Chandler Bauder, U.S. Naval Research Laboratory and Aly Fathy, University of Tennessee

If you wanted to check someone’s pulse from across the room, for example to remotely monitor an elderly relative, how could you do it? You might think it’s impossible, because common health-monitoring devices such as fingertip pulse oximeters and smartwatches have to be in contact with the body.

However, researchers are developing technologies that can monitor a person’s vital signs at a distance. One of those technologies is radar.

We are electrical engineers who study radar systems. We have combined advances in radar technology and artificial intelligence to reliably monitor breathing and heart rate without contacting the body.

Noncontact health monitoring has the potential to be more comfortable and easier to use than traditional methods, particularly for people looking to monitor their vital signs at home.

How radar works

Radar is commonly known for measuring the speed of cars, making weather forecasts and detecting obstacles at sea and in the air. It works by sending out electromagnetic waves that travel at the speed of light, waiting for them to bounce off objects in their path, and sensing them when they return to the device.

Radar can tell how far away things are, how fast they’re moving, and even their shape by analyzing the properties of the reflected waves.

Radar can also be used to monitor vital signs such as breathing and heart rate. Each breath or heartbeat causes your chest to move ever so slightly – movement that’s hard for people to see or feel. However, today’s radars are sensitive enough to detect these tiny movements, even from across a room.

Advantages of radar

There are other technologies that can be used to measure health remotely. Camera-based techniques can use infrared light to monitor changes in the surface of the skin in the same manner as pulse oximeters, revealing information about your heart’s activity. Computer vision systems can also monitor breathing and other activities, such as sleep, and they can detect when someone falls.

However, cameras often fail in cases where the body is obstructed by blankets or clothes, or when lighting is inadequate. There are also concerns that different skin tones reflect infrared light differently, causing inaccurate readings for people with darker skin. Additionally, depending on high-resolution cameras for long-term health monitoring brings up serious concerns about patient privacy.

Radar, on the other hand, solves many of these problems. The wavelengths of the transmitted waves are much longer than those of visible or infrared light, allowing the waves to pass through blankets, clothing and even walls. The measurements aren’t affected by lighting or skin tone, making them more reliable in different conditions.

Radar imagery is also extremely low resolution – think old Game Boy graphics versus a modern 4K TV – so it doesn’t capture enough detail to be used to identify someone, but it can still monitor important activities. While it does project energy, the amount does not pose a health hazard. The health-monitoring radars operate at frequencies and power levels similar to the phone in your pocket.

Radar + AI

Radar is powerful, but it has a big challenge: It picks up everything that moves. Since it can detect tiny chest movements from the heart beating, it also picks up larger movements from the head, limbs or other people nearby. This makes it difficult for traditional processing techniques to extract vital signs clearly.

To address this problem we created a kind of “brain” to make the radar smarter. This brain, which we named mm-MuRe, is a neural network – a type of artificial intelligence – that learns directly from raw radar signals and estimates chest movements. This approach is called end-to-end learning. It means that, unlike other radar plus AI techniques, the network figures out on its own how to ignore the noise and focus only on the important signals.

We found that this AI enhancement not only gives more accurate results, it also works faster than traditional methods. It handles multiple people at once, for example an elderly couple, and adapts to new situations, even those it didn’t see during training – such as when people are sitting at different heights, riding in a car or standing close together.

Implications for health care

Reliable remote health monitoring using radar and AI could be a major boon for health care. With no need to touch the patient’s skin, risks of rashes, contamination and discomfort could be greatly reduced. It’s especially helpful in long-term care, where reducing wires and devices can make life significantly easier for patients and caregivers.

Imagine a nursing home where radar quietly watches over residents, alerting caregivers immediately if someone has breathing trouble, falls or needs help. It can be implemented as a home system that checks your breathing while you sleep – no wearables required. Doctors could even use radar to remotely monitor patients recovering from surgery or illness.

This technology is moving quickly toward real-world use. In the future, checking your health could be as simple as walking into a room, with invisible waves and smart AI working silently to take your vital signs.

Chandler Bauder, Electronics Engineer, U.S. Naval Research Laboratory and Aly Fathy, Professor of Electrical Engineering, University of Tennessee

This article is republished from The Conversation under a Creative Commons license. Read the original article.

David Oygenblik, Georgia Institute of Technology and Brendan Saltaformaggio, Georgia Institute of Technology

From drones delivering medical supplies to digital assistants performing everyday tasks, AI-powered systems are becoming increasingly embedded in everyday life. The creators of these innovations promise transformative benefits. For some people, mainstream applications such as ChatGPT and Claude can seem like magic. But these systems are not magical, nor are they foolproof – they can and do regularly fail to work as intended.

AI systems can malfunction due to technical design flaws or biased training data. They can also suffer from vulnerabilities in their code, which can be exploited by malicious hackers. Isolating the cause of an AI failure is imperative for fixing the system.

But AI systems are typically opaque, even to their creators. The challenge is how to investigate AI systems after they fail or fall victim to attack. There are techniques for inspecting AI systems, but they require access to the AI system’s internal data. This access is not guaranteed, especially to forensic investigators called in to determine the cause of a proprietary AI system failure, making investigation impossible.

