Today, many wrist wearables can perform a simplified version of the same test. They record a single-lead ECG in about 30 seconds using just two small electrodes built into the device.

Despite this convenience, a smartwatch isn't replacing the large ECG machines used in hospitals. Instead, it solves a different problem. A clinical ECG is designed for diagnosing a wide range of heart conditions, while a wrist wearable focuses on capturing enough electrical information to monitor heart rhythm and identify certain abnormalities, such as atrial fibrillation (AFib).

Multiple studies have shown that modern single-lead smartwatch ECGs can detect AFib with high accuracy under appropriate conditions, but they're intended to complement, not replace, a standard 12-lead ECG.

That raises an interesting question: how can two tiny metal contacts on a smartwatch detect electrical signals generated deep inside your heart?

The answer combines biology, electronics, embedded systems, and digital signal processing. Let's break it down.

Table of Contents

• Your Heart Is an Electrical System Before It's a Mechanical One

• How a Wrist Wearable Captures That Electrical Signal

• From Analog Waveform to a Digital ECG Waveform

• Why Wrist Wearables Use a Single-Lead ECG

• Clinical ECG vs Wrist ECG: What’s the difference

• Where Smartwatch ECG Performs Surprisingly Well

• Why Consumer ECG Still Has Important Limitations

• The Engineering Challenges Behind Wrist ECG

• The Future of Wearable ECG

• Wrap Up

Your Heart Is an Electrical System Before It's a Mechanical One

Before your heart can pump a single drop of blood, it has to fire an electrical impulse. You can think of the heart as a synchronized electrical circuit where every mechanical beat starts with a precisely timed waveform pulse.

This sequence begins in the sinoatrial (SA) node which is the heart's natural pacemaker, located in the upper right chamber. The SA node fires a tiny waveform spike that spreads across the atria, causing them to contract and push blood down.

Next, the signal hits the atrioventricular (AV) node, which acts like an intentional delay gate to let the ventricles fill completely.

Finally, the pulse surges through specialized conductive pathways into the ventricles, triggering a powerful contraction that circulates blood through your body. Because human tissues and fluids are electrically conductive, these microscopic waveform shifts ripple outward until they reach the surface of your skin.

How a Wrist Wearable Captures That Electrical Signal

Capturing a biological signal from two isolated points on the skin surface is a tricky hardware problem. A typical smartwatch solves this using two main metal electrodes: one integrated into the back crystal touching the wrist and another built into the side crown or outer frame.

When you touch the side crown with a finger from your opposite hand, your body completes a continuous electrical path. This loop spans across your arms, shoulders, and chest cavity. The watch measures the electrical potential difference between these two distinct contact points.

But this biological signal is vanishingly small, typically between 0.5 and 2 millivolts. Because it travels across long paths of skin and muscle, it arrives heavily contaminated by ambient noise, static electricity, and electromagnetic interference from nearby appliances.

To manage this, the wearable routes the raw micro-wave into an Analog Front End (AFE). The AFE uses high-impedance instrumentation amplifiers and differential sensing to boost the heart signal by orders of magnitude while stripping away common-mode noise before the data ever reaches a digital processor.

From Analog Waveform to a Digital ECG Waveform

Once the Analog Front End cleans and amplifies the microscopic waveform, software algorithms step in to transform raw analog input into the sharp line graph you see on your screen.

First, an Analog-to-Digital Converter (ADC) samples the analog waveform hundreds of times per second, converting continuous waves into a high-resolution stream of digital data.

Next, Digital Signal Processing (DSP) algorithms strip out environmental noise. A high-pass filter eliminates low-frequency baseline wander caused by chest breathing. A low-pass filter cuts high-frequency noise from micro-tremors in your hand muscles.

Finally, a dedicated notch filter cancels out the persistent 50 Hz or 60 Hz hum emitted by power grids and wall outlets.

After filtering, specialized software algorithms analyze the clean waveform. Using precise peak-detection routines, the firmware identifies the QRS complex, specifically the tall R-peak that marks ventricular contraction. By calculating the exact time intervals between successive R-peaks, the device determines instantaneous heart rate and flags irregular beats.

The smartwatch isn't just recording waveforms, it's continuously scrubbing and interpreting data before drawing the final waveform.

Why Wrist Wearables Use a Single-Lead ECG

In clinical cardiology, an ECG measurement is defined by a "lead," which represents a specific spatial view of the heart's electrical vector between two reference points.

Because a smartwatch only features two distinct contact locations, it can only measure a single vector across the upper body. In standard 12-lead nomenclature, the path going from the right arm to the left arm is classified as Lead I.

A Lead I configuration tracks the primary horizontal electrical axis of the heart. This single perspective provides clear timing intervals between heartbeats, making it remarkably effective for calculating heart rate and evaluating basic rhythm regularity.

But because Lead I only views the heart along a single plane, it can't detect localized structural issues occurring on the inferior or posterior walls of the heart muscle.

Clinical ECG vs Wrist ECG: What’s the difference

While both technologies measure bioelectric waveform, their implementation targets fundamentally different monitoring requirements:

A hospital ECG views the heart from 12 unique electrical angles simultaneously, mapping vectors across 3 dimensions. A smartwatch, by contrast, observes only one horizontal plane. This core structural difference explains why cardiologists rely on 12-lead systems for full clinical diagnoses.

