Low-cost wearable tracks heart and breathing with clinical-grade accuracy

Heart attacks kill faster than almost any other medical emergency, and the difference between survival and death often comes down to minutes. Yet the tools that doctors rely on to catch cardiac trouble early—electrocardiograms, blood troponin tests, echocardiograms—are locked inside hospitals. A team of researchers from Antonio Nariño University in Colombia and the University of São Paulo in Brazil now reports the development and validation of a wearable system that could bring continuous cardiorespiratory monitoring out of the clinic and onto the wrist and chest of the people who need it most. Writing in Medical & Biological Engineering & Computing, the team describes a two-device platform that measured heart rate with a percentage error of just 1.94 percent against clinical-grade reference equipment, meeting the accuracy threshold the researchers set before the study began.

The stakes are enormous. Cardiovascular diseases claimed an estimated 19.8 million lives in 2022, roughly 32 percent of all deaths worldwide, and 85 percent of those deaths were caused by acute myocardial infarction and stroke. Heart attacks can be treated with high survival probabilities when patients receive early attention, but symptoms are not always obvious. Chest pain is the classic warning, yet subtler signs—breathing difficulty, dizziness, tachycardia, sweating—are frequently missed or dismissed. Even when patients reach a hospital, standard diagnostic protocols have limits: electrocardiography detects significant ST-segment changes in only about 57 percent of heart attacks, and some patients never return after discharge. The researchers argue that prevention and early detection depend on affordable technology that can watch the cardiovascular system continuously, in daily life, rather than in a single hospital snapshot.

The system the team built consists of two synchronized devices. The first is a wristband built around a MAX30105 photoplethysmography sensor, a reflectance-mode optical sensor that shines red, green, and infrared light into the skin to capture the cardiac pulse wave and estimate blood oxygen saturation. The second is a chest band featuring a novel optical respiratory-detection mechanism alongside an MPU6050 inertial measurement unit that tracks body movement. Both devices are driven by ESP32-C3 microcontrollers—compact 32-bit RISC-V chips with built-in Wi-Fi and Bluetooth 5.0 Low Energy—and stream data to a custom mobile application, which in turn uploads everything to a cloud database. The total component cost of both devices comes to $82.88 at single-unit distributor prices, a figure the researchers note is one to two orders of magnitude below the clinical instruments they validated against.

The chest band’s respiratory sensor is the system’s most inventive element. Instead of impedance electrodes or strain gauges, the team designed a retractable optical assembly: a 3-millimeter red LED paired with a photoresistor, mounted so that thoracic expansion during inspiration pulls the band’s sliding structures apart and allows light to pass. At rest, the structures overlap and block the light; when the chest expands by up to 12 millimeters, light leaks through and the photoresistor registers the change. The microcontroller’s 12-bit analog-to-digital converter reads values ranging from 215 in the fully contracted position to 4095 when fully stretched—a resolution of roughly 3 micrometers. Each upward crossing of a threshold set at 500 counts corresponds to one complete inspiration, so counting these transitions over a 60-second window yields the respiratory rate directly from mechanical chest-wall displacement.

That direct mechanical approach appears to pay off in accuracy. Respiratory rate measured by the chest band showed a mean absolute percentage error of 3.47 percent after physical activity and 4.69 percent at rest, with a Pearson correlation of 0.99 against the ADInstruments physiograph reference. The post-activity root-mean-square error of 1.14 breaths per minute is lower than the 1.5 to 3.5 breaths per minute typically reported for impedance-based wearable systems, which the researchers attribute to the optical method’s resistance to motion-induced degradation. Heart rate, derived offline from the raw photoplethysmographic waveform using a zero-phase Butterworth band-pass filter and dynamic peak detection, achieved errors of 1.99 percent at rest and 1.94 percent post-activity, with Bland–Altman limits of agreement spanning roughly ±5 beats per minute—performance comparable to leading commercial wrist-worn optical sensors such as the Polar OH1.

Blood oxygen saturation told a more complicated story, and the researchers are unusually candid about it. The wristband’s SpO2 error was small—1.64 percent at rest and 2.98 percent after exercise—yet its correlation with the reference monitor was weak, just 0.35 at rest and 0.21 post-activity. The team explains this apparent contradiction as range restriction: in a cohort of healthy young adults, reference saturation varied only between 94 and 100 percent, a spread so narrow that the correlation coefficient is mathematically driven toward zero regardless of how well the devices agree. Correlation measures association, not agreement, and the Bland–Altman analysis is the appropriate yardstick here. What the correlation genuinely obscured, however, was a real degradation after exercise: five of twenty post-activity acquisitions deviated by five percentage points or more, with a maximum underestimation of nine points, likely reflecting altered peripheral perfusion and a sensor configuration optimized for pulse-wave quality rather than oximetry. The researchers explicitly state the device is not yet suitable for applications requiring absolute saturation values, and validation under controlled desaturation per the ISO 80601-2-61 standard remains outstanding.

