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Elderly Fall Detection Using Smart Wearable Devices

Arezoomand, Arman | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53187 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Arghavani Hadi, Jamal; Fakharzadeh, Mohammad
  7. Abstract:
  8. Elderly falls are one of the leading causes of injury and death to this growing population. In this project, an elderly fall detection system has been designed and built in the form of a wearable device that allows the elderly to be constantly monitored by wearing it continuously. The system hardware is made in the form of a wearable watch that fastens to the left hand of the elderly and continuously monitors the signals of its three sensors, which are accelerometer, gyroscope, and magnetometer. The system continually applies its initial diagnostic method to the signals and sends motion information to a host computer via a wireless connection in the event of a fall. The primary diagnostic software on the computer analyzes the signals and accurately detects falls from daily activities of life. The diagnostic results of this system are automatically reported to the relevant people, such as nurses, relatives, or emergency staff. By providing timely assistance to the person, secondary risks are minimized. The diagnostic software of this system uses convolutional neural networks, which is a kind of deep learning methods. The dataset that this network has been trained with includes 1070 samples, part of which has been produced using the wearable device built for this research. These samples are stored from the daily activities and falls of 11 subjects with different physical characteristics. Then, using 6 different methods, the motion signals were converted into color digital images to train the two proposed convolutional neural networks. Finally, the results of combining these 6 methods with 2 proposed networks were compared. The final software can classify the samples in the project-specific dataset without error. The system hardware has low power consumption, and despite using all three common motion sensors, it can provide service for 30 hours without the need for charging
  9. Keywords:
  10. Convolutional Neural Network ; Inertial Sensor ; Deep Learning ; Wearable Assistive Device ; Falling Detection ; Optimal Power Consumption

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