Automated Speech Recognition for a Personal Emergency Response System (PERS)
Keywords: Speech recognition, speech-based PERS, automated emergency response.
Background
The use of traditional push-button personal emergency response systems (PERS) in the home of the older adult has been shown to ease both caregiver and user anxiety, support aging-in-place, and decrease overall healthcare costs. However, only a small proportion of the total older adult population who could benefit from this technology actually uses these devices. Reasons for non-use include stigmatization or burden from always having to wear a button, fear of losing independence, inability to access or problems using their system (e.g., not wearing button, too difficult or easy to push button), system cost, and other personal reasons (e.g., unattractive, lack of perceived need) (Mann et al., 2005; Porter, 2005). Furthermore, the majority of calls to the emergency call centre tend to be false alarms or accidental, further stressing already limited emergency resources.
With an increasing senior population and the subsequent rise in need for healthcare and social services it is important to ensure that potentially life-saving assistive technologies that support aging-in-place, such as the PERS, be made usable, effective and efficient for future generations.
Research indicates that older adults are open to using various assistive technologies, however, the technology must work well and fill a need before the technology is adopted (Mann et al., 2001; Demiris et al., 2004). Older adults have also been found to be very receptive to technologies which are controlled using speech alone (Johnson et al., 2007; Anderson et al., 1999).
It is hypothesized that if the user is not required to wear any actuators, then the burden and stigmatization of wearing “the button” will be eliminated. Furthermore, if the system was intelligent, allowing the user to cancel accidental calls for assistance, change their mind over the course of time and call for help when desired, or adapt the dialogue to the user’s previous calls for assistance, then these combined changes should improve overall PERS efficiency, effectiveness and usability.
Overview of Research
To improve PERS effectiveness, efficiency and usability, IATSL is working on developing an automated, speech-based PERS as an alternative to the basic push button activators (Hamil et al., 2009). See Figure 1, the HELPER System.
Within the larger home-health assistive technology project (the HELPER system), a communication and response module (CRM) was developed for the PERS incorporating speech recognition and artificial intelligence (Mihailidis et al., 2006; Hamil et al., 2009.). This prototype was successfully tested in a controlled laboratory environment with younger adults using a custom-built microphone array. The next step in this project is to further optimize the system’s ability to communicate with the target user in a home environment. A better understanding of what happens during a personal emergency response call is needed to help specify how the communication dialogue of this system should function, in addition to, a speech database of older adult voices to further train, develop, and test the PERS speech recognition system.
Figure 1. Flow diagram of a conventional PERS (solid lines) with the addition of the HELPER system (dotted lines).
Project Methodology
This research project has been divided into three phases: (I) Characterization of the personal emergency response event, (II) Identification of key words and phrases used in personal emergency response calls, and (III) Speech corpus development.
Phase I: Characterizing the personal emergency response event
To characterize personal emergency response events using recorded and real personal emergency response calls. To identify callers, reason for calls, risk levels, call responses, and important conversational measures useful that may be useful for system development.
Phase II: Identification of key words and phrases used in personal emergency response calls
To extract key words and phrases from recorded and real personal emergency response calls that may be used to characterize a personal emergency response event.
Phase III: Speech Corpus Development
To develop a speech corpus of older adult speech in emergency situations that may be used to train, develop and test the communication component of the automated, speech-based PERS.
Future Work
Future work will focus on system development and testing. This includes development of the automated PERS communication dialogue and dialogue manager; training and development of the speech recognizer; testing of the speech recognition system, dialogue manager, and communication dialogue using the speech database and speech in mock emergency situations in the home lab, and finally, live subject testing in the home lab followed by live subject testing in the real world.
Research Team
Alex Mihailidis, University of Toronto
Elizabeth Rochon, University of Toronto
Willy Wong, Associate Professor, University of Toronto
Tom Chau, Associate Professor, University of Toronto
Gerald Penn, Associate Professor, University of Toronto
Vicky Young,
Ph.D. candidate, University of Toronto
Funding Sources
Natural Sciences and Engineering Research Council (NSERC)
CIHR strategic fellowship in Healthcare Technology and Place
Engineers Canada-TD Insurance Meloche Monnex
University Health Network - Toronto Rehabilitation Institute
Rehabilitation Sciences Institute, University of Toronto
References
- Anderson, S. et al. 1999. Recognition of elderly speech and voice-driven document retrieval. IEEE, proceedings of the ICASSP, p.145-148.
- Demiris, G. et al. 2004. Older adults’ attitudes towards and perceptions of ‘smart home’ technologies: a pilot study. Medical Informatrics & The internet in Medicine. 29(2): 87-94.
- Johnson, J.L., Davenport, R. and W.C. Mann. 2007. Consumer Feedback on Smart Home Applications. Topics in Geriatric Rehabilitation. 23(1): p. 60-72.
- Mann, W.C., Belchior, P, Tomita, M. and Kemp, B.J. 2005. Use of personal emergency response systems by older individuals with disabilities. Assistive Technology, 17, 82-88.
- Mann W.C., Marchant T., Tomita M., Fraas L., Stanton K. 2001-2002 Winter. Elder acceptance of health monitoring devices in the home, Care Management Journals; 3(2): 91-8.
- Hamil, M., Young, V., Boger, J., Mihailidis, A. 2009. Development of an automated speech recognition interface for personal emergency response systems. Journal of NeuroEngineering and Rehabilitation, 6(26).
- Mihailidis, A. et al. 2006. An Intelligent Health Monitoring and Emergency Response System. ICOST paper. Gerontechnology, 4(4):209-222.
- Porter, E.J. 2005. Wearing and using personal emergency response systems. Journal of Gerontological Nursing. Oct, 26-33.
