Health-related acoustic signals, such as cough and breathing sounds, are relevant for medical diagnosis and continuous health monitoring. Most existing machine learning approaches for health acoustics are trained and evaluated on specific tasks, limiting their generalizability across various healthcare applications. In this paper, we leverage a self-supervised learning framework, SimCLR with a Slowfast NFNet backbone, for contrastive learning of health acoustics. A crucial aspect of optimizing Slowfast NFNet for this application lies in identifying effective audio augmentations. We conduct an in-depth analysis of various audio augmentation strategies and demonstrate that an appropriate augmentation strategy enhances the performance of the Slowfast NFNet audio encoder across a diverse set of health acoustic tasks. Our findings reveal that when augmentations are combined, they can produce synergistic effects that exceed the benefits seen when each is applied individually.

That said, technologies--such as big data, cloud computing, supercomputers, artificial intelligence (AI), robotics, 3D printing, thermal imaging and 5G--are being used to effectively complement the traditional methods of increased hygiene, self- and forced quarantines, and enforced global travel bans. Having enforced traditional measures in place, for instance, police officers in China now wear AI-powered helmets that can automatically record the temperatures of pedestrians. The high-tech headgear has an infrared camera, and sounds an alarm if anyone in a radius of 16ft has fever. Equipped with the facial-recognition technology, it can also display the pedestrian's personal information, such as their name on a virtual screen. Officials at railway stations, airports and in other public areas in India, too, are using smart thermal scanners to record temperatures from a distance, thus helping in identifying potential coronavirus carriers.

A team of US researchers has invented a portable surveillance device powered by machine learning called'FluSense' that can detect coughing and crowd size in real time, analyse the data to directly monitor flu-like illnesses and influenza trends and predict the next pandemic in the making. The'FluSense' creators from University of Massachusetts Amherst said that the new edge-computing platform, envisioned for use in hospitals, healthcare waiting rooms and larger public spaces, may expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS. "This may allow us to predict flu trends in a much more accurate manner," said study co-author Tauhidur Rahman, assistant professor of computer and information sciences. Models like these can be lifesavers by directly informing the public health response during a flu epidemic. These data sources can help determine the timing for flu vaccine campaigns, potential travel restrictions, the allocation of medical supplies and more.

University of Massachusetts Amherst researchers have invented a portable surveillance device powered by machine learning - called FluSense - which can detect coughing and crowd size in real time, then analyze the data to directly monitor flu-like illnesses and influenza trends. The FluSense creators say the new edge-computing platform, envisioned for use in hospitals, healthcare waiting rooms and larger public spaces, may expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS. Models like these can be lifesavers by directly informing the public health response during a flu epidemic. These data sources can help determine the timing for flu vaccine campaigns, potential travel restrictions, the allocation of medical supplies and more. "This may allow us to predict flu trends in a much more accurate manner," says co-author Tauhidur Rahman, assistant professor of computer and information sciences, who advises Ph.D. student and lead author Forsad Al Hossain.

University of Massachusetts Amherst researchers have invented a portable surveillance device powered by machine learning – called FluSense – which can detect coughing and crowd size in real time, then analyze the data to directly monitor flu-like illnesses and influenza trends. The FluSense creators say the new edge-computing platform, envisioned for use in hospitals, healthcare waiting rooms and larger public spaces, may expand the arsenal of health surveillance tools used to forecast seasonal flu and other viral respiratory outbreaks, such as the COVID-19 pandemic or SARS. Models like these can be lifesavers by directly informing the public health response during a flu epidemic. These data sources can help determine the timing for flu vaccine campaigns, potential travel restrictions, the allocation of medical supplies and more. "This may allow us to predict flu trends in a much more accurate manner," says co-author Tauhidur Rahman, assistant professor of computer and information sciences, who advises Ph.D. student and lead author Forsad Al Hossain.

Researchers from the University of Massachusetts Amherst have created an AI that listens for coughing and sneezing sounds to estimate what percentage of people in a public space have a respiratory illness. The device, called FluSense, was initially tested over an eight month period in four clinic waiting rooms on the university's campus. In addition to recording'non-speech' audio samples, FluSense is also equipped with a thermal camera to scan for people with elevated temperatures. According to its co-creator, Tauhidur Rahman, the device isn't meant to single out individual cases of illness but capture trends at the population level to see if something is developing that may not yet have been picked up in medical testing. 'I thought if we could capture coughing or sneezing sounds from public spaces where a lot of people naturally congregate, we could utilize this information as a new source of data for predicting epidemiologic trends,' he told UMass Amherst's news blog.