Urwyler, Prabitha; Stucki, Reto; Müri, René Martin; Mosimann, Urs Peter; Nef, Tobias (August 2015). Passive wireless sensor systems can recognize activites of daily living. IEEE Engineering in Medicine and Biology Society conference proceedings, pp. 8042-8045. IEEE Service Center 10.1109/EMBC.2015.7320259
Text
07320259.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (757kB) |
The ability to determine what activity of daily living a person performs is of interest in many application domains. It is possible to determine the physical and cognitive capabilities of the elderly by inferring what activities they perform in their houses. Our primary aim was to establish a proof of concept that a wireless sensor system can monitor and record physical activity and these data can be modeled to predict activities of daily living. The secondary aim was to determine the optimal placement of the sensor boxes for detecting activities in a room. A wireless sensor system was set up in a laboratory kitchen. The ten healthy participants were requested to make tea following a defined sequence of tasks. Data were collected from the eight wireless sensor boxes placed in specific places in the test kitchen and analyzed to detect the sequences of tasks performed by the participants. These sequence of tasks were trained and tested using the Markov Model. Data analysis focused on the reliability of the system and the integrity of the collected data. The sequence of tasks were successfully recognized for all subjects and the averaged data pattern of tasks sequences between the subjects had a high correlation. Analysis of the data collected indicates that sensors placed in different locations are capable of recognizing activities, with the movement detection sensor contributing the most to detection of tasks. The central top of the room with no obstruction of view was considered to be the best location to record data for activity detection. Wireless sensor systems show much promise as easily deployable to monitor and recognize activities of daily living.
Item Type: |
Conference or Workshop Item (Paper) |
---|---|
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 > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology |
Graduate School: |
Graduate School for Cellular and Biomedical Sciences (GCB) |
UniBE Contributor: |
Urwyler-Harischandra, Prabitha, Stucki, Reto, Müri, René Martin, Mosimann, Urs Peter, Nef, Tobias |
Subjects: |
600 Technology > 610 Medicine & health 600 Technology > 620 Engineering |
ISSN: |
1557-170X |
ISBN: |
978-1-4244-9270-1 |
Publisher: |
IEEE Service Center |
Language: |
English |
Submitter: |
Vanessa Vallejo |
Date Deposited: |
16 Feb 2016 17:03 |
Last Modified: |
05 Dec 2022 14:51 |
Publisher DOI: |
10.1109/EMBC.2015.7320259 |
BORIS DOI: |
10.7892/boris.75571 |
URI: |
https://boris.unibe.ch/id/eprint/75571 |