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Development of a Low False-Alarm-Rate Fall-Down Detection System Based on Machine Learning for Senior Health Care

Sui, Yongkun
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310208

Abstract Details

2015, MS, University of Cincinnati, Engineering and Applied Science: Electrical Engineering.
The objective of this thesis is to develop a low false-alarm-rate fall-down detection system for senior health care with an inertial measurement unit (IMU) and a microcontroller embedded with machine learning algorithm. Fatal delay in medical treatment caused by unconsciousness after seniors’ fall-down results in thousands of death each year in US. Several different types of fall-down detection systems have been developed for seniors, especially those who live alone to send emergency notification to their families or emergency care agents. Recently, research has been conducted to reduce the high false-alarm-rate that fall-down detection systems suffer due to poor motion recognition. In this work, a prototype IMU-based smart fall-down detection system and low false-alarm-rate fall-down recognition algorithms have been newly developed. The fall-down detection system is composed of a combination of 3-axis gyroscope and a 3-axis accelerometer as the sensing unit, a push button and alarm as input/output, and a microcontroller as the control and data processing unit. The system combines angular velocity provided by the gyroscope and acceleration provided by the accelerometer to get the inclination data for orientation characterization. The impact data derived from the acceleration indicates the severity of the fall-down. The system has been calibrated by commercially available IMU to ensure its accuracy and stability. Fall-down and other motions are simulated with this system in this work. A low false-alarm-rate fall-down detection algorithm based on machine learning is developed and fully characterized in this work. The algorithm allows the detection system to be customizable by collecting false-alarm reports from the users. The algorithm extracts the pattern of similar false-alarm motions then trains the motion database to classify them as “safe motion”. Motions similar to known “safe motion” will be ignored by the detection system to reduce the false-alarm-rate. With 50 sets of “safe motion” training data for the test of the development system, the achieved specificity of the fall-down detection system is 90 % which means a false-alarm-rate of 10 % with a relatively high sensitivity of 96 %. In conclusion, the low false-alarm-rate fall-down detection system with an inertial measurement unit (IMU) and a microcontroller embedded with machine learning algorithm has been successfully developed and characterized in this work, and the developed system can be greatly helpful for the health care of senior fall-down.
Chong Ahn, Ph.D. (Committee Chair)
Wen-Ben Jone, Ph.D. (Committee Member)
Ian Papautsky, Ph.D. (Committee Member)
42 p.
Electrical Engineering
fall detection; low false-alarm-rate; machine learning

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Citations

  • Sui, Y. (2015). Development of a Low False-Alarm-Rate Fall-Down Detection System Based on Machine Learning for Senior Health Care [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310208

    APA Style (7th edition)

  • Sui, Yongkun. Development of a Low False-Alarm-Rate Fall-Down Detection System Based on Machine Learning for Senior Health Care. 2015. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310208.

    MLA Style (8th edition)

  • Sui, Yongkun. "Development of a Low False-Alarm-Rate Fall-Down Detection System Based on Machine Learning for Senior Health Care." Master's thesis, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310208

    Chicago Manual of Style (17th edition)

ucin1439310208
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This open access ETD is published by University of Cincinnati and OhioLINK.