Your watch doesn't actually see a "step" or directly measure "sleep." Instead, tiny sensors continuously detect things such as movement, changes in blood flow, electrical activity, and temperature. Those signals are converted into digital data, processed to remove noise, and analyzed by algorithms that look for meaningful patterns.

This process happens quietly in the background. Every movement of your wrist can become a stream of numbers. A change in reflected light can become a heart-rate reading. Several different signals can be combined to estimate what you were doing or how your body was responding.

By the time that information reaches the screen, the original signal has already gone through several layers of processing, turning something the sensor can detect into something you can understand.

What We'll Cover:

• What Is a Wearable Sensor Actually Measuring?

• The Tiny Sensors Doing All the Work

  • Accelerometer

  • Gyroscope

  • Photoplethysmography (PPG)

  • Electrocardiogram (ECG)

  • Temperature Sensor

• How a Physical Signal Becomes Data

• Raw Sensor Data Is Messier Than It Looks

• How Algorithms Turn Messy Signals Into Useful Information

  • Digital Filtering

  • Feature Extraction

  • Metric Calculation

  • Why Wearables Combine Multiple Sensors

• Where Does All This Data Go?

• A Sensor Can Be Accurate and the Final Result Can Still Be Wrong

• Wrap Up

When you check your daily summary, there's a fundamental gap between what the user interface displays and what the underlying hardware actually captured.

Consumer health trackers don't observe abstract concepts like "recovery" or "cardio strain." Instead, they observe physical, mechanical, and optical properties occurring right at the surface of your skin.

The sensor’s sole job is to capture raw physical reality without bias, while software carries the burden of interpretation. Because biological signals are dynamic and influenced by countless environmental variables, converting physical values into physiological insights requires comprehensive mathematical modeling.

A comprehensive review in Nature Digital Medicine on wearable sensing and analytics highlights that wearable health tracking is fundamentally a signal-processing challenge rather than a simple hardware readout.

The Tiny Sensors Doing All the Work

To capture physical signals accurately within a compact form factor, modern wearables rely on a cluster of miniaturized electromechanical and optical modules.

Accelerometer

The accelerometer detects linear acceleration and inertial forces across three spatial axes (X, Y and Z). Built using Micro-Electro-Mechanical Systems (MEMS), it contains microscopic suspended masses that deflect during movement, altering local electrical capacitance.

When you walk, your arm swings in a predictable, periodic pattern. The accelerometer records these repetitive acceleration peaks, allowing software to distinguish rhythmic locomotion from random gestures like typing or drinking water.

Gyroscope

While the accelerometer detects linear movement and gravity, the gyroscope measures angular velocity and rotational motion. It monitors how quickly and along which axis the device rotates in space.

By pairing a gyroscope with an accelerometer, the device can accurately determine its spatial orientation, ensuring that a simple wrist roll to view the screen isn't mistakenly categorized as an exercise rep or a walking stride.

Photoplethysmography (PPG)

PPG sensors use light to monitor changes in microvascular blood volume. Green light-emitting diodes (LEDs) illuminate the capillary bed beneath the skin, while adjacent photodetectors measure the light reflected back.

Because hemoglobin naturally absorbs green light, each ventricular contraction of the heart expands arterial volume, briefly increasing light absorption and lowering the reflected signal. The time between these reflection dips corresponds directly to individual pulse events.

Electrocardiogram (ECG)

While PPG relies on optical reflection, an ECG sensor detects the direct bioelectrical impulses driving the cardiac muscle.

When the heart beats, electrical currents spread across the myocardium, creating subtle voltage fluctuations across your body. By placing a finger on a dedicated case electrode while the back of the watch rests against your wrist, you complete a circuit that lets differential amplifiers measure the heart's depolarization and repolarization waves directly.

Temperature Sensor

Wearable temperature sensors employ thermistors or dedicated resistance temperature detectors (RTDs) resting against the skin surface.

It's worth noting that peripheral skin temperature isn't identical to core body temperature. Skin temperature fluctuates significantly based on ambient air, blood vessel dilation, and peripheral blood circulation. This makes it most valuable for identifying relative baseline deviations, such as sleep-phase cooling or early illness markers, rather than absolute clinical readings.

