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falldetectionsystem [2014/12/09 08:05]
mroriz [4.2.1 Experiment 4] 		falldetectionsystem [2014/12/09 08:11]
mroriz [4.1.3 Experiment 3]
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 In the first experiment we define the correct values for the Lay thresholds. We put the accelerometer in inactivity state only varying the orientation of the axis simulating a person that move very slowly over the floor and collected the accelerometer data during approximately 3 minutes at a frequency of one sample per half second for a total of 369 samples. Then we calculate the maximum and minimum values of acceleration (module(x, y, z)), the mean and the standard deviation. The values obtained were MEAN: 0,987053732, STANDARD DEVIATION: 0,043657314, MAX: 1,325917283, MIN: 0,698281932. We detect that the maximum and minimum values are out of the range [mean+sd, mean-sd] and so we decide that the better thresholds are the maximum and minimum values. In the first experiment we define the correct values for the Lay thresholds. We put the accelerometer in inactivity state only varying the orientation of the axis simulating a person that move very slowly over the floor and collected the accelerometer data during approximately 3 minutes at a frequency of one sample per half second for a total of 369 samples. Then we calculate the maximum and minimum values of acceleration (module(x, y, z)), the mean and the standard deviation. The values obtained were MEAN: 0,987053732, STANDARD DEVIATION: 0,043657314, MAX: 1,325917283, MIN: 0,698281932. We detect that the maximum and minimum values are out of the range [mean+sd, mean-sd] and so we decide that the better thresholds are the maximum and minimum values.
  
-{{pmu%20relatorio%202_files:image016.png?494x181}}Figura 7:Experiment 1 Data+{{image016.png?494x181}}Figura 7:Experiment 1 Data
  
 === Formalization === === Formalization ===
  
-·         Objective: Determinate the correct values for the Lay thresholds.+  * Objective: Determinate the correct values for the Lay thresholds.
  
-·         Set-up: accelerometer in inactivity state, recording of the values of the accelerometer during approximately 3 minutes at a frequency of one sample per half second for a total of 369 samples+  * Set-up: accelerometer in inactivity state, recording of the values of the accelerometer during approximately 3 minutes at a frequency of one sample per half second for a total of 369 samples
  
-·         Parameters to be varied: change the orientation of the axis of accelerometer simulating a person that move very slowly over the floor  +  * Parameters to be varied: change the orientation of the axis of accelerometer simulating a person that move very slowly over the floor 
- +
-·         Metrics:  +
- +
-module(x,y,z)= {{pmu%20relatorio%202_files:image008.png?89x24}},+
  
 +  * Metrics: 
 +module(x,y,z)= {{image008.png?89x24}},
 meam(module(x,y,z)) , meam(module(x,y,z)) ,
- 
 standar_deviation(module(x,y,z)), standar_deviation(module(x,y,z)),
- 
 Min(module(x,y,z)), Min(module(x,y,z)),
- 
 Max(module(x,y,z)) Max(module(x,y,z))
  
-·         Results: We detect that the maximum and minimum values are out of the range [mean+sd, mean-sd] and so we decide that the better thresholds are the maximum and minimum values.+  * Results: We detect that the maximum and minimum values are out of the range [mean+sd, mean-sd] and so we decide that the better thresholds are the maximum and minimum values.
  
