle
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