Where Smartwatch ECG Performs Surprisingly Well

Despite being limited to a single lead, smartwatch ECGs excel in scenarios where traditional clinical equipment struggles: capturing sporadic, intermittent events in everyday life.

Conditions like AFib often occur unpredictably in short bursts. A patient might experience palpitations at home, yet present a perfectly normal rhythm by the time they reach a clinic for a formal standard test.

A comprehensive meta-analysis on smartwatch ECG diagnostic accuracy showed that consumer smartwatch algorithms achieve high sensitivity and specificity for detecting AFib when evaluated against clinical reference standards.

Capturing an irregular rhythm moment in real-time on a wrist device provides actionable, timestamped data that clinicians can later review to guide further diagnostic testing.

Why Consumer ECG Still Has Important Limitations

Understanding wearable ECG engineering also means acknowledging the hardware limitations inherent to consumer form factors.

Firstly, dry metal electrodes lack the conductive gel used in clinical settings, creating higher skin-electrode impedance. Factors like dry skin, excessive sweat, loose strap fit, or wrist tattoos can degrade the signal-to-noise ratio.

Secondly, micro-movements introduce motion artifacts, waveform spikes created by flexing muscles that can mimic or obscure true cardiac signals.

Most importantly, because a single lead can't evaluate vector changes across the entire myocardium, a wearable can't detect acute heart attacks, silent ischemia, or complex ventricular arrhythmias. A clean smartwatch reading simply confirms a stable rhythm along Lead I. It's never a complete clean bill of health.

The Engineering Challenges Behind Wrist ECG

Building a functional ECG into a watch involves navigating severe hardware and software trade-offs. Engineers must balance signal sensitivity against power consumption, battery constraints, and physical footprint.

The primary engineering challenges include:

• Ultra-low-power AFE design: The sensing circuitry must remain accurate while drawing minimal microamps from a tiny battery.

• Real-time adaptive filtering: Embedded processors must run digital bandpass and notch filters on incoming data without causing system latency.

• Motion artifact cancellation: Algorithms must differentiate between true cardiac electrical waves and electromyographic signals generated by flexing arm muscles.

• On-device machine learning: Lightweight classification models must run locally on microcontroller hardware to classify rhythms securely without relying entirely on cloud processing.

Ultimately, the hardest engineering problem isn't detecting bioelectricity, it's separating a faint cardiac signal from the noisy environment of a moving human wrist.

The Future of Wearable ECG

As embedded systems continue to evolve, wearable cardiac monitoring is moving toward multi-sensor fusion. Future devices are pairing single-lead ECG data with optical Photoplethysmography (PPG), wrist temperature sensors, and continuous accelerometers.

By combining optical blood volume shifts (PPG) with electrical timing (ECG), devices can calculate Pulse Transit Time (PTT) to estimate blood pressure non-invasively.

Simultaneously, edge-AI chips are becoming efficient enough to perform continuous, low-power background rhythm monitoring, notifying users the moment an anomaly is detected rather than relying solely on manual 30-second tests.

Wrap Up

Wearable ECG is a good example of how solving a real-world problem often requires expertise from multiple engineering disciplines. A feature that appears as a simple 30-second test on a smartwatch depends on analog circuit design, embedded firmware, digital signal processing, and intelligent algorithms working together to produce reliable results.

As wearable devices continue to become more capable, understanding the engineering behind features like ECG becomes just as important as understanding what they measure. It shows that building modern health technology isn't just about adding new sensors, it's about designing systems that can turn tiny, noisy biological signals into information that people can trust.

But a surprising percentage of people building around this metric don’t really know what it means or why it even matters for the apps they're building. So consider this more as a grounding in what HRV actually means. It should be useful whether you're designing the feature, writing the copy, or just trying to make sense of your own ring data.

Table of Contents

• What HRV Actually Measures

• Why the Context Around HRV Data Matters More Than the Number

• Where HRV Data Gets Misused

  • Treating HRV as Real-time Data

  • Ignoring Measurement Method Differences

  • Overcomplicating the Output

  • Skipping Data Quality Checks

• Principles for Working with HRV

• A Note on Privacy

• Wrap Up

What HRV Actually Measures

Heart Rate Variability (HRV) isn't heart rate. Instead, it’s the variability of time intervals between subsequent heartbeats. In case your heart works at 60 bpm, it doesn’t imply that each heartbeat happens exactly once per second. The intervals may vary from 900 milliseconds to 1100 milliseconds, and that’s what HRV actually is.

Increased HRV usually indicates proper functioning of the autonomic nervous system and the ability to change states efficiently, switching from stress to relaxation. Decreased HRV is often an indicator of being exhausted, sick, or under increased physiological stress.

This is the measure which top athletes obsessively monitor. Also, it can be helpful for those who suffer from chronic conditions, insomnia, and burnout.

Here’s the part that trips people up: HRV isn’t one number. It’s a family of metrics, each calculated differently.

1. RMSSD stands for Root Mean Square of Successive Differences and is the most common metric that you'll come across. RMSSD indicates short-term variation and forms the basis for the majority of HRV scores on wearable consumer devices.

2. SDNN stands for Standard Deviation of NN intervals and indicates general variability, being used primarily in clinical research settings.