Comprehensive engineering overviews, such as this IEEE review of wearable physiological sensors, emphasize that these diverse hardware components must operate in close harmony to continuously reconstruct a clear picture of bodily activity.

How a Physical Signal Becomes Data

Before computational logic can make sense of physical phenomena, continuous analog events must be converted into discrete numerical data.

When an optical photodiode detects fluctuating light levels, it outputs a continuous, smooth electrical voltage. Computers, however, can't compute infinite continuous curves. They operate exclusively on discrete numbers. This transition is handled by an Analog-to-Digital Converter (ADC).

The ADC periodically samples the continuous voltage wave and quantizes it into a discrete digital value:

• Sampling Rate: Expressed in Hertz (Hz), this defines how many times per second the ADC records a value.

  Fast, electrically complex signals like ECG require sampling rates of 250 Hz to 500 Hz to capture sharp wave morphology without losing critical cardiac peaks. Conversely, skin temperature changes slowly and can be accurately tracked at a fraction of a single Hertz (such as one sample every few seconds), preserving battery life and system storage.

• Resolution: Typically measured in bits (such as 12-bit, 16-bit or 24-bit depth), resolution determines how finely the converter quantizes the electrical signal. A higher bit-depth allows the system to resolve tiny physiological variations, such as shallow pulse signals on darker skin tones or during cold weather, without the waveform clipping or flattening.

As explored in signal processing literature on wearable biometric data acquisition, selecting appropriate sampling frequencies and quantization ranges balances the need for high signal fidelity with power consumption constraints.

Raw Sensor Data Is Messier Than It Looks

In controlled clinical environments, diagnostic tools are firmly attached to stationary patients. Consumer wearables, by contrast, must gather physiological data during dynamic, everyday movements. Consequently, raw sensor output rarely resembles textbook physiological waveforms.

Everyday wear introduces severe real-world interference:

• Motion Artifacts: When you run, type, or grip objects, muscle contractions and sudden impacts physically rattle the device, creating massive inertial spikes that obscure subtle cardiac pulses.

• Sensor Displacement: A loose strap causes the device chassis to bounce against the epidermis, changing the optical path length between the LEDs and the photodetector, which introduces sharp baseline drifts.

• Environmental & Physiological Noise: Ambient sunlight leaking beneath the device edges can overwhelm sensitive photodiodes, while cold environments trigger peripheral vasoconstriction, drastically reducing blood volume in the wrist capillaries.

Because of this constant interference, wearable firmware includes automated Signal Quality Indices (SQIs). Before handing raw data to downstream algorithms, the system evaluates signal-to-noise ratios (SNR). If a specific data window is completely distorted by motion, the algorithm flags it as unreliable and discards it rather than generating an inaccurate reading. The dynamics of real-time artifact suppression are thoroughly analyzed in wearable artifact removal research.

How Algorithms Turn Messy Signals Into Useful Information

Once the signal is digitized and validated for basic quality, deterministic digital signal processing and algorithmic modeling convert the raw numerical stream into actionable human metrics.

Digital Filtering

Raw data first passes through digital bandpass filters configured to discard frequencies that fall outside the bounds of human physiology.

For an optical heart-rate signal, an algorithm suppresses frequencies below 0.5 Hz (30 BPM) and above 4.0 Hz (240 BPM), filtering out slow baseline drift and high-frequency electrical hum.

Feature Extraction

Instead of continuously processing thousands of raw digital samples, the software extracts concise statistical and morphological markers:

• Peak-to-Peak Intervals (△ t): The precise time duration between consecutive pulse crests.

• Signal Variance: The degree of dispersion in acceleration values across a rolling 5-second window.

• Dominant Frequency: The primary harmonic component identified through Fast Fourier Transforms (FFT).

Metric Calculation

For heart rate, the algorithm identifies valid systolic peaks, measures the inter-beat interval, eliminates mathematical outliers and computes the instantaneous beats per minute (60 / △ t).

