In 2024, 79,384 people in the United States died from drug overdoses. That number actually represents progress. It's down nearly 27% from the roughly 110,000 deaths recorded in 2023, according to the CDC's National Center for Health Statistics. Synthetic opioids, primarily illicit fentanyl, drove the majority of those deaths. The mechanism is almost always the same: opioids suppress breathing, oxygen levels crash, and without intervention the person dies. The whole thing can happen in minutes.
The obvious question researchers have been asking for years: can technology detect when someone's breathing is failing and trigger a response before it's too late? Several groups are working on it from different angles, including contactless approaches that use cameras, sonar, and motion sensors to track respiratory rate without touching the person.
"The window to prevent irreversible brain damage or death once oxygen levels become low is perilously short, less than five minutes, with hypoxic injury beginning within minutes after an overdose." — Aung et al., JMIR (2025)
Why respiratory monitoring matters more than anything else
Opioid overdose kills through one pathway: respiratory depression. The drugs bind to mu-receptors in the brainstem and suppress the urge to breathe. Breathing slows, becomes shallow, and eventually stops. Everything else that follows, the cyanosis, the cardiac arrest, the brain damage, is downstream of that respiratory failure.
This makes the problem, in theory, detectable. If you can continuously monitor someone's breathing rate and depth, you can catch the dangerous slowdown before it becomes fatal. The Anesthesia Patient Safety Foundation has been pushing for better respiratory monitoring in hospital settings for years. Their 2024 newsletter reported that opioid-induced respiratory depression occurs in up to 46% of patients receiving opioids on general care hospital floors. Most of those episodes go undetected because nurses check vital signs intermittently, every one to four hours, rather than continuously.
If hospitals struggle to monitor respiratory depression in patients who are literally in their care, the challenge is far harder for people using opioids outside medical settings, often alone, often in circumstances where calling 911 isn't something they'll do preemptively.
Technologies under development for overdose detection
| Technology | Detection method | Contact required | Response mechanism | Validation stage | Key limitation |
|---|---|---|---|---|---|
| Second Chance (UW) | Smartphone sonar monitors breathing | None, phone nearby | Alerts emergency contacts, calls 911 | Tested at supervised injection site (Vancouver) | Requires phone setup before each use |
| DOVE wearable sensor | Shoulder-mounted PPG + motion | Yes, worn on body | Classifies hypoxemia via machine learning | IJCAI 2024, OxyCaps capsule network | Must be worn consistently |
| Closed-loop naloxone device | Wearable respiratory sensor | Yes, injector attached | Automatic naloxone injection | Proof of concept (Chan et al., 2021) | Regulatory and liability barriers |
| Camera-based rPPG | Video analysis of facial blood flow | None | Software alerts, potential integration with response systems | Research stage | Requires face visibility and adequate lighting |
| Continuous pulse oximetry | Finger or ear clip SpO2 | Yes | Audible alarm at low SpO2 threshold | Clinical standard for hospitals | Not practical for unsupervised community use |
| Capnography (ETCO2) | Exhaled CO2 measurement | Yes, nasal cannula | Clinical alarm systems | Gold standard for ventilation | Equipment cost, not portable |
Each of these sits at a different point on the spectrum between accuracy and real-world usability. The hospital-grade options work well but are impractical outside clinical settings. The contactless approaches are more deployable but less validated.
The smartphone sonar approach
The most widely reported overdose detection system is Second Chance, developed by Rajalakshmi Nandakumar and colleagues at the University of Washington. Published in Science Translational Medicine in 2019, the app converts a smartphone's speaker and microphone into an active sonar system. It emits inaudible sound pulses and measures how they bounce back from the person's chest, tracking breathing rate and movement from up to three feet away.
The team tested it with real drug users at Insite, a supervised injection facility in Vancouver. Participants prepared and used drugs as they normally would while the app monitored in the background. The system identified respiratory depression symptoms with roughly 90% accuracy, including apnea events (breathing cessation) and respiratory rates below the danger threshold.
What makes Second Chance interesting from a harm reduction perspective is that it doesn't require the person to wear anything. They set up the phone, use drugs, and the phone watches their breathing. If breathing stops or slows dangerously, the app can alert someone. The practical limitation: the person has to set it up each time, which assumes a level of preparation and routine that doesn't match every use scenario.
Camera-based monitoring and where rPPG fits
Camera-based vital signs measurement offers something the sonar approach doesn't: the ability to monitor without the user doing anything at all. A camera in a room can track respiratory rate by detecting subtle chest movements through video motion analysis and can estimate heart rate through remote photoplethysmography, which picks up blood volume changes in facial skin.
In a substance use context, this matters for several settings:
Supervised consumption facilities
These sites already exist in several countries and are expanding in parts of North America. Staff visually monitor clients after drug use, watching for signs of overdose. Adding camera-based respiratory monitoring would give staff an objective, continuous measurement rather than relying on periodic visual checks. A system that flags when someone's breathing rate drops below 8 breaths per minute would catch changes that a busy staff member might miss, particularly in a room with multiple clients.
Hospital general care floors
The APSF has repeatedly documented the problem of unmonitored opioid-induced respiratory depression on general wards. Continuous pulse oximetry helps but has high false alarm rates, leading to alarm fatigue. Khanna et al. published research showing that continuous monitoring with capnography and pulse oximetry detected respiratory depression episodes that intermittent nursing checks missed entirely. Camera-based monitoring could provide continuous respiratory rate data without the wires, adhesives, or finger clips that patients remove when they get up to use the bathroom.
