suyash16999 / SisFallAnalysis

Analysis of the SisFall fall detection dataset with readings from accelerometer and gyroscope.

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Analysis of the SisFall Dataset for Fall Detection

Matthew Johnson, July 25, 2018 (last updated August 24, 2018)

A dataset of performed trials of activities of daily living (ADLs) and falls with subjects wearing two triaxis accelerometers and a gyroscope.


I used features common throughout related literature, specifically from [3]:

Sum Vector Magnitude = Image

Sum Vector Magnitude on Horizontal Plane = Image

Angle between z-axis and vertical = Image

Standard deviation magnitude on horizontal plane = Image

Standard deviation magnitude = Image

Signal Magnitude Area = Image

Signal Magnitude Area on horizontal plane = Image

SisFall Dataset: http://sistemic.udea.edu.co/en/investigacion/proyectos/english-falls/

Sources:
  • [1] Automatic Fall Monitoring: A Review
    Natthapon Pannurat, Surapa Thiemjarus, and Ekawit Nantajeewarawat
  • [2] Real-life/real-time elderly fall detection with a triaxial accelerometer
    A. Sucerquia, J.D. López and J.F. Vargas-Bonilla
  • [3] SisFall: A Fall and Movement Dataset
    A. Sucerquia, J.D. López and J.F. Vargas-Bonilla
  • [4] Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model
    Dongha Lim, Chulho Park, Nam Ho Kim, Sang-Hoon Kim, and Yun Seop Yu

Sensor Orientation:

Image


Dataset Information

Activities of Daily Living (ADLs):

Code Activity # Trials Trial Length
D01 Walking slowly 1 100s
D02 Walking quickly 1 100s
D03 Jogging slowly 1 100s
D04 Jogging quickly 1 100s
D05 Walking upstairs and downstairs slowly 5 25s
D06 Walking upstairs and downstairs quickly 5 25s
D07 Slowly sit in a half height chair, wait a moment, and up slowly 5 12s
D08 Quickly sit in a half height chair, wait a moment, and up quickly 5 12s
D09 Slowly sit in a low height chair, wait a moment, and up slowly 5 12s
D10 Quickly sit in a low height chair, wait a moment, and up quickly 5 12s
D11 Sitting a moment, trying to get up, and collapse into a chair 5 12s
D12 Sitting a moment, lying slowly, wait a moment, and sit again 5 12s
D13 Sitting a moment, lying quickly, wait a moment, and sit again 5 12s
D14 Being on oneís back change to lateral position, wait a moment, and change to oneís back 5 12s
D15 Standing, slowly bending at knees, and getting up 5 12s
D16 Standing, slowly bending without bending knees, and getting up 5 12s
D17 Standing, get into a car, remain seated and get out of the car 5 25s
D18 Stumble while walking 5 12s
D19 Gently jump without falling (trying to reach a high object) 5 12s

Falls:

Code Activity # Trials Trial Length
F01 Fall forward while walking caused by a slip 5 15s
F02 Fall backward while walking caused by a slip 5 15s
F03 Lateral fall while walking caused by a slip 5 15s
F04 Fall forward while walking caused by a trip 5 15s
F05 Fall forward while jogging caused by a trip 5 15s
F06 Vertical fall while walking caused by fainting 5 15s
F07 Fall while walking, with use of hands in a table to dampen fall, caused by fainting 5 15s
F08 Fall forward when trying to get up 5 15s
F10 Fall forward when trying to sit down 5 15s
F11 Fall backward when trying to sit down 5 15s
F09 Lateral fall when trying to get up 5 15s
F12 Lateral fall when trying to sit down 5 15s
F13 Fall forward while sitting, caused by fainting or falling asleep 5 15s
F14 Fall backward while sitting, caused by fainting or falling asleep 5 15s
F15 Lateral fall while sitting, caused by fainting or falling asleep 5 15s

Subjects:

Subject Age Height Weight Gender Subject Age Height Weight Gender
SA01 26 165 53 F SA13 22 157 55 F
SA02 23 176 58.5 M SA14 27 160 46 F
SA03 19 156 48 F SA15 25 160 52 F
SA04 23 170 72 M SA16 20 169 61 F
SA05 22 172 69.5 M SA17 23 182 75 M
SA06 21 169 58 M SA18 23 181 73 M
SA07 21 156 63 F SA19 30 170 76 M
SA08 21 149 41.5 F SA20 30 150 42 F
SA09 24 165 64 M SA21 30 183 68 M
SA10 21 177 67 M SA22 19 158 50.5 F
SA11 19 170 80.5 M SA23 24 156 48 F
SA12 25 153 47 F

Correlations:

I have noticed that the average of the maximum obtained value of horizontal vector magnitude standard deviation is inversely proportional to the amplitude of the same averaged measure in the first four falls, on a per activity per subject basis. The correlation between the two aforementioned variables is -0.89. The first four ADLs are walking and jogging so I am trying to use them as a baseline measure for personalized variance for threshold fall detection methods.

Image
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Plotting method examples:

plot_trials(): (Using set thresholds)
Image
plot_one_from_each():
Image
plot_feats():
Image

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Analysis of the SisFall fall detection dataset with readings from accelerometer and gyroscope.


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