3. The LF/HF ratio refers to the HRV frequency domains, dividing the HRV into two parts of different frequencies.

All of the major HRV providers, such as Apple Health, Garmin, Fitbit, and Oura, provide HRV scores, yet they don’t always agree on which metric they’re surfacing. And they don’t always tell you.

Why the Context Around HRV Data Matters More Than the Number

HRV, in its raw form, is almost entirely meaningless. A reading of 45ms could either be an indication of peak physical health in one person or a warning sign of poor physical well-being in another. Factors such as age, physical fitness, timing of measurements, and even sleeping position influence the normal HRV value.

Understanding that this is perhaps the biggest factor in interpreting HRV is the first step when developing features around it.

Commercial wearables have managed to address this issue by establishing a personal baseline based on readings taken in 30-90 days of wearing the device and presenting deviation from this baseline rather than absolute values.

The lesson here is simple: if your product has anything to do with health (recovery apps, coaching platforms, and so on) then you must follow the same logic, otherwise your users will get confused.

Showing them a raw reading of 38ms won’t make much sense anyway. The better pattern: track trends over time, flag deviations, and let the data explain itself relative to the user’s own history. Not population averages, not clinical reference ranges, but their own.

Where HRV Data Gets Misused

Treating HRV as Real-time Data

HRV isn't intended for real-time measurements. The most reliable HRV values can be obtained by collecting overnight data, as this allows minimizing external factors’ impact on the result.

This is why companies like Oura, Apple, and WHOOP rely precisely on nighttime HRV values. If a product is measuring HRV in the middle of workouts and business meetings, then you're most probably dealing with noise rather than insights.

Ignoring Measurement Method Differences

ECG-based HRV, which can be measured by a chest strap or a professional-grade ECG monitor, is much more precise compared to PPG-based HRV measured by optical sensors incorporated into consumer wearables.

During nighttime, the accuracy difference between these types of data is minimal but grows when a person becomes more active. If your app needs precision – say, you’re building for clinical or research contexts – know your source.

Overcomplicating the Output

Users aren’t cardiologists. Having RMSSD, SDNN, and LF/HF appear in your dashboard may seem complete, but really, it just makes things confusing and causes analysis paralysis.

The most successful consumer HRV applications boil everything down to a readiness or recovery metric. Having more than two HRV metrics on one screen should make you think twice.

Skipping Data Quality Checks

Wearable data is inherently messy due to motion artifacts, loose placement, uneven wear, and so on. Before including a reading in a calculation, do your homework and see whether data quality was flagged by the wearable. Apple’s HealthKit provides metadata for this purpose, as does the Oura API.

Principles for Working with HRV

There are a few patterns that hold up across most use cases:

1. Build for the baseline first: Put a data window threshold on any feature using HRV metrics as its basis. Fourteen days may be a good minimum, but thirty is preferable. No trends can be shown without sufficient historical data.

2. Normalize before comparing: When comparing HRV across users (let’s say for a team wellness dashboard), it makes much more sense to use a z-score normalization with respect to a baseline of each user than just compare absolute numbers. A reading of 55ms for one user and 40ms for another might actually signify the same physiological state, once you account for each person's baseline.

3. Design for trends, not single data points: One bad HRV day is almost certainly random. But three or four consecutive days of bad readings coming from an athlete used to having significantly higher readings is definitely something to pay attention to. Again, sparklines and rolling averages for seven days will help more than a single point comparison.

4. Be honest about what HRV can’t tell you: It could show signs of physiological stress, but it can't differentiate between causes of this stress, such as intense training, poor sleep, general anxiety, or even developing illness.

A Note on Privacy

HRV resides within the grey area that most product teams tend to overlook. While it may not be classified as PHI by HIPAA in consumer-oriented scenarios, it's very personal biometric information. HRV patterns may give an indication of stress levels, mental well-being, and even provide predictive information regarding the onset of diseases.

If you’re storing or processing HRV data, it's a good idea to consider your data retention practices, the third parties you share the information with, and whether your disclosures to users have been clear enough. Users are getting smarter about this. Regulators are, too.

Wrap Up

HRV is actually valuable data. This isn’t just marketing talk. There’s plenty of science behind HRV and the technology to measure it has been getting more refined. But it’s worth remembering that, as with most health data, it’s only valuable if used intelligently.

Know what you’re building upon. Design for personal context, not universal benchmarks. Make sure the data is easy to consume. And don’t take it any less seriously than your users do when they wear these devices every day, hoping it will make them feel better.

That’s really what it comes down to.

This thing is quietly recording your heart rate and movements while you’re sleeping. So it's a valid question to ask whether the wireless signals or electromagnetic field (EMF) radiation it emits could interfere with sleep quality, disrupt hormones such as melatonin, affect circadian rhythms, or produce other biological effects over time.

The concerns are part of the wider discussion on EMF’s and wireless technology. Although all smartphones, Wi-Fi, and wearables emit RF waves, the amounts of energy used and how they work can be quite different. Understanding what your sleep tracker is actually doing helps put those concerns into context.

