Faking a cold to get out of work? AI might tell if you’re actually sick
Global News
A new study shows that artificial intelligence is pretty good at predicting the common cold, and could be used by employers to find out whether or not workers are sick.
Artificial intelligence is changing the way we work, do school assignments, enjoy our airwaves and now … take sick days?
Anyone planning to fake an illness in the future might want to rethink their plans, as scientists are working on AI technology that will be able to detect whether a person is sick or not, based on the sound of their voice.
Researchers from Sardar Vallabhbhai National Institute of Technology in India have been studying how to diagnose illness using non-invasive techniques, and are in the process of developing a method that can identify when a person has a common cold, based on their speech sounds.
The study, as reported by The Economist and published earlier this year in the journal Biomedical Signal Processing and Control, is designed to help diagnose illness over the phone in an effort to minimize medical appointments and prevent the spread of viruses, but it might also make it harder for people to call in sick when they’re not actually ill.
The researchers, led by electronic engineer Pankaj Warule, analyzed the voice patterns of 630 German people, 111 of whom had a cold. They hypothesized that a cold could upset the vocal patterns normally present in those who are feeling well.
It’s no secret that voices change when a sore throat and runny nose come into play, and the researchers found that vocal rhythms of those with a cold are uniquely distinct, with differences in amplitude and frequency.
According to the study, subjects were asked to count from one to 40, before recounting what they did on the weekend. They were then asked to recite Aesop’s fable The North Wind and the Sun — a text frequently used when researching phonetics.
The new features proposed, which were placed into machine-learning algorithms, fared quite well in determining those who were feeling poorly; they were able to accurately classify cold and non-cold speech with scores of 69 per cent and 67 per cent, respectively.