The validation study itself involved 43 healthy volunteers aged 19 to 28, who produced 50 acquisitions: 30 at rest and 20 after running approximately 100 meters. Each participant lay supine and motionless for one minute while wearing both prototype devices alongside the ADInstruments physiograph and the EDAN iM8 vital-signs monitor. The inertial unit was tested separately across six activity conditions, from sitting motionless to running, and a movement-percentage threshold of 40 percent cleanly separated rest from activity: sitting registered 3 percent, walking 64 percent, and running 87 percent, with no false positives at rest. This movement context matters, the researchers argue, because it allows cardiovascular algorithms to distinguish genuine physiological changes from motion artifacts—potentially reducing false alarms in future risk-detection systems.

What distinguishes this platform from consumer smartwatches is architectural. Devices like the Apple Watch and Garmin smartwatches calculate derived metrics but do not expose raw photoplethysmographic signals, which are essential for developing more sophisticated algorithms. The Colombian-Brazilian system deliberately transmits complete raw pulse and respiration waveforms to the cloud, leaving filtering, feature extraction, and analysis to the researcher rather than fixing them in firmware. As processing methods evolve, the same acquisitions can be re-analyzed with new techniques—statistical, spectral, or non-linear heart-rate-variability indices, for instance—without changing the hardware. The devices can even upload autonomously over Wi-Fi without an intermediary smartphone, an advantage for older adults and patients with limited mobility who are the system’s ultimate target users.

The team is equally forthright about the study’s limits. This is a technical feasibility study in healthy young volunteers, not a clinical validation. No participant had cardiovascular disease, no elderly participants were included, and skin phototype and body mass index were not recorded—a material omission given documented disparities in optical sensing accuracy across skin tones. The optical respiratory sensor also showed more variable errors in female participants, with limits of agreement 1.7 times wider than in males, which the researchers attribute plausibly to diaphragmatic breathing patterns and mechanical interference of breast tissue with band positioning, though the difference did not reach statistical significance. All acquisitions were performed with participants supine and stationary, so performance under free-living ambulatory conditions—where motion artifact dominates—remains untested. Three acquisitions even had to be repeated after wristband battery depletion produced implausible readings, motivating future battery-state monitoring and signal-quality indices.

The pathway from here is already in motion. A data-collection study with a consultant cardiologist at Clínica Valle de Pubenza in Popayán, Colombia, has assessed 50 high-risk older adults using the system, and a preliminary risk-estimation model has been trained—though the researchers deliberately report that its performance does not yet reach clinical-application range, owing to the small labeled dataset. The long-term vision is twofold: rapid, low-burden cardiovascular screening in settings where access to cardiology is limited, and home-based longitudinal monitoring of patients already at high risk. If the platform’s accuracy holds in the populations it was designed for, an $83 wearable that streams raw cardiac and respiratory waveforms to the cloud could become the foundation for machine-learning systems that flag a heart attack before it strikes—turning the wrist into a window on the heart that no hospital waiting room can match.

Subject of Research: Development and validation of a low-cost wearable system for continuous cardiorespiratory monitoring using photoplethysmography and optical respiratory detection

Article Title: Development and validation of an integrated wearable system for continuous cardiorespiratory monitoring using photoplethysmography and optical respiratory detection

Article References: Ramírez, J. D., Toro, N., Campo, J. M., Gómez-Peña, G., Villamarín-Muñoz, J. A., & Mosquera-Sánchez, J. A. (2026). Development and validation of an integrated wearable system for continuous cardiorespiratory monitoring using photoplethysmography and optical respiratory detection. Medical & Biological Engineering & Computing. https://doi.org/10.1007/s11517-026-03677-y

Image Credits: AI Generated

DOI: 10.1007/s11517-026-03677-y

Keywords: wearable technology, photoplethysmography, cardiorespiratory monitoring, heart attack detection, SpO2, respiratory rate, heart rate variability, Bland-Altman analysis, ESP32, mobile health, cardiovascular risk, biomedical sensors