Table of Contents

• How Sleep Trackers Measure Sleep

• What's Actually Transmitting From Your Ring or Watch

• How That Stacks Up Against Safety Limits

• Where the Melatonin Research Gets Misapplied

• What the Latest Research Actually Says

  • The Pushback

• What This Means for How You Wear It at Night

• The Bigger Risk Isn't the Radio

How Sleep Trackers Measure Sleep

Before discussing the impact of signals and EMF, we should know what exactly happens when a device monitors your sleep.

Sleep trackers rarely actually measure your sleep but rather use sensors to track movements, heart rate, heart rate variability (HRV), breathing rate, temperature, and more.

The software analyzes data and distinguishes between states like awake, light sleep, deep sleep, and REM sleep. As opposed to professional polysomnography, sleep tracking devices don't measure brain waves and so can't actually observe sleep. They only make an educated guess.

This is critical to understand, because it's not the Bluetooth radio itself that measures your sleep, but rather sensors.

What's Actually Transmitting From Your Ring or Watch

The chip in your Oura, Ultrahuman, or Apple Watch communicates with your phone via Bluetooth Low Energy (BLE). BLE was designed with a power budget rather than a performance budget because, in this case, power is more important than range when you have the receiver just a few inches away, in your pocket or on your nightstand.

The transmit power, according to the Bluetooth specification, is capped at 100 milliwatts. But most chips used by consumers are well below the maximum power limit, often at 1-10 milliwatts.

By contrast, a cell phone during a voice call can transmit up to 250 to 2,000 milliwatts. You're not carrying a miniaturized cell tower, you're carrying an item that transmits in bursts of low-power signals.

How That Stacks Up Against Safety Limits

The SAR metric is used to quantify the amount of RF energy tissue absorbs. This metric is measured in watts per kilogram. The Federal Communications Commission (FCC) caps the SAR level to 1.6 W/kg averaged over 1 g of tissue. But according to International Commission on Non-Ionizing Radiation Protection (ICNIRP) standards, the average SAR should be maintained at 2 W/kg averaged over 10 g of tissue.

No wireless communication device will be certified and made available on the market unless it meets the criteria of the SAR metric.

In the United States, the SAR limit for wrist-worn devices is 4.0 W/kg. According to Apple's Apple Watch RF exposure data, the watch has a reported SAR value of approximately 0.17 W/kg, while Oura reports a SAR value of 0.0003 W/kg for the Oura Ring. Both are well below regulatory limits, illustrating just how little RF energy these wearables typically emit.

According to an engineering evaluation published in 2024 by Kim, Sharif and Nasim, SAR levels associated with commercial wearable technology operated at 2.4 GHz have been found to comply with the regulatory threshold and the safety guideline at the distance of skin contact.

Where the Melatonin Research Gets Misapplied

Melatonin is the natural hormone responsible for regulating your sleep-wake cycle, which is why it has been frequently mentioned in the context of the relationship between EMFs and sleep. Indeed, there have been numerous studies in the past indicating that exposure to specific forms of electromagnetic fields might have an impact on melatonin production, circadian rhythms, or oxidative stress.

But the results from these studies have been quite mixed. Some research has found a notable effect, while others have found no significant effect at all. Most importantly, most studies that are constantly being cited refer to extremely low-frequency (ELF) fields generated by power lines, electrical wiring, and household electricity in general and not the Bluetooth device radiofrequency signals.

That research is real, but it often concerns extremely low-frequency (ELF) fields, those at 50-60 Hz found in power lines and electrical wiring, rather than the 2.4 GHz radiofrequency used by your Bluetooth ring. These are different parts of the electromagnetic spectrum with different interaction mechanisms.

Citing ELF melatonin studies to explain RF wearable exposure is a bit like citing research on UV exposure to explain what your microwave does. Related field, wrong frequency range.

What the Latest Research Actually Says

The strongest evidence we have today comes from a series of systematic reviews commissioned by the World Health Organization.

There were several reviews published between 2024 and 2025 which aimed to assess whether RF-EMF (Radiofrequency Electromagnetic Fields) exposure was connected with outcomes such as sleep disorders, headaches, and nonspecific symptoms.

No evidence suggesting a cause-and-effect relationship between RF-EMF exposure below current safety thresholds and sleep disorders was reported by either experimental or observational research.

This certainly doesn't solve the problem because of the uncertainty of the evidence. And this is quite low due to challenges in estimating actual RF exposures experienced by people. We're all surrounded by various signals emitted by our smartphones, Wi-Fi routers, laptops, cellular towers, and so on.

The conclusion is relatively simple, though: there's currently no evidence that would show that RF exposures using Bluetooth disrupts people’s sleep. That being said, researchers are still actively exploring the issue.

The Pushback

Not everyone agrees with these findings. Some researchers claim that WHO review fails to give enough weight to certain studies and that research findings on the subject are still lacking.

This criticism targets the whole body of work on RF-EMF radiation since most of them are based on mobile phones rather than wearable technology.

The debate is ongoing, but present research doesmn't show any disruptions caused by wearables with Bluetooth functionality.

What This Means for How You Wear It at Night

If the thought of wearing a sleep-tracking wearable next to your body for a full eight hours leaves you feeling uncomfortable, the quickest solution won’t be getting rid of it. Many wearables offer a low-power mode, airplane mode, or similar settings that disable Bluetooth communication while allowing the device to continue collecting data through onboard sensors such as the accelerometer and optical heart rate sensor.

