Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Nef, Tobias; Urwyler, Prabitha; Büchler, Marcel; Tarnanas, Ioannis; Stucki, Reto; Cazzoli, Dario; Müri, René Martin; Mosimann, Urs Peter (2015). Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data. Sensors, 15(5), pp. 11725-11740. Molecular Diversity Preservation International MDPI 10.3390/s150511725

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Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Item Type:

Journal Article (Original Article)

Division/Institute:

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 > Pre-clinic Human Medicine > BioMedical Research (DBMR) > Forschungsbereich Pavillon 52 > Forschungsgruppe Perzeption und Okulomotorik

UniBE Contributor:

Nef, Tobias; Urwyler-Harischandra, Prabitha; Büchler, Marcel; Tarnanas, Ioannis; Stucki, Reto; Cazzoli, Dario; Müri, René Martin and Mosimann, Urs Peter

Subjects:

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

ISSN:

1424-8220

Publisher:

Molecular Diversity Preservation International MDPI

Language:

English

Submitter:

Vanessa Vallejo

Date Deposited:

13 Jul 2015 11:28

Last Modified:

19 May 2016 13:26

Publisher DOI:

10.3390/s150511725

PubMed ID:

26007727

Additional Information:

This article belongs to the Special Issue Sensors and Smart Cities

Uncontrolled Keywords:

activities of daily living; ambient assisted living; data classification; data mining; healthcare technology; smart cities; smart homes

BORIS DOI:

10.7892/boris.70232

URI:

https://boris.unibe.ch/id/eprint/70232

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