Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Urwyler, Prabitha; Rampa, Luca; Stucki, Reto; Büchler, Marcel; Müri, René Martin; Mosimann, Urs Peter; Nef, Tobias (2015). Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers. Biomedical engineering online, 14(54), p. 54. BioMed Central 10.1186/s12938-015-0050-4

s12938-015-0050-4.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (2MB) | Preview

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.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > University Psychiatric Services > University Hospital of Geriatric Psychiatry and Psychotherapy
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Gerontechnology and Rehabilitation
04 Faculty of Medicine > Faculty Institutions > Office of the Dean, Faculty of Medicine > Office of the Dean, Medicine
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > Forschungsbereich Pavillon 52 > Forschungsgruppe Perzeption und Okulomotorik

UniBE Contributor:

Urwyler-Harischandra, Prabitha; Rampa, Luca; Stucki, Reto; Büchler, Marcel; Müri, René Martin; Mosimann, Urs Peter and Nef, Tobias


600 Technology > 610 Medicine & health
600 Technology > 620 Engineering




BioMed Central




Vanessa Vallejo

Date Deposited:

13 Jul 2015 12:19

Last Modified:

28 Nov 2020 02:25

Publisher DOI:


PubMed ID:





Actions (login required)

Edit item Edit item
Provide Feedback