For people concerned about EMF exposure, this setting reduces wireless transmissions during the night while still preserving most sleep-tracking functionality on the device. The reduction in radiofrequency emissions is typically small in absolute terms because Bluetooth Low Energy already transmits at very low power and only intermittently. Still, you may prefer minimizing any unnecessary wireless activity while you sleep.

I’m not saying that disabling Bluetooth improves sleep quality or health outcomes. But if doing so helps you feel more comfortable or less worried about wearing a device overnight and its EMF, it can be a practical compromise that allows you to continue tracking your sleep without the added concern.

The Bigger Risk Isn't the Radio

The bigger sleep-tracking problem probably isn't EMF at all. Neurologists are seeing more patients who walk in fixated on hitting a target number of REM minutes when using a wearable device that measures sleep stages based on movement and heart rate.

Unlike a laboratory polysomnography test, where sleep stages are measured directly from brain activity, the measurement provided by the wearable device is only an inference based on movement and heart rate. The term used for such obsession is 'orthosomnia', which is better described as the downside of wearing a sleep tracker rather than anything related to EMFs.

If you're going to worry about something at 2 a.m., the accuracy of the sleep data is probably a better place to focus than the Bluetooth chip. Sleep trackers estimate sleep stages rather than measuring them directly, and that limitation can sometimes create more anxiety than the radio signals themselves.

For women who are in their reproductive years, chances are your wearable technology has misread your luteal phase symptoms as either a result of being overtrained or even sick. This is because the technology likely detected a symptom that it doesn’t actually understand.

Let’s get into how this is actually happening by going from sensors to algorithms and finally to where the accuracy gap actually lives.

Table of Contents

• What the Menstrual Cycle Actually Does to Your Biometrics

  • Resting Heart Rate and HRV

  • Heart Rate Variability (HRV)

  • Skin Temperature

• How Wearables Measure These Signals

  • PPG Sensors and What They Actually Capture

  • Temperature Sensors: Continuous vs. Spot Measurement

• How the Algorithms Work

  • Calendar-Based vs. Physiology-Based Detection

  • How Machine Learning Classifies Cycle Phases

• Why the Accuracy Gap Exists

• What Cycle-Aware Algorithms Look Like in Practice

• Wrapping Up

What the Menstrual Cycle Actually Does to Your Biometrics

Before jumping into the sensors and algorithms, here's what they're actually detecting. The menstrual cycle isn't the noise within wearable data, but an active component that alters the physiology upon which any recovery or health algorithm relies.

There are three signals that tell the story.

Resting Heart Rate

Multiple studies using continuous wearable monitoring have confirmed that resting heart rate increases 2-7 bpm from the follicular phase to the luteal phase. One prospective study of 91 women observed that resting heart rate was 3.8 bpm higher in the mid-luteal phase compared to the period of menstruation.

Heart Rate Variability (HRV)

On the other hand, HRV changes in the opposite direction. In particular,  a meta-analysis of more than 1,000 participants showed the reduction of vagally mediated HRV from follicular to luteal phases of the menstrual cycle.

For example, one study reported that SDNN decreased from 154 ms in the follicular phase to 136 ms in the luteal phase, which represents a decrease of 12%. Progesterone is responsible for such effects. Specifically, it triggers the renin-angiotensin system (RAS), increases the total blood volume, raises HRj, and reduces parasympathetic influence. On the other hand, estrogen decreases HR (negative chronotropic effect) and leads to greater HRV.

So during the mid-luteal phase, you already have an increased RHR but a reduced HRV. To a recovery algorithm that does not know where you are within your menstrual cycle, this combination signifies stress, sickness or overtraining.

Skin Temperature

The temperature shift has been most thoroughly studied out of the three. Postovulatory rise of basal body temperature by 0.3–0.7°C due to progesterone’s effect has been known for over 100 years and constitutes the basis of traditional fertility awareness methods.

My Oura Ring data also shows that skin temperature usually increases during the luteal phase. It also tends to drop briefly just prior to ovulation due to an abrupt drop in body temperature related to estrogen.

The key point here is that signals change in the same direction at the same time, every cycle, predictably. When an algorithm treats these indicators separately, it's structurally wrong.

How Wearables Measure These Signals

PPG Sensors and What They Actually Capture

Heart rate and HRV measurements from wearables are done by Photoplethysmography (PPG). This sensor emits LED light, generally green for heart rate and red & infrared for SpO2, to shine on your skin. Light gets absorbed differently by blood depending on its volume, so as your heart beats and blood flows in capillaries, light reflected from your skin will be different for each heartbeat. Variation in light reflected is known as the PPG waveform.

Based on PPG waveform data, wearables calculate beat-to-beat intervals. While calculating the heart rate is relatively easy as it simply counts peaks per minute, HRV needs precise timing since it measures the variation in milliseconds between consecutive heartbeats. That’s where signal quality starts to matter a lot.

Placement of sensors on your skin also plays a vital role in this. Generally, finger devices such as smart rings like Oura and Ultrahuman give cleaner PPG signals compared to wrist-worn devices such as your Apple Watch, Garmin, or WHOOP. The finger has higher density capillaries, resulting in larger pulse amplitude and lower motion artifacts.