 MEAN: 0,987053732 MEAN: 0,987053732
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 In the second experiment we define the correct values for the Fall thresholds. We put the accelerometer in the poked of pants, and executed standard daily activities such as stand, sit, walk, in different orders and repeatedly; collecting the accelerometer data during approximately 2 minutes at a frequency of one sample per half second for a total of 234 samples. Then we calculate the maximum value and minimum value of acceleration (module(x, y, z)), the mean and the standard deviation. The values obtained were MEAN: 0,987490079, STANDARD DEVIATION: 0,129584083, MAX: 1,855629105, MIN: 0,512538108. We detect that the maximum and minimum values are out of the range [mean+sd, mean-sd] and so we decide that the better thresholds are the maximum and minimum values. In the second experiment we define the correct values for the Fall thresholds. We put the accelerometer in the poked of pants, and executed standard daily activities such as stand, sit, walk, in different orders and repeatedly; collecting the accelerometer data during approximately 2 minutes at a frequency of one sample per half second for a total of 234 samples. Then we calculate the maximum value and minimum value of acceleration (module(x, y, z)), the mean and the standard deviation. The values obtained were MEAN: 0,987490079, STANDARD DEVIATION: 0,129584083, MAX: 1,855629105, MIN: 0,512538108. We detect that the maximum and minimum values are out of the range [mean+sd, mean-sd] and so we decide that the better thresholds are the maximum and minimum values.
  
-{{pmu%20relatorio%202_files:image018.png?472x187}}Figura 8: Experiment 2 Data+{{image018.png?472x187}}Figura 8: Experiment 2 Data
  
 === Formalization === === Formalization ===
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 In the third experiment we validate the thresholds found in the second experiment with accelerometer data that contain values of fall. For that we put the accelerometer at the height of the pocket of pants, and throw it on the floor for 6 times and between each time we performed different activities such as stand, sit, walk. We collected the accelerometer data during 2 minutes at a frequency of one sample per half second for a total of 239 samples. We calculated the mean of the module and then we created a function that detect the falls in the same way that the Esper Rule, with the Fall Threshold. In the third experiment we validate the thresholds found in the second experiment with accelerometer data that contain values of fall. For that we put the accelerometer at the height of the pocket of pants, and throw it on the floor for 6 times and between each time we performed different activities such as stand, sit, walk. We collected the accelerometer data during 2 minutes at a frequency of one sample per half second for a total of 239 samples. We calculated the mean of the module and then we created a function that detect the falls in the same way that the Esper Rule, with the Fall Threshold.
  
-{{pmu%20relatorio%202_files:image020.png?536x31}}As result, all the 6 times the function detects the fall.+{{image020.png?536x31}}As result, all the 6 times the function detects the fall.
  
-{{pmu%20relatorio%202_files:image022.png?527x175}}Figura 9: Experiment 3 Data+{{image022.png?527x175}}Figura 9: Experiment 3 Data
  
 === Formalization === === Formalization ===
  
-·         Objective: validate the thresholds found in the second experiment with accelerometer data that contain values of fall, creating a function fall(x) that detect the falls in the same way that the Esper Rule.+  * Objective: validate the thresholds found in the second experiment with accelerometer data that contain values of fall, creating a function fall(x) that detect the falls in the same way that the Esper Rule.
  
-·         Set-up: accelerometer at the height of the pocket of pants, and throw it on the floor for 6 times and between each fall we performed different activities such as stand, sit, walk. We collected the accelerometer data during 2 minutes at a frequency of one sample per half second for a total of 239 samples.+  * Set-up: accelerometer at the height of the pocket of pants, and throw it on the floor for 6 times and between each fall we performed different activities such as stand, sit, walk. We collected the accelerometer data during 2 minutes at a frequency of one sample per half second for a total of 239 samples.
  
-·         Parameters to be varied: activities performed: none, walk, sit, stand and fall +  * Parameters to be varied: activities performed: none, walk, sit, stand and fall
- +
-·         Metrics:+
  
-{{pmu%20relatorio%202_files:image024.png?145x20}} {{pmu%20relatorio%202_files:image026.png?516x31}}+  * Metrics:
  
-Where {{pmu%20relatorio%202_files:image028.png?246x24}} +{{image024.png?145x20}} {{image026.png?516x31}}
  
-·         Results: As result, all the 6 times the function detects the fall.+Where {{image028.png?246x24}} 
  
 +  * Results: As result, all the 6 times the function detects the fall.
 ===== 4.2 Test of thresholds ===== ===== 4.2 Test of thresholds =====
  
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