title: A wearable sensor system for medication adherence prediction.
abstract: Studies have revealed that non-adherence to prescribed medication can lead to hospital readmissions, clinical complications, and other negative patient outcomes. Though many techniques have been proposed to improve patient adherence rates, they suffer from low accuracy. Our objective is to develop and test a novel system for assessment of medication adherence.
  
  
  
  Recently, several smart pill bottle technologies have been proposed, which can detect when the bottle has been opened, and even when a pill has been retrieved. However, very few systems can determine if the pill is subsequently ingested or discarded. We propose a system for detecting user adherence to medication using a smart necklace, capable of determining if the medication has been ingested based on the skin movement in the lower part of the neck during a swallow. This, coupled with existing medication adherence systems that detect when medicine is removed from the bottle, can detect a broader range of use-cases with respect to medication adherence.
  
  
  
  Using Bayesian networks, we were able to correctly classify between chewable vitamins, saliva swallows, medication capsules, speaking, and drinking water, with average precision and recall of 90.17% and 88.9%, respectively. A total of 135 instances were classified from a total of 20 subjects.
  
  
  
  Our experimental evaluations confirm the accuracy of the piezoelectric necklace for detecting medicine swallows and disambiguating them from related actions. Further studies in real-world conditions are necessary to evaluate the efficacy of the proposed scheme.
authors.affiliation: Department of Computer Science, University of California, Los Angeles, 3514 Boelter Hall, Los Angeles, CA 90095, United States. Electronic address: kalantarian@cs.ucla.edu.
authors.forename: Haik
authors.initials: H
authors.lastname: Kalantarian
authors.affiliation: Department of Computer Science, University of California, Los Angeles, 3514 Boelter Hall, Los Angeles, CA 90095, United States. Electronic address: babakm@cs.ucla.edu.
authors.forename: Babak
authors.initials: B
authors.lastname: Motamed
authors.affiliation: Department of Preventative Medicine, Northwestern University, 680 North Lakeshore Drive, Suite 1400, Chicago, IL 60611, United States. Electronic address: nabil@northwestern.edu.
authors.forename: Nabil
authors.initials: N
authors.lastname: Alshurafa
authors.affiliation: Department of Computer Science, University of California, Los Angeles, 3256N Boelter Hall, Los Angeles, CA 90095, United States. Electronic address: majid@cs.ucla.edu.
authors.forename: Majid
authors.initials: M
authors.lastname: Sarrafzadeh
mesh_terms: D000284:Administration, Oral; D001499:Bayes Theorem; D003679:Deglutition; D005544:Forecasting; D006801:Humans; D055118:Medication Adherence; D000076251:Wearable Electronic Devices
keywords: Deglutition; Medication adherence; Pervasive computing; Piezoelectric sensor; Wearable devices