PRE2020 Group 1 numbers: Difference between revisions
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|<!--Col1--> | |<!--Col1-->Measurement station cycle time | ||
|<!--Col2-->4 minutes | |<!--Col2-->4 minutes | ||
|<!--Col3-->1 minute testing + 2 minutes disinfecting + 1 spare minute | |<!--Col3-->1 minute testing + 2 minutes disinfecting + 1 spare minute | ||
| Line 70: | Line 70: | ||
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|<!--Col1--> | |<!--Col1-->Estimated cost of the total system | ||
|<!--Col2-->€10950 | |<!--Col2-->€10950 | ||
|<!--Col3--> | |<!--Col3-->€5500 for thermal camera, €1000 for microphones, €100 for microphone in cabin, €400 for camera on pole, €750 for camera pole, €3000 for booth including computer, software, screens, and interfaces, €200 for UV light | ||
|<!--Col4-->Various webshops | |<!--Col4-->Various webshops | ||
Latest revision as of 10:10, 15 October 2020
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| Name | Value | Explanation | Source |
| Microphone array range | 10x10 meter | Range provided by manufacturer | [1] |
| Microphone mounting height | 2.5 meters | Minimum mounting height provided by manufacturer | [1] |
| Distance between detection device and measurement station | 2 meters | Preferred value | |
| Percentage of infected people | 0.1% | This is the amount of people currently tested positive for COVID-19. Some of these people are in the hospital and do not count, also the people tested positive will be a lot more careful coming into contact with others. However there are also people that have COVID-19 that are not tested. This will even out and so the same percentage of infected people was taken. | [2] |
| Chance of detecting a cough with camera and microphone | 87% | Chance obtained by Korean researchers using a similar system | [3] |
| Measurement station cycle time | 4 minutes | 1 minute testing + 2 minutes disinfecting + 1 spare minute | |
| Expected false positive rate in measurement station | 7% | Study of similar cough classification algorithm achieved 96.6% accuracy. In this specific case this becomes higher than only the cough test since only 1 test needs to be positive to refuse access | [4] |
| Expected false negative rate in measurement station | 7% | Study of similar cough classification algorithm achieved 96.6% accuracy. In this specific case this becomes higher than only the cough test since only 1 test needs to be positive to refuse access | [4] |
| Frequency of random person getting picked out | After every 2 coughing persons detected, 1 person is picked out randomly, if there is no queue | This will ensure random people are not picked too often/not often enough and also random persons will not have to wait in a queue | |
| Chance a healthy person coughs when in range of the microhpone | 0.08% | Based on average cough time and walking speed | [5] |
| Estimated cost of the total system | €10950 | €5500 for thermal camera, €1000 for microphones, €100 for microphone in cabin, €400 for camera on pole, €750 for camera pole, €3000 for booth including computer, software, screens, and interfaces, €200 for UV light | Various webshops |
| Salary of medical personnel | €25 per hour | Obtained from dad of group member who is a salary administrator | |
| Dutch reproduction rate of COVID-19 | 3 | [6] | |
| Number of people in half an hour entering Heuvel Galerie during a quiet hour at a quiet entrance | 24 | Counted by group members | |
| Number of people in half an hour entering Heuvel Galerie during a quiet hour at a busy entrance | 61 | Counted by group members | |
| Number of people in half an hour entering Heuvel Galerie during a medium busy hour at a quiet entrance | 117 | Counted by group members | |
| Number of people in half an hour entering Heuvel Galerie during a medium busy hour at a busy entrance | 194 | Counted by group members | |
| Number of people in half an hour entering Heuvel Galerie during a busy hour at a quiet entrance | 233 | Counted by group members | |
| Number of people in half an hour entering Heuvel Galerie during a busy hour at a busy entrance | 756 | Counted by group members |
- ↑ 1.0 1.1 [1] Sorama. Sorama Smart City Listener.
- ↑ [2] RIVM. Actuele informatie over het nieuwe coronavirus.
- ↑ [3] Center for Noise and Vibration Control KAIST (2020). Deep Learning-Based Cough Recognition Model Helps Detect the Location of Coughing Sounds in Real Time.
- ↑ 4.0 4.1 [4] Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, S., Ali, K., John, C. N., Hussain, I., & Nabeel, M. (2020). AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in Medicine Unlocked, 20, 100378.
- ↑ [5] Encyclopedia of Children's Health. Cough.
- ↑ [6] RIVM. Verspreiding COVID-19.