Wristwear makes up for this problem with more sophisticated signal processing techniques. But there's always a price to pay for that. For instance, Oura Ring 4 provides users with an 18-path multilayered wavelength PPG sensor with adaptive sensor configurations.

Temperature Sensors: Continuous vs. Spot Measurement

Temperature sensors incorporated in current wearables measure skin temperature and not core body temperature. These sensors, called thermistors, are capable of detecting temperature fluctuations in terms of changes in electrical resistance.

While there's a relationship between skin temperature and core body temperature, the two aren't the same. Skin temperature responds to factors such as room temperature, weather conditions, and temperature variation caused by changes in blood flow around the skin surface.

Even so, continuous overnight monitoring of skin temperatures may provide better information compared to traditional basal body temperature (BBT). With the fertility awareness technique, temperature is always measured at the same time each morning, right before getting out of bed. Missing a measurement or a bad night of sleep may negatively impact results.

Wearables take a different approach. By collecting temperature data throughout the night, they can identify longer-term trends and reduce the impact of short-term fluctuations.

Some devices, such as the Apple Watch Series 8 and later, Fitbit Sense, and Oura Ring, have temperature sensors. Most smart rings track temperature changes from an individual’s baseline, not the absolute temperature itself. It makes identifying temperature increases, which happen after ovulation, easier.

How the Algorithms Work

Calendar-Based vs. Physiology-Based Detection

Perhaps the most basic way of detecting the menstrual cycle is through a calendar model. The user inputs the first day of their period, the app calculates the average cycle length, and predicts the fertile window forward from there.

Apps like Clue, Flo, and older versions of Apple’s period tracker use this as their foundation. It’s a simple algorithm that needs no sensor data at all.

The problem with calendar algorithms is accuracy. These types of methods operate on regular cycles, but these aren't common in many women. For ovulation detection, for example, studies reveal that there's an average error of 3.44 days for calendar methods alone.

Also, calendar methods predict menstrual phases based only on dates entered by the user, whereas physiology-based approaches analyze sensor data such as temperature, heart rate and HRV to detect ovulation and cycle-related changes. For example, Oura uses heart rate and temperature to detect ovulation with an average error of 1.26 days.

How Machine Learning Classifies Cycle Phases

Machine learning algorithms don't use a single metric to determine where you are within your menstrual cycle. Rather, they examine patterns in several physiological indicators taken from wearables, such as skin temperature, heart rate, heart rate variability (HRV), and in some cases, electrodermal activity (EDA).

Over time, machine learning algorithms figure out which cycle stages correspond to which physiological patterns. For example:

• The luteal stage is characterized by an increase in skin temperature and changes in cardiovascular metrics.

• Ovulation causes changes in patterns in terms of temperature and heart rate.

• The menstrual phase can show its own distinct combination of physiological changes.

• The follicular phase is generally the most difficult one to recognize since its biometric signatures aren't clearly defined and tend to coincide with those from other phases.

A 2025 study found that machine learning algorithms can effectively determine the menstrual, ovulatory, and luteal phases. The accuracy of the results decreased when the follicular phase was added to the list of phases.

Modern cycle tracking apps have become complex because of this reason and they no longer depend solely on temperature. It becomes easier for a device to identify the phases of the menstrual cycle with every additional physiological signal that it captures.

Other technologies like the Vivoo FlowPad are also emerging that attempt to collect menstrual health data directly rather than inferring it from wearable sensors.

Why the Accuracy Gap Exists

The issue with wearables comes down to the fact that many of the metrics related to menstrual cycle phases aren’t exclusive to the menstrual cycle.

Take, for instance, the metrics such as a high resting heart rate, reduced HRV, and increased skin temperature. These could be observed during the luteal phase, but can also occur thanks to a range of other factors, including illness, lack of sleep, stress, consumption of alcohol, or even jet lag.

Yet another hurdle with menstrual tracking involves individual differences since some women might have significant changes during their menstrual cycles when it comes to temperature and HRV, whereas others will have minimal changes in those metrics.

This is why most menstrual tracking algorithms require individual baselines instead of population baselines, meaning that the more data is collected from a woman regarding her menstrual cycles, the better it gets at identifying her personal patterns.

What Cycle-Aware Algorithms Look Like in Practice

Until 2025, most wearables considered tracking cycles and recovery as two separate concepts. Oura became the first big company to connect the two.

Its updated algorithm accounts for increased resting heart rate, decreased HRV, and increased body temperature, all common during the luteal phase. Instead of automatically lowering readiness scores, it checks whether those changes are a normal part of the menstrual cycle.

This reduced the number of falsely low recovery scores during the second half of the menstrual cycle. In 2026, Oura went further with a dedicated AI model focused on cycles, fertility, pregnancy, and menopause.

WHOOP chose a different route through its metric called cardiovascular amplitude that measures heart rate and HRV variability throughout the whole cycle. Rather than focusing on individual phases, it looks at the overall physiological impact of hormonal changes.

Natural Cycles became the first fertility app that obtained FDA approval for contraceptive use, collecting users' body temperature data with the help of their wearables’ sensors like the Apple Watch, Oura Ring, Garmin, or its own dedicated NC Band.

Garmin, Fitbit, and Samsung track menstrual cycles, but those insights remain largely separate from their recovery and readiness metrics.

