Tom Yun
CTVNews.ca writer
Sunday, September 26, 2021
Smartphone sensor data combined with time-of-day data resulted in an accuracy rate of 90 per cent when it came to detecting cannabis use, a new study found.
Researchers from Rutgers University in New Jersey say smartphone sensor data combined with machine learning could detect whether someone is under the influence of marijuana.
The researchers set out to develop a proof-of-concept way to passively detect cannabis use as an alternative to existing detection measures, such as blood, urine or saliva tests. Their findings were published in September in the journal Drug and Alcohol Dependence.
"Adverse effects of acute cannabis intoxication have been reported by young adults, with associated consequences such as poor academic and work performance, and injuries and fatalities due to driving while 'high' on cannabis," the authors wrote in the study.
The authors conducted a study experiment involving 57 young adults who reported using cannabis at least twice a week. The participants were asked to complete three surveys a day over a 30-day period that asked how high they were feeling at a given time, as well as when they had last used cannabis and the quantity consumed. In total, the participants reported 451 episodes of cannabis use.
The participants were also asked to download an app that analyzed GPS data, phone logs, accelerometer data and other smartphone sensors and usage statistics.
When only looking at the time of day, the algorithm was able to accurately detect an episode of cannabis use with 60 per cent accuracy. The smartphone sensor data alone was also able to produce an accuracy rate of 67 per cent.
However, smartphone sensor data combined with time-of-day data resulted in an accuracy rate of 90 per cent.
“Using the sensors in a person’s phone, we might be able to detect when a person might be experiencing cannabis intoxication and deliver a brief intervention when and where it might have the most impact to reduce cannabis-related harm,” said corresponding author and Rutgers professor Tammy Chung in a news release.
The GPS data was the most important dataset when it came to detecting cannabis use. The researchers found that participants would tend to travel shorter distances while they were high. Accelerometer data was the second most important feature, as it can be used to measure body movements.
The researchers say this is the first study to look at how smartphone sensors could be used to detect cannabis intoxication.
Chung and her colleagues were also involved in a similar study from 2018 that investigated whether smartphone data could detect heavy drinking episodes. In that study, they found that an algorithm that measured smartphone-usage patterns, such as screen-on duration, typing speed and time of day, could detect heavy drinking episodes with 91 per cent accuracy.
Sunday, September 26, 2021
Smartphone sensor data combined with time-of-day data resulted in an accuracy rate of 90 per cent when it came to detecting cannabis use, a new study found.
Researchers from Rutgers University in New Jersey say smartphone sensor data combined with machine learning could detect whether someone is under the influence of marijuana.
The researchers set out to develop a proof-of-concept way to passively detect cannabis use as an alternative to existing detection measures, such as blood, urine or saliva tests. Their findings were published in September in the journal Drug and Alcohol Dependence.
"Adverse effects of acute cannabis intoxication have been reported by young adults, with associated consequences such as poor academic and work performance, and injuries and fatalities due to driving while 'high' on cannabis," the authors wrote in the study.
The authors conducted a study experiment involving 57 young adults who reported using cannabis at least twice a week. The participants were asked to complete three surveys a day over a 30-day period that asked how high they were feeling at a given time, as well as when they had last used cannabis and the quantity consumed. In total, the participants reported 451 episodes of cannabis use.
The participants were also asked to download an app that analyzed GPS data, phone logs, accelerometer data and other smartphone sensors and usage statistics.
When only looking at the time of day, the algorithm was able to accurately detect an episode of cannabis use with 60 per cent accuracy. The smartphone sensor data alone was also able to produce an accuracy rate of 67 per cent.
However, smartphone sensor data combined with time-of-day data resulted in an accuracy rate of 90 per cent.
“Using the sensors in a person’s phone, we might be able to detect when a person might be experiencing cannabis intoxication and deliver a brief intervention when and where it might have the most impact to reduce cannabis-related harm,” said corresponding author and Rutgers professor Tammy Chung in a news release.
The GPS data was the most important dataset when it came to detecting cannabis use. The researchers found that participants would tend to travel shorter distances while they were high. Accelerometer data was the second most important feature, as it can be used to measure body movements.
The researchers say this is the first study to look at how smartphone sensors could be used to detect cannabis intoxication.
Chung and her colleagues were also involved in a similar study from 2018 that investigated whether smartphone data could detect heavy drinking episodes. In that study, they found that an algorithm that measured smartphone-usage patterns, such as screen-on duration, typing speed and time of day, could detect heavy drinking episodes with 91 per cent accuracy.
Smartphones used to check water for pollutants – by tracking paramecia
By Ben Coxworth
September 24, 2021
A pair of Paramecium aurelia swim through a water sample
Amai 129/C.C. 4.0
Even though it's vitally important for people in impoverished nations to check drinking water sources for pollutants, they often lack the facilities for performing such tests. A new system could help, as it uses a smartphone camera to check up on tiny aquatic organisms.
Developed by scientists at the Singapore University of Technology and Design, the setup can be used to analyze untreated water samples from lakes or rivers on the spot, within a matter of minutes.
A team led by Asst. Prof. Javier Fernandez started by observing single-celled organisms known as paramecia, which are abundant in bodies of water throughout the world. The researchers initially noted the average swimming speed of the organisms in untainted water, and then observed how much that speed decreased as different concentrations of pollutants such as heavy metals and antibiotics were introduced.
When the scientists subsequently measured the swimming speed of paramecia in water samples – utilizing a simple microscope attachment on a smartphone camera, along with object tracking algorithms – they found that they could accurately determine how polluted the water was, based on how much slower than normal the organisms were swimming.
For instance, even when heavy metals were present in concentrations considered to be half of what's safe for humans to consume, the swimming speed of the paramecia decreased by half.
"Taking a sample of water and measuring the speed of paramecia can therefore be used as a straightforward method to assess the drinkability of water without the need for specialized equipment or chemicals," says Fernandez. "Usually, you would need a different test for each pollutant, but paramecia swimming is a global measurement."
The research is described in a paper that was recently published in the journal Scientific Reports.
Source: Singapore University of Technology and Design
By Ben Coxworth
September 24, 2021
A pair of Paramecium aurelia swim through a water sample
Amai 129/C.C. 4.0
Even though it's vitally important for people in impoverished nations to check drinking water sources for pollutants, they often lack the facilities for performing such tests. A new system could help, as it uses a smartphone camera to check up on tiny aquatic organisms.
Developed by scientists at the Singapore University of Technology and Design, the setup can be used to analyze untreated water samples from lakes or rivers on the spot, within a matter of minutes.
A team led by Asst. Prof. Javier Fernandez started by observing single-celled organisms known as paramecia, which are abundant in bodies of water throughout the world. The researchers initially noted the average swimming speed of the organisms in untainted water, and then observed how much that speed decreased as different concentrations of pollutants such as heavy metals and antibiotics were introduced.
When the scientists subsequently measured the swimming speed of paramecia in water samples – utilizing a simple microscope attachment on a smartphone camera, along with object tracking algorithms – they found that they could accurately determine how polluted the water was, based on how much slower than normal the organisms were swimming.
For instance, even when heavy metals were present in concentrations considered to be half of what's safe for humans to consume, the swimming speed of the paramecia decreased by half.
"Taking a sample of water and measuring the speed of paramecia can therefore be used as a straightforward method to assess the drinkability of water without the need for specialized equipment or chemicals," says Fernandez. "Usually, you would need a different test for each pollutant, but paramecia swimming is a global measurement."
The research is described in a paper that was recently published in the journal Scientific Reports.
Source: Singapore University of Technology and Design
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