Development of a Fall Detecting System for the Elderly Residents speaker: 林佑威 Author: Chia-Chi Wang, Chih-Yen Chiang, Po-Yen Lin, Yi-Chieh Chou, I-Ting Kuo, Chih-Ning Huang, Chia-Tai Chan Bioinformatics and Biomedical Engineering, ICBBE The 2nd International Conference on 1

Outline I. Introduction II. Method III. Experimental Results IV. Conclusion 2

Introduction 25~35% of elderly residents experienced fall-related injury more than one time per year. 30~40% of all needed to be hospitalized. 3% of the fallers helplessly lie without any external support for more than 20 minutes The cost forecasting of medical care for elderly residents’ fall- related injury goes to $43.8 billion by

Research (2003) Thomas Degen et al. inlaid two accelerometers into a wrist watch (2006) C.C. Yanget al. used a triple-axial accelerometer placed at the waist level (2005) U. Lindemann et al. proposed a pilot study with two accelerometers into the hearing aid housing 4

Method 5

Sensor Position Accelerometer has been used in various studies to monitor a range of human movement The paper Inlaid the accelerometers into the hearing aid housing 6

Four Criteria on Fall Detection A accelerometer was placed above the ear side The sample rate of the accelerometer was 200Hz. 7 X軸X軸 Y軸Y軸 Z軸Z軸

Four Criteria on Fall Detection (1). Sum-vector of all axes (Sa): it is used to describe the spatial variation of acceleration during the falling interval. 8

Four Criteria on Fall Detection (2).Sum-vector of horizontal plane (Sh): An acceleration change of the horizontal plane (x- z plane) 9

Four Criteria on Fall Detection Timestamp of falling body to be at rest (Trs) Timestamp of the body’s initial contact to the ground (Tic) 10

Four Criteria on Fall Detection Backward integration of reference velocity (Vmax) According to the dynamics of free-falling objects, 0.2 meters height of potential energy completely transformed into kinetic energy may give rise to a velocity of 2 m/s. 11

Four Criteria on Fall Detection 12 Flow of fall detection

Experimental Results 1.Five volunteers 2.Eight kinds of falling posture 3.Seven daily activities 13

Seven daily activities The seven activities include standing, sitting down, lying down, walking, jumping, going up (down) stairs, and jogging 14

Eight kinds of falling posture 15

Falling:Right-Side to the Ground 16

Lie Down Twice:Slow then Quick 17

Experimnets 18

Conclusion These experimental results have demonstrated the proposed falls detection is effective The algorithm had been accomplished The data need to be transmitted to the central computer to do further data analysis 19

Conclusion The future work : 1.Bluetooth module 2.Alarm system with VoIP or SMS communications. 20