We are computer scientists who study digital forensics. Our team at the Georgia Institute of Technology has built a system, AI Psychiatry, or AIP, that can recreate the scenario in which an AI failed in order to determine what went wrong. The system addresses the challenges of AI forensics by recovering and “reanimating” a suspect AI model so it can be systematically tested.

Uncertainty of AI

Imagine a self-driving car veers off the road for no easily discernible reason and then crashes. Logs and sensor data might suggest that a faulty camera caused the AI to misinterpret a road sign as a command to swerve. After a mission-critical failure such as an autonomous vehicle crash, investigators need to determine exactly what caused the error.

Was the crash triggered by a malicious attack on the AI? In this hypothetical case, the camera’s faultiness could be the result of a security vulnerability or bug in its software that was exploited by a hacker. If investigators find such a vulnerability, they have to determine whether that caused the crash. But making that determination is no small feat.

Although there are forensic methods for recovering some evidence from failures of drones, autonomous vehicles and other so-called cyber-physical systems, none can capture the clues required to fully investigate the AI in that system. Advanced AIs can even update their decision-making – and consequently the clues – continuously, making it impossible to investigate the most up-to-date models with existing methods.

Pathology for AI

AI Psychiatry applies a series of forensic algorithms to isolate the data behind the AI system’s decision-making. These pieces are then reassembled into a functional model that performs identically to the original model. Investigators can “reanimate” the AI in a controlled environment and test it with malicious inputs to see whether it exhibits harmful or hidden behaviors.

AI Psychiatry takes in as input a memory image, a snapshot of the bits and bytes loaded when the AI was operational. The memory image at the time of the crash in the autonomous vehicle scenario holds crucial clues about the internal state and decision-making processes of the AI controlling the vehicle. With AI Psychiatry, investigators can now lift the exact AI model from memory, dissect its bits and bytes, and load the model into a secure environment for testing.

Our team tested AI Psychiatry on 30 AI models, 24 of which were intentionally “backdoored” to produce incorrect outcomes under specific triggers. The system was successfully able to recover, rehost and test every model, including models commonly used in real-world scenarios such as street sign recognition in autonomous vehicles.

Thus far, our tests suggest that AI Psychiatry can effectively solve the digital mystery behind a failure such as an autonomous car crash that previously would have left more questions than answers. And if it does not find a vulnerability in the car’s AI system, AI Psychiatry allows investigators to rule out the AI and look for other causes such as a faulty camera.

Not just for autonomous vehicles

AI Psychiatry’s main algorithm is generic: It focuses on the universal components that all AI models must have to make decisions. This makes our approach readily extendable to any AI models that use popular AI development frameworks. Anyone working to investigate a possible AI failure can use our system to assess a model without prior knowledge of its exact architecture.

Whether the AI is a bot that makes product recommendations or a system that guides autonomous drone fleets, AI Psychiatry can recover and rehost the AI for analysis. AI Psychiatry is entirely open source for any investigator to use.

AI Psychiatry can also serve as a valuable tool for conducting audits on AI systems before problems arise. With government agencies from law enforcement to child protective services integrating AI systems into their workflows, AI audits are becoming an increasingly common oversight requirement at the state level. With a tool like AI Psychiatry in hand, auditors can apply a consistent forensic methodology across diverse AI platforms and deployments.

In the long run, this will pay meaningful dividends both for the creators of AI systems and everyone affected by the tasks they perform.

David Oygenblik, Ph.D. Student in Electrical and Computer Engineering, Georgia Institute of Technology and Brendan Saltaformaggio, Associate Professor of Cybersecurity and Privacy, and Electrical and Computer Engineering, Georgia Institute of Technology

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Annette Regan, University of California, Los Angeles

Whooping cough, a bacterial infection that can be especially dangerous for babies and young children, is on the rise. Already in 2025 the U.S. has recorded 8,485 cases. That’s compared with 4,266 cases during the same period in 2024.

Like measles, which is also spreading at unprecedented levels, whooping cough, more formally known as pertussis, can be prevented by a safe
and effective vaccine. But with anti-vaccine sentiment increasing and cuts to immunization services, vaccination rates for whooping cough over the past two years have declined in children.

The Conversation asked epidemiologist Annette Regan to explain why pertussis has become so prevalent and how families can protect themselves from the disease.

What is pertussis and why is it dangerous?

Pertussis is a vaccine-preventable disease caused by the bacterium Bordetella pertussis. Researchers in France first identified the B. pertussis bacterium in 1906. The first recorded epidemic of pertussis is thought to have occurred in Paris in 1578.

Infection can cause an acute respiratory illness characterized by severe and spasmodic coughing spells. The classic symptom of pertussis is a “whoop” sound caused by someone trying to breath during a bad cough. Severe complications of pertussis include slowed or stopped breathing, pneumonia and seizures. The disease is most severe in young babies, although severe cases and deaths can also occur in older children and adults.

Some doctors call pertussis “the 100-day cough” because symptoms can linger for weeks or even months.