Wrapping Up

This boils down to the mismatch between measurements taken by wearables and what recovery algorithms were designed to handle.

PPG sensors and temperature sensors allow wearables to detect changes that happen across the menstrual cycle and they work well enough. Multi-parameter machine learning allows for reliable classification of the cycle phases, particularly those happening during ovulation.

But problems arise because many recovery algorithms have been trained on data biased towards male samples, where hormonal cycle variations are considered to be noise. These recovery algorithms lack the means to differentiate between luteal phase physiology and initial phases of an illness. Sensors won’t solve this problem, but algorithmic design will.

From the perspective of developing health apps using wearable device APIs, we already have access to health metrics that incorporate information about the current stage of the cycle. Oura provides it in specific endpoints, Apple integrates with HealthKit’s HKCategoryTypeIdentifier, and WHOOP ties it into its recovery model.

The problem here is that data can be accessed on these platforms via different APIs, data models, and integration techniques. While Oura, Apple HealthKit, and WHOOP may expose similar health metrics, there can still be differences in the sampling frequency, preprocessing methods, and metric definitions, making it hard to create algorithms that would work consistently across platforms.

This lack of standardization also contributes to the training data problem. Data collected by Oura, Apple Watch, and WHOOP can't always be combined easily since each platform stores and works with data differently. As a result, researchers and developers have to do additional work preparing and normalizing data before it can be used to train models.

There are sensors and the models have been improving, but the APIs are fragmented and the lack of training data is real. That’s where the work is.

But then I put another smartwatch on my wrist in an attempt to see how different those results were. Both watches were on the same wrist, at the same time, counting the same walk. One said 8,400 steps, while the other said 6,900.

Same wrist, 1,500 steps apart.

So naturally, I had a small crisis about everything I thought I knew.

The strange thing is that nobody really tells you this when you buy a fitness tracker. The packaging doesn’t read “lab accurate, not life accurate” or anything remotely close to this idea. The app never mentions the fact that two people who own wearables from the same company might actually have their steps counted differently.

But when you start looking into how wearables calculate your steps, things make more sense than you think.

Table of Contents

• Inside the MEMS Accelerometer

• How a Step Becomes a Number

• Why Wrist-Based Tracking Is Hard

• Why Slow Walking Confuses Wearables

• False Steps Are Real

• Why Lab Accuracy Doesn’t Match Real Life

• Do Some Brands Perform Better?

• How the Person Wearing the Device Affects Accuracy and What You Can Actually Do to Improve It

• Final Thoughts

Inside the MEMS Accelerometer

Each modern fitness tracker and smartwatch includes a MEMS accelerometer (Micro-Electro-Mechanical System). The MEMS consists of a tiny silicon chip with microscopic moving parts inside.

Body movements cause these microscopic components to move by an extremely small margin, which is captured by the sensor as a change in the electric signal. Most wearable devices have 3-axis or triaxial accelerometers. This means that they measure motions in three directions at once:

• up/down

• left/right

• forward/backward

These signals are captured continuously at about 50 times per second.

When you walk, your body produces a recognizable motion pattern, such as hip movement downward, movement of torso up and down and arm swinging rhythmically. What's most important is that your body bounces up and down with each step taken.

This vertical bounce is considered one of the clearest indications when someone walks and that is why step counters are so dependent upon it.

The accelerometer sends out three streams of information regarding movement. Many algorithms combine them into a single magnitude signal using the Euclidean norm:

‖a‖ = √(x² + y² + z²)

This gives the device a rotation-independent way to measure total acceleration.

The Role of Gyroscopes

Higher-end wearables also have a gyroscope that detects any rotations. The accelerometer and gyroscope together make up the Inertial Measurement Unit (IMU). This makes sure that the device can differentiate between you walking or just moving your wrist around.

How a Step Becomes a Number

The sensor itself can't calculate anything. All it does is produce raw motion data. The real work happens when that signal is interpreted through an algorithm, which is where things begin to differ dramatically.

All companies have their own proprietary algorithms. The algorithm used by Garmin is not the same algorithm used by Apple. And Apple’s is not the same as Samsung’s. Some of the most common approaches include:

• Peak detection: The algorithm detects repeating peaks in the acceleration signal that correspond to walking activity in humans. Since most people walk at a step rate in the range of 100 to 130 steps per minute, the device is designed to detect periodic activity in this range.

• Zero-crossing detection: Instead of looking for peaks, this approach involves determining how often the signal crosses a midpoint value.

• Autocorrelation: This method involves searching for repeating patterns in the motion signal over time.

• Frequency-domain analysis: Some algorithms search for the dominant frequency of walking.

• Machine learning models: Modern wearable devices have adopted machine learning approaches for walking pattern recognition based on training on large datasets of labeled walking behavior. These systems can differentiate between walking, running, typing, driving and random arm motion most of the time.

But every algorithm carries its own trade-offs. If the sensitivity of the algorithm is set too high, then the smartwatch may overlook slow or subtle motions. On the other hand, if the sensitivity is set too low, the watch would generate false steps when washing dishes or riding on bumpy roads.

There's no perfect setting, but every company has chosen to adopt its preferred compromise.

Why Wrist-Based Tracking Is Hard

The primary problem with modern wearable devices is their position on the body.

