A smartphone based real-time daily activity monitoring system. Shumei Zhang Paul McCullagh Jing Zhang Tiezhong Yu

Transcription

1 A smartphone based real-time daily activity monitoring system Shumei Zhang Paul McCullagh Jing Zhang Tiezhong Yu

2 Outline Contribution Background Methodology Experiments

3 Contribution This paper proposes a new approach to detect falls with: Rule-based classification algorithm consideration of context (what people did before falls) empirical threshold variables implementation of a smart phone based activity monitoring system Basically, classification of human activities

4 Background Automatic monitoring of daily activities: records heights you climb and the number of steps you walk lead to a healthier lifestyle and promote regular exercise Also, assist elderly people living independently at home Daily activity monitoring with context-aware reminder has the potential to reduce the occurrence of chronic diseases Falls are the leading cause of injury and a major global health problem, particular for the elderly population

5 Background - continue Chronic heart failure or abnormal heart rate lead to higher risk of falling Approximately 3% people who experience a fall will remain on the ground for 20mins prior to receiving assistance

6 Methodology HTC (android system) smart phone Specification: embedded BMA150 3D accelerometer AK8973 3D Magnetic sensor AK8973 orientation sensor GPS Wi-Fi sensors memory: 512 MB Space: 2 GB Version of operating system: Android version 2.3.3

7 Methodology Phone is belt-worn on the left side of the waist Related work shows that waist and head give more accurate results for fall detection in a horizontal orientation (x is vertical, y is horizontal and z is orthogonal to the screen)

8 Feature Collection Phone is belt-worn on the left side of the waist Related work shows that waist and head give more accurate results for fall detection In a horizontal orientation (x is vertical, y is horizontal and z is orthogonal to the screen)

9 Feature Collection The phone s orientation can be obtained from orientation sensor Measure 3D rotation angles along the three axises

10 Features Collection Two kinds of features are collected: 3D acceleration (t, Ax, Ay, Az) 3D rotation angles (t, θx, θy, θz)

11 Sampling frequency Can be from 5 Hz to 80 Hz Low frequency might miss some information Low frequency gives a lower data load and higher efficiency of data processing Related work stated that classification accuracy is greater than 95% irrespectively with the sampling frequency at: 80 Hz (96.9 % ± 1 %) 40 Hz (97.4 % ± 0.7 %) 20 Hz (96.9 % ± 1.1 %) 10 Hz (97 % ± 1 %) 5 Hz (95 % ± 1.4 %) In this paper, they chose 5Hz

12 Acceleration 3D acceleration (t, Ax, Ay, Az) Ax, Ay, Az = (-g, +g) g is the gravity acceleration

13 Rotation 3D rotation angles (θx, θy, θz) θx: [-180, 180]; 0 = horizontal; ±180 = upside down; ±90 = left/right θy: [ 90,90 ]. 0 = horizontal; 90 = upward; 90 = downward θz: [0, 360 ]. 0 /360 = North; 180 = South; 90 = East; 270 = West

14 Posture Classification Based on the data set (t,id,a x,a y,a z,δa, θ X,θ Y,θ Z ) ΔA acceleration change id: id of the time segment Includes: Motionless posture classification Motion posture classification

15 Motionless Posture Classification Equation 3 and 4 is the rule for motionless posture mlp = 2s th1 = 0.4m/s^2 R(lyi) is for lying R(til) is for tilt θcali = 20%

16 Motion Posture Classification Rule for detecting motion posture mp is 1s smp is 2s th2 = 3.5m/s^2 PT is posture transition

17 Example Motion periods: < t 0,t 1 >;< t 2,t 3 >;< t 4,t 5 > T m Motionless periods: < t 1,t 2 >;< t 3,t 4 >;< t 5,t 6 > Posture array: {pos(t 0 ), pos(t 1 ),..., pos(t 7 )}

18 T(rest) is the prescheduled rest Fall Detection

19 Experiments Indoor environment 6 healthy people (5 male and 1 female, age range 20-52) Two independent observers Compared with AccThr (uses only acceleration)

20 Determine th1 and th2 Ask six subjects to carry out three motionless activities Calculate ΔA Sort ΔA from highest to lowest 98.6% samples are less than 0.4m/s^2

21 Continue on Experiments Ask 6 people to perform normal daily activities Ask 6 people to perform fall or fall-like activities

22 72 falls ending with lying 72 falls ending with sitting tilted 72 normal lying 36 bending and a number of standing walking, sitting and fall-like activities (jumping and sitting down heavily)

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24 Weaknesses Need bigger data volume Experiments with 6 subject might not be enough Age of subjects now is only 20-52, might need to test more Authors did not mentions the data processing on server side Lack analysis of their results

25 Strengths Easy to follow The results are promising comparing with old techniques The empirical study is good, make them confident to set those threshold variables Their choices for data collection are well supported by related work

26 Related Work We have seen many related work that classify daily activities with machine learning algorithm (SVM, Neural Network, Naive Bayes ) There is a commercial fall detectors MCT-241MD PERS (with built-in tilt sensor) Other related work that support the choices made by this paper

27 Future Work How to improve the results for bending