The World Health Organization estimates that 24.1 million pertussis cases and 160,700 deaths occur worldwide in children under 5 each year. Pertussis is highly contagious. Upon exposure, 80% of people who have not been previously exposed to the bacterium or vaccinated against the disease will develop an infection.

Fortunately, the disease is largely preventable with a safe and effective vaccine, which was first licensed in the U.S. in 1914.

How do cases last year and this year compare with past years?

During the COVID-19 pandemic between 2020 and 2022, pertussis cases were lower than usual. This may have been a result of limited social contact due to social distancing, masking, school closures and lockdown measures, which reduced the spread of disease overall.

In the past two years, however, pertussis cases have surpassed figures from before the pandemic. In 2024, local and state public health agencies reported 35,435 pertussis cases to the Centers for Disease Control and Prevention – a rate five times higher than the 7,063 cases reported in 2023 and nearly double the 18,617 cases reported in 2019 prior to the pandemic.

Between October 2024 and April 2025, at least four people in the U.S. have died of pertussis: two infants, one school-age child and one adult.

Why are pertussis cases rising?

Although vaccines have resulted in a dramatic decline in pertussis infections in the U.S., incidence of the disease has been rising since the 1990s, except for a brief dip during the COVID-19 pandemic.

Before the start of routine childhood vaccination for pertussis in 1947, its rates hovered between 100,000 and 200,000 cases per year. With vaccines, rates plunged under 50,000 annually by the late 1950s and under 10,000 per year in the late 1960s. They reached a low of 1,010 cases in 1976.

Starting in the 1980s and 1990s, however, the U.S. and several other countries have been seeing a steady resurgence of pertussis cases, which have exceeded 10,000 cases in the U.S. every year from 2003 to 2019. They dropped again during the pandemic until last year’s resurgence.

There is no single explanation for why cases have been rising recently, but several factors probably contribute. First, pertussis naturally occurs in cyclic epidemics, peaking every two to five years. It is possible that the U.S. is headed into one of these peaks after a period of low activity between 2020 and 2022. However, some scientists have noted that the increase in cases is larger than what would be expected during a usual peak.

Some scientists have noted that this apparent resurgence correlates with a change in the type of vaccine used in children. Until the 1990s, the pertussis vaccine contained whole, killed B. pertussis bacteria cells. Whole-cell vaccine can stimulate a long-lasting immune response, but it is also more likely to cause fever and other vaccine reactions in children.

In the 1990s, national vaccine programs began to transition to a vaccine that contains purified components of the bacterial cell but not the whole cell. Some scientists now believe that although this partial-cell vaccine is less likely to cause high fevers in children, it provides protection for a shorter time. Immunity after whole-cell vaccination is thought to last 10-12 years compared with three to five years after the partial-cell vaccine. This means people may become susceptible to infection more quickly after vaccination.

Vaccination rates are also not as high as they should be and have started falling in children since 2020. In the U.S., the percent of kindergartners who are up to date with recommended pertussis vaccines has declined from 95% during the 2019-20 school year to 92% in the 2023-24 school year. Even fewer adolescents receive a booster dose.

How can people protect themselves and their families?

Routine vaccination for children starting in infancy followed by booster doses in adolescents and adults can help keep immunity high.

Public health experts recommend that children receive five doses of the pertussis vaccine. According to the recommendations, they should receive the first three doses at 2, 4 and 6 months of age, then two additional doses at 15 months and 4 years of age, with the aim of providing protection through early adolescence.

Infants younger than 6 weeks are not old enough to get a pertussis vaccine but are at the greatest risk of severe illness from pertussis. Vaccination during pregnancy can offer protection from birth due to antibodies that pass from the mother to the developing fetus. Many countries, including the U.S., now recommend that women receive one dose of pertussis vaccine between the 27th and 36th week of every pregnancy to protect their babies.

To maintain protection against pertussis after childhood, a booster dose of pertussis vaccine is recommended for adolescents at 11 to 12 years of age. The CDC recommends that all adults receive at least one booster dose.

Because immunity declines over time, people who are in contact with infants and other high-risk groups, such as caregivers, parents and grandparents, may benefit from additional booster doses. When feasible, the CDC also recommends a booster dose for adults 65 years and older.

Vaccine safety studies over the past 80 years have proven the pertussis vaccine to be safe. Around 20% to 40% of vaccinated infants experience local reactions, such as pain, redness and swelling at the vaccination site, and 3% to 5% of vaccinated infants experience a low-grade fever. More severe reactions are much less common and occur in fewer than 1% of vaccinated infants.

The vaccine is also highly effective: For the first year after receiving all five doses of the pertussis vaccine, 98% of children are protected from pertussis. Five years after the fifth dose, 65% of vaccinated children remain protected.

Booster vaccination during adolescence protects 74% of teens against pertussis, and booster vaccination during pregnancy protects 91% to 94% of immunized babies against hospitalization due to pertussis.

Families can talk to their regular health care providers about whether a pertussis vaccine is needed for their child, themselves or other family members.

Annette Regan, Adjunct Associate Professor of Epidemiology, University of California, Los Angeles

This article is republished from The Conversation under a Creative Commons license. Read the original article.