iFall: An android application for fall monitoring and response

By Frank Sposaro, Gary S. Tyson. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009

357 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, General Health Professions

View Paper: https://www.semanticscholar.org/paper/a03e259293d46e7360252a72a7c7881de22fd58a

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In General Health Professions

Citations in the corpus

Challenges, issues and trends in fall detection systems (2013, methodology): “Some of these studies [49] have resulted in real fall detection applications that are available for download in Google Play [56].”
Smartphones and Health Promotion: A Review of the Evidence (2013, background): “If the user does not respond, the system alerts pre-specified social contacts with an informational message via SMS [33].”
A Systematic Review of Healthcare Applications for Smartphones (2012, background): “Sposaro and Tyson (2009) [81] demonstrated the costeffectiveness of iFall, which utilizes the inbuilt tri-axial accelerometer of an Android phone, processes the data on the phone, and prompts the user in cases of possible falls.”
Inertial Sensor-Based Gait Recognition: A Review (2015, background): “Such approaches have been examined in several areas, mostly in sports ( i.e. , step count and gait speed estimation [9]) and clinical applications (assessment of user’s health state based on gait abnormalities [10], fall detection [11], etc. ).”
Mobile Phone Sensing Systems: A Survey (2013, background): “The authors wish to acknowledge the anonymous reviewers for their valuable comments and special thanks to Jun Zhang & Chao Chen (Deakin University) for helping to prepare this manuscripts.”
A review of wearable sensors and systems with application in rehabilitation (2012, background): “Often, these systems combine fall detection with localization of the person who fell via a GPS-based method [77,78].”
Human fall detection on embedded platform using depth maps and wireless accelerometer (2014, methodology): “For instance, in iFall application [14], data from the accelerometer is evaluated using several threshold-based algorithms and position data to determine the person’s fall.”