Post-anesthesia recovery
Patients recovering from procedures involving opioid analgesia need respiratory monitoring during their recovery period. Camera-based systems could supplement existing monitoring, particularly as patients transition from high-acuity recovery areas to general wards where monitoring intensity drops.
The DOVE project and machine learning classification
Lingamoorthy et al. presented the Drug Overdose Vital-Signs Evaluator (DOVE) at the International Joint Conference on Artificial Intelligence in 2024. Their system uses a shoulder-mounted device combining PPG sensors and motion detection, then applies a novel capsule network architecture called OxyCaps to classify hypoxemia, the dangerous drop in blood oxygen that precedes overdose death.
The DOVE team trained their model on sleep apnea data as a proxy for opioid-induced respiratory depression, since both conditions involve repeated episodes of breathing cessation followed by oxygen desaturation. Their OxyCaps architecture outperformed standard deep learning approaches on this classification task, correctly identifying hypoxemia episodes that simpler algorithms missed.
This matters because raw vital sign data isn't enough. Breathing rate alone doesn't tell you whether someone is in danger. A respiratory rate of 10 might be fine for one person and an emergency for another. Machine learning models that combine respiratory rate, heart rate variability, oxygen saturation trends, and movement patterns have a better chance of distinguishing "sleeping normally" from "respiratory depression progressing toward arrest."
What the overdose alert review found
Tran et al. published a state-of-the-art review in the Journal of Medical Internet Research (2023) cataloging overdose alert and response technologies. Their review covered wearable sensors, smartphone apps, environmental sensors, and closed-loop naloxone delivery systems. The findings paint an honest picture of where things stand.
Most devices remain at the prototype or early feasibility stage. The closed-loop systems, where a wearable detects respiratory depression and automatically injects naloxone, face regulatory barriers that go beyond technical performance. Getting FDA clearance for a device that autonomously administers a rescue medication requires a burden of proof that no current system has met.
The review also found a consistent gap: most systems were tested in controlled lab environments or simulated overdose scenarios, not with actual opioid users in real-world settings. The University of Washington's work at the Vancouver injection site is one of the few exceptions. Real-world drug use involves unpredictable positions, multiple substances, varying environments, and people who may not set up monitoring equipment reliably.
The skin tone and equity problem
Any camera-based monitoring system deployed in substance use settings has to work across diverse populations. Published rPPG research has documented reduced accuracy on darker skin tones. Nowara et al. at Rice University demonstrated in 2020 that many standard rPPG algorithms showed bias in pulse rate extraction across different skin pigmentation levels.
The opioid crisis disproportionately affects communities of color in specific ways that vary by substance and geography. A monitoring system that works well on lighter skin but fails on darker skin would compound existing health disparities rather than reducing them. Any serious deployment of camera-based overdose detection technology has to address this bias before it reaches clinical use.
What would actually save lives
The technology is interesting but the harder problems are structural. People dying from opioid overdoses are often alone. They may be unhoused. They may actively avoid medical settings. A monitoring system, no matter how accurate, only works if it's present during the high-risk period.
Supervised consumption sites solve the "someone is watching" problem directly. Camera-based monitoring in those settings is a natural fit because the infrastructure already exists and clients are already being observed. The technology adds objectivity and consistency to what staff already do.
For people using drugs alone at home, the gap between having the technology and using the technology remains wide. Second Chance requires deliberate setup. Wearable devices require deliberate wearing. Camera systems require installation and present obvious privacy concerns. The people most at risk of overdose death are often the least likely to have consistent access to any of these tools.
Circadify has developed camera-based vital sign measurement technology capable of tracking respiratory rate and heart rate from video. These capabilities have potential applications in overdose risk settings, from supervised facilities to hospital wards where patients receive opioid medications. The underlying technology for detecting respiratory depression contactlessly exists. Getting it into the rooms where people actually die is the remaining challenge.
Frequently asked questions
Can a camera detect an opioid overdose?
Camera-based systems can monitor respiratory rate and heart rate contactlessly, which are the two vital signs most affected during opioid-induced respiratory depression. While no camera system is FDA-cleared specifically for overdose detection, research teams are developing video-based respiratory monitoring that could flag dangerous breathing slowdowns in real time.
What technology is being developed to detect opioid overdoses?
Several approaches are under development, including smartphone sonar apps like Second Chance from the University of Washington, shoulder-mounted wearable sensors, closed-loop naloxone delivery devices, and camera-based respiratory monitoring using rPPG. Each has different tradeoffs in accuracy, wearability, and real-world practicality.
How does opioid-induced respiratory depression work?
Opioids bind to mu-receptors in the brainstem, suppressing the respiratory drive. Breathing slows, becomes shallow, and in severe cases stops entirely. Hypoxia follows within minutes. The window to administer naloxone and prevent brain damage or death is extremely short, typically under five minutes once oxygen levels drop critically.
Could contactless monitoring work in harm reduction facilities?
Supervised consumption sites already monitor clients visually after drug use. Camera-based vital signs could add objective respiratory rate tracking to supplement visual observation, potentially catching subtle breathing changes earlier than a human observer. Research in this area is still early but the clinical rationale is strong.
Related articles
- Contactless respiratory rate detection — How camera-based respiratory monitoring works and current validation data.
- Camera-based vital signs in emergency triage — How rPPG is being tested in emergency department settings for rapid patient assessment.
- What is rPPG technology? — A complete overview of remote photoplethysmography and how it extracts vital signs from video.