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Spine Cop: Posture Correction Monitor and Assistant.

Pedro RibeiroAna Rita SoaresRafael GirãoMiguel NetoSusana Cardoso
Published in: Sensors (Basel, Switzerland) (2020)
Back and spine-related issues are frequent maladies that most people have or will experience during their lifetime. A common and sensible observation that can be made is regarding the posture of an individual. We present a new approach that combines accelerometer, gyroscope, and magnetometer sensor data in combination with permanent magnets assembled as a wearable device capable of real-time spine posture monitoring. An independent calibration of the device is required for each user. The sensor data is processed by a probabilistic classification algorithm that compares the real-time data with the calibration result, verifying whether the data point lies within regions of confidence defined by a computed threshold. An incorrect posture classification is considered if both accelerometer and magnetometer classify the posture as incorrect. A pilot trial was performed in a single adult test subject. The combination of the magnets and magnetometer greatly improved the posture classification accuracy (89%) over the accuracy obtained when only accelerometer data were used (47%). The validation of this method was based on image analysis.
Keyphrases
  • electronic health record
  • machine learning
  • deep learning
  • big data
  • physical activity
  • magnetic resonance imaging
  • artificial intelligence
  • computed tomography
  • magnetic resonance
  • data analysis
  • young adults