A Hybrid Algorithm for Fall Detection

Stud Health Technol Inform. 2017;237:163-168.

Abstract

Falling is a major problem among the people globally, frequently due to some health problems including vision loss or balance disorder as a consequence of aging. As a result of the falling in elder people; injuries, complications, neurological problems and mortality are generally occurred. This situation also affects the patients' families psychologically. A large number of studies show that significant fractures, injuries and in some cases death are commonly encountered in elderly. Nonetheless, in the immediate intervention, the rate of damage is reduced and the life quality can be significantly restored. The purpose of this paper is to reduce fall-related problems by developing a new fall detector that we called Tesodev fall detector. In addition, a proper algorithm and a wearable electronic device attaching the sensor to patient's clothes are provided. The electronic device is an IOT device which can send and receive data wirelessly, which means that the device can inform the medical centres to improve medical attention time. Also, this device is modular and device allows keeping other medical data such as blood sugar, tension, heart rate, SPO2. Sensitivity, error rate and classification accuracy in this study are 89.8%, 23.4%, 76%, respectively.

Keywords: Fall Detector; Geriatric Patients; IOT; Prototype.

MeSH terms

  • Accidental Falls*
  • Aged
  • Aging
  • Algorithms*
  • Humans
  • Monitoring, Ambulatory*
  • Quality of Life
  • Wearable Electronic Devices*