Login / Signup

Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor.

Xin YangAbul DoulahMuhammad FarooqJason PartonMegan A McCroryJanine A HigginsEdward Sazonov
Published in: Scientific reports (2019)
Accurate and objective assessment of energy intake remains an ongoing problem. We used features derived from annotated video observation and a chewing sensor to predict mass and energy intake during a meal without participant self-report. 30 participants each consumed 4 different meals in a laboratory setting and wore a chewing sensor while being videotaped. Subject-independent models were derived from bite, chew, and swallow features obtained from either video observation or information extracted from the chewing sensor. With multiple regression analysis, a forward selection procedure was used to choose the best model. The best estimates of meal mass and energy intake had (mean ± standard deviation) absolute percentage errors of 25.2% ± 18.9% and 30.1% ± 33.8%, respectively, and mean ± standard deviation estimation errors of -17.7 ± 226.9 g and -6.1 ± 273.8 kcal using features derived from both video observations and sensor data. Both video annotation and sensor-derived features may be utilized to objectively quantify energy intake.
Keyphrases
  • weight gain
  • patient safety
  • physical activity
  • healthcare
  • high resolution
  • emergency department
  • adverse drug
  • mass spectrometry
  • weight loss
  • big data
  • deep learning