The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
iFall: An android application for fall monitoring and response
Abstract
Injuries due to falls are among the leading causes of hospitalization in elderly persons, often resulting in a rapid decline in quality of life or death. Rapid response can improve the patients outcome, but this is often lacking when the injured person lives alone and the nature of the injury complicates calling for help. This paper presents an alert system for fall detection using common commercially available electronic devices to both detect the fall and alert authorities. We use an Android-based smart phone with an integrated tri-axial accelerometer. Data from the accelerometer is evaluated with several threshold based algorithms and position data to determine a fall. The threshold is adaptive based on user provided parameters such as: height, weight, and level of activity. The algorithm adapts to unique movements that a phone experiences as opposed to similar systems which require users to mount accelerometers to their chest or trunk. If a fall is suspected a notification is raised requiring the user's response. If the user does not respond, the system alerts pre-specified social contacts with an informational message via SMS. If a contact responds the system commits an audible notification, automatically connects, and enables the speakerphone. If a social contact confirms a fall, an appropriate emergency service is alerted. Our system provides a realizable, cost effective solution to fall detection using a simple graphical interface while not overwhelming the user with uncomfortable sensors.
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Citations
Cites
Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm
Bourke, A. K., O’Brien, J. V., & Lyons, G. M. (2007) · Gait & Posture · doi:10.1016/j.gaitpost.2006.09.012 · Cited by 821
Fall detection - Principles and Methods
Noury, N., Fleury, A., Rumeau, P., Bourke, A. K., Laighin, G. O., Rialle, V., & Lundy, J. E. (2007) · 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2007.4352627 · Cited by 615
A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor
Bourke, A. K., & Lyons, G. M. (2008) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2006.12.001 · Cited by 509
Staying connected while on the move: Cell phone use and social connectedness
Wei, R., & Lo, V.-H. (2006) · New Media & Society · doi:10.1177/1461444806059870 · Cited by 493
Wearable Sensors for Reliable Fall Detection
Chen, J., Kwong, K., Chang, D., Luk, J., & Bajcsy, R. (2005) · 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference · doi:10.1109/iembs.2005.1617246 · Cited by 443
A Smart and Passive Floor-Vibration Based Fall Detector for Elderly
Alwan, M., Rajendran, P. J., Kell, S., Mack, D., Dalal, S., Wolfe, M., & Felder, R. (2006) · 2006 2nd International Conference on Information & Communication Technologies · doi:10.1109/ictta.2006.1684511 · Cited by 402
Peterson, E. W., Cho, C. C., & Finlayson, M. L. (2007) · Multiple Sclerosis Journal · doi:10.1177/1352458507079260 · Cited by 258
A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information
Miaou, S.-G., Sung, P.-H., & Huang, C.-Y. (2006) · 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2. · doi:10.1109/ddhh.2006.1624792 · Cited by 253
Lee, T., & Mihailidis, A. (2005) · Journal of Telemedicine and Telecare · doi:10.1258/1357633054068946 · Cited by 237
Activity summarisation and fall detection in a supportive home environment
Nait-Charif, H., & McKenna, S. J. (2004) · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · doi:10.1109/icpr.2004.1333768 · Cited by 223
Cited by
A review of wearable sensors and systems with application in rehabilitation
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396
A Systematic Review of Healthcare Applications for Smartphones
Mosa, A. S. M., Yoo, I., & Sheets, L. (2012) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-12-67 · Cited by 1,184
Human fall detection on embedded platform using depth maps and wireless accelerometer
Kwolek, B., & Kepski, M. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.09.005 · Cited by 650
Smartphone medication adherence apps: Potential benefits to patients and providers
Dayer, L., Heldenbrand, S., Anderson, P., Gubbins, P. O., & Martin, B. C. (2013) · Journal of the American Pharmacists Association · doi:10.1331/japha.2013.12202 · Cited by 473
Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls
Bagalà, F., Becker, C., Cappello, A., Chiari, L., Aminian, K., Hausdorff, J. M., Zijlstra, W., & Klenk, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037062 · Cited by 464
Metadata
- Authors
- Sposaro, F., & Tyson, G.
- Venue
- 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Publisher
- IEEE
- Published
- 2009-09
- Volume
- 2009
- Pages
- 6119-6122
- OpenAlex
- W2096962968
- Cited by
- 317
- References
- 21
- FWCI
- 15.54
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