The waist-mounted pedometer is located close to the center of mass, which allows for the detection of very clear and strong signals. This is why traditional clip-on pedometers often perform better than you might expect.

On the contrary, smartwatches operate with signals detected from arm movements, and your wrist is noisy. The natural walk accompanied by a natural swinging of the arms provides a very strong correlation between leg movements and arm movements, but any real-life scenario may disturb this reading. If you’re pushing a stroller, holding shopping bags, a mobile phone, or walking with hands deep in your pockets, the correlation weakens and it becomes impossible to detect a specific signal related to leg movement.

Beyond this, even the side of the body where you wear the smartwatch plays a role in data interpretation. For example, the dominant arm tends to produce a stronger acceleration signal, but it also produces additional signals due to its active work in performing different routine actions.

This is one of the reasons why you would’ve noticed that most wearable devices require specifying which wrist you are strapping the device to during setup.

Why Slow Walking Confuses Wearables

One of the most surprising things about this research is that it’s far easier to accurately detect regular walking than it is to detect slow walking.

Wearables do well enough in detecting walking speed at normal paces. But as your speed drops, the acceleration and rhythm of the motion signals start to become smaller. This makes it difficult for algorithms trained primarily on healthy adults walking at normal treadmill speeds to identify slow walking correctly. The same studies may also show that wrist-worn trackers underestimate the number of steps in slow walking.

But why does this matter? The older we get, the slower and more restricted our gait becomes, which means that the motion signals detected by our wearable devices will be weaker. Then there are patients with neurological conditions like Parkinson’s disease or stroke, which can produce gait patterns that consumer algorithms simply weren’t trained to recognize.

So the next time you think your watch is broken, its likely that the detection system is simply working from assumptions that don’t match your movement pattern.

False Steps Are Real

Wearables not only overlook steps, but they can actually create non-existent ones. Because accelerometers measure all kinds of acceleration, quite a lot of actions completely unrelated to walking may trick the algorithm.

Activities that can cause phantom steps include:

• Driving on bumpy roads

• Typing aggressively on the keyboard

• Washing dishes

• Folding the laundry

• Drumming

• Taking a train or bus

There's an engineering dilemma here: the more sensitive your algorithm is to slow walking, the more prone to errors. The less sensitive it is, the better it works, but undercounts certain activities.

This is the reason for very different results from different brands despite having the same data entered.

Why Lab Accuracy Doesn’t Match Real Life

Fitness trackers tend to be surprisingly accurate within laboratory testing. In tests under controlled conditions, such as on treadmills, a number of wearables manage to reach step counting errors below 10%. But the real world tends to be messier.

Researchers call this “free-living” data because it involves movements made outside of the laboratory in normal life situations. The real-world walking pattern is characterized by irregularities such as rough surfaces, stops, carrying things, changes in speed, unpredictable arm movements and walking intervals. All these factors contribute to making step detection more difficult.

A smartwatch may prove to be exceptionally accurate in a controlled environment, yet still behave inconsistently in daily use.

Do Some Brands Perform Better?

Yes, but it’s rarely that significant. Studies indicate that Apple Watch, Garmin, Fitbit, and Samsung are all decent pedometers during normal walking conditions. Garmins are especially valued for their consistency and reliable tracking when outdoors. The Apple Watch reportedly works very well for altered gait and slower walking. Fitbits use a more sensitive algorithm and that may result in increased step counts.

But there are other elements at play. The speed at which you walk, natural movement of your arms, location of the device on the body, what activity you’re doing, and how the algorithm interprets your movement. The difference between two people using the same watch is often larger than the difference between two brands.

How the Person Wearing the Device Affects Accuracy and What You Can Actually Do to Improve It

A few things can noticeably improve step-count accuracy:

• Walk at a natural pace when possible: Wearables work best at moderate walking speeds. Walking slowly, shuffling and stop-and-go actions are significantly harder to detect with algorithms.

• Wear the device properly: Try putting on your wearable device snugly above your wrist bone to ensure consistent movement along with your arm.

• Set your dominant wrist correctly: Most smartwatches will adjust sensitivity settings based on which hand you use as the dominant one.

• Keep your arms moving naturally: Holding bags, pushing a stroller, or keeping your hands in your pockets might affect the accuracy because most wearable sensors depend greatly on the motion of your arms.

• Keep firmware updated: Most manufacturers continue to refine their algorithms through firmware updates.

• Use trends, not exact numbers: Step counts are better at tracking your habits and long-term patterns on the same device, but are less accurate compared to the number of steps registered by other brands.

• Be cautious with slow or altered gait: Older adults, rehabilitation patients, or those with brain injuries are likely to see undercounting of steps because most algorithms are trained on standard walking patterns.

Final Thoughts

The technology in today’s wearables is truly amazing. A small sensor the size of a grain of rice measures your movement multiple times each second, filters out noise and tries to make sense of the data generated by it. That’s an incredibly difficult engineering problem. But step counting is ultimately an estimation problem.

There are different step counts generated by various brands because they all have different filtering, motion classification, signal interpretation and sensor placement. None of them count your actual steps perfectly. They estimate the probabilities based on your movement patterns. And in reality, human movements aren't always neat and monitored.

So if you have two watches that generate different step counts, don’t panic. They do their best to understand messy movement data.