title: Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers.
abstract: BACKGROUND
  Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data.
  
  
  METHODS
  A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL.
  
  
  RESULTS
  Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB.
  
  
  CONCLUSIONS
  The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.
authors.affiliation: Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland. prabitha.urwyler@artorg.unibe.ch.
authors.forename: Prabitha
authors.initials: P
authors.lastname: Urwyler
authors.affiliation: University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland. luca.rampa@gef.be.ch.
authors.forename: Luca
authors.initials: L
authors.lastname: Rampa
authors.affiliation: Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland. reto.stucki@artorg.unibe.ch.
authors.forename: Reto
authors.initials: R
authors.lastname: Stucki
authors.affiliation: Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland. marcel-buechler@bluewin.ch.
authors.forename: Marcel
authors.initials: M
authors.lastname: Büchler
authors.affiliation: Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland. rene.mueri@insel.ch.|Perception and Eye Movement Laboratory, Departments of Neurology and Clinical Research, University Hospital Inselspital, University of Bern, Bern, Switzerland. rene.mueri@insel.ch.
authors.forename: René
authors.initials: R
authors.lastname: Müri
authors.affiliation: Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland. urs.mosimann@gef.be.ch.|University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland. urs.mosimann@gef.be.ch.
authors.forename: Urs P
authors.initials: UP
authors.lastname: Mosimann
authors.affiliation: Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland. tobias.nef@artorg.unibe.ch.|ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland. tobias.nef@artorg.unibe.ch.
authors.forename: Tobias
authors.initials: T
authors.lastname: Nef
mesh_terms: D061725:Accelerometry / Q000295:instrumentation; D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D000465:Algorithms; D001499:Bayes Theorem; D002940:Circadian Rhythm; D003663:Decision Trees; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D007259:Infrared Rays; D008297:Male; D008499:Medical Records; D008875:Middle Aged; D009483:Neuropsychological Tests; D012890:Sleep; D000069553:Supervised Machine Learning; D062125:Thermometry / Q000295:instrumentation; D059015:Wireless Technology / Q000295:instrumentation