Designing an Intelligent, Flexible Elderly Monitoring System With NI CompactRIO and NI
LabVIEW
"We used NI CompactRIO as one of the hardware
components capable of sensing and processing
physiological signs of the elderly person, such as
heart rate, blood pressure, and weight. The
physiological data gives the system a snapshot of
the elderly person’s health condition and helps the
system discern ADLs from potential events that
could be fatal."
- Chee Teck Phua, Nanyang Polytechnic
The Challenge:
Developing an elderly monitoring system that discerns activities of daily living (ADLs) from events such as a fall, fire, or sudden death.
The Solution:
Creating an intelligent elderly monitoring system using NI LabVIEW with cameras to track users, a wireless sensor network for environmental
monitoring, and CompactRIO for physiological sign sensing.
Author(s):
Chee Teck Phua - Nanyang Polytechnic
As in many developed countries, one of Singapore’s key challenges includes an aging population. There is an emerging need for remote monitoring of the
elderly with minimum false alarms [1]. However, commercially available monitoring systems are primarily based on decisions made by managers that are
based on recommendations by technicians, and have resulted in choices that don’t fit the actual needs of the elderly person receiving care [2].
We created a system that periodically acquires data from sensors and automatically or manually maps the acquired data to the ADLs of the elderly
person. The user can customize the system based on the data acquired by the sensors for the ADLs of the elderly person.
System Overview
The system we created offers nonintrusive ADL monitoring for the elderly by evaluating environmental sensors (see Figure 1). Coupled with periodic
monitoring of physiological signs and video of the elderly person, this solution provides an automated or manually adjustable self-learning system so users
can associate measured data with normal ADLs.
System Hardware
We used CompactRIO (http://www.ni.com/compactrio/)as one of the hardware components capable of sensing and processing physiological signs of the
elderly person such as the heart rate, blood pressure, and weight (see Figure 2). The physiological sign data gives the system a snapshot of the elderly
person’s health condition and helps the system discern ADLs from potential events that could be fatal.
The second key hardware component in the system uses wireless sensor network (http://www.ni.com/wsn/) (WSN) devices to interface with sensors for
environmental sensing (see Figure 3). Using environmental data collected from the WSN, the system correlates the data to the ADLs of the elderly person.
The last hardware component in the system is the smart camera (see Figure 4). The elderly person uses the camera to capture his or her image every
day, with a time log on each image. In addition, the system transmits the images to a central monitoring station and stores them for data analysis in the
event of abnormalities in other measured parameters. The data obtained from the various sensors and the stored images enhance the capabilities in
discerning ADLs from potentially harmful events, such as a fall.
System Software
The system software platform is LabVIEW (http://www.ni.com/labview/), which gives us an intuitive graphical system design interface based on a unique
graphical programming language; built-in engineering-specific libraries of software functions and hardware interfaces; and data analysis, visualization, and
sharing features. LabVIEW integrates the hardware components into a single GUI (see Figure 5).
As shown in Figure 5, the system acquires data from the various sensors and processes it to discern the ADLs of the elderly person from potentially fatal
events (see Figure 6). This framework delivers a platform that adapts to the needs of the user and helps caregivers associate the potential of fatal events
through abnormalities in measured data.
Conclusion
We used CompactRIO and LabVIEW to create a flexible solution that remotely monitors elderly people in their homes. The hardware platform supports
ease of data communication to the monitoring station, while the software platform uses LabVIEW to deliver a user-adaptable solution with which users can
associate measured data to their ADLs. We plan to deploy this system for trial in the elderly community in Singapore to collect feedback to enhance the
application.
Acknowledgement
We want to express our gratitude to the School of Engineering at Nanyang Polytechnic (Singapore) for the support of this development and use of facilities
that made this work possible.
References
[1] Abbate, S., et al., “Recognition of false alarms in fall detection systems,” Consumer Communications and Networking Conference (CCNC), IEEE 9–12
Jan. 2011, pp. 23–28.
[2] Anne-mie Sponselee, et al., “Smart Home Technology for the Elderly: Perceptions of Multidisciplinary Stakeholders,”
hbo-kennisbank.uvt.nl/cgi/fontys/show.cgi?fid=3645 (http://hbo-kennisbank.uvt.nl/cgi/fontys/show.cgi?fid=3645).
Author Information:
Chee Teck Phua
Nanyang Polytechnic
180 Ang Mo Kio Avenue 8
569830
Singapore
Tel: 65500703
Fax: 65500400
Phua_chee_teck@nyp.gov.sg (mailto:Phua_chee_teck@nyp.gov.sg)
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Figure 1. System Overview
Figure 2. Useing CompactRIO for Physiological Sign Sensing
Figure 3. Using NI WSN Devices for Environmental Sensing
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Figure 4. Using a Smart Camera for Periodic Elderly Sighting
Figure 5. A Reference Platform for a User Interface and Hardware Control
Figure 6. Data Processing Framework and Decision to Discern ADLs from Fatal Events
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