For step detection, the algorithm processes 3-axis accelerometer arrays:

The software monitors this composite acceleration value for rhythmic threshold crossings and frequency signatures typical of a human gait, ignoring non-cyclical vibrations like riding a car over a bumpy road.

Machine learning classifiers, trained on large labeled movement datasets, help classify these feature profiles into specific activities such as cycling, swimming or sleeping.

Why Wearables Combine Multiple Sensors

A single physical sensor often lacks the context needed to accurately understand what your body is doing. To resolve ambiguity, devices use Sensor Fusion, combining data from multiple distinct sensors to generate more accurate inferences than any single sensor could provide alone.

Consider a sudden rise in heart rate from 65 BPM to 145 BPM:

• If the accelerometer detects no concurrent body movement, the algorithm may interpret the event as psychological stress, caffeine intake, or a cardiac anomaly.

• If the accelerometer simultaneously registers a sustained, high-cadence rhythmic movement signature, the system identifies the elevated heart rate as a normal physiological response to running.

By pairing optical, thermal, and inertial data points simultaneously, the system constructs a detailed picture of the user's metabolic state.

As detailed in the Biomedical Engineering survey on multimodal sensor fusion, combining complementary sensor streams helps eliminate false positives and balances out individual hardware limitations.

Where Does All This Data Go?

The data pipeline extends beyond the physical device on your wrist. Processing tasks are distributed across local hardware, your paired mobile phone and remote cloud infrastructure.

• On the Wearable (Edge Computing): Time-sensitive tasks run directly on low-power microcontrollers embedded inside the wearable. Filtering raw voltages, detecting steps and monitoring safety alerts (such as fall detection) happen locally, ensuring low latency, lower power consumption and better privacy.

• On the Smartphone: Because smartphones have faster multi-core processors and larger batteries, they handle heavy machine learning tasks, data visualization and the fusion of GPS traces with wrist kinematics.

• In the Cloud: Aggregated summaries are periodically uploaded to remote data centers for long-term historical tracking, deep longitudinal comparisons and training the next generation of algorithmic models across anonymized populations.

A Sensor Can Be Accurate and the Final Result Can Still Be Wrong

A common misconception is that an inaccurate health metric points directly to a broken sensor. In reality, a physical sensor can function with micro-voltage precision while the final displayed metric remains fundamentally incorrect.

There's a distinct difference between direct physical measurement and algorithmic estimation:

• The photodiode may accurately record light absorption.

• The ADC may convert those currents into digital samples without losing precision.

• Yet, if the user experiences severe vascular constriction from cold air or if an unpredictable arm movement mimics a pulse frequency, the peak-detection logic may latch onto the wrong frequency peak.

A metric can fail at multiple points along the pipeline: poor contact mechanics, edge-case physiology that falls outside the training dataset, or mathematical assumptions that break down during specific sports. Recognizing that wearables provide informed physiological estimations rather than direct clinical measurements is key to interpreting everyday health data properly.

Wrap Up

Wearable data goes through much more than a sensor before it becomes the numbers you see on your screen. Sensors capture physical signals, hardware converts them into digital data, and algorithms filter, combine, and interpret those signals to produce useful metrics.

Understanding this process also makes one thing clear: wearable measurements aren't always direct readings. They're often estimates built from several layers of sensing and computation. The better we understand that pipeline, the better we can understand what our wearable data is actually telling us.

That's when I realized why the two scenarios, although completely different, produce similar results. Your wearable doesn't know what you’re feeling. It just knows that your body is doing something and it's guessing what that is based on the signals your wearable is collecting, whether you're thrilled or terrified.

This doesn’t mean your tech is flawed. It’s a limitation baked into how every wearable on the market measures emotions and honestly, once you understand why, you’ll read your own data a lot differently.

Table of Contents

• What Your Wearable Is Actually Measuring

• Stress and Valence and Why Your Ring Only Sees Half the Picture

• Real Examples Where This Actually Messes With Your Data

  • Pre-race nerves vs a genuine anxiety spike

  • A tough workout vs a genuinely stressful day

  • Excitement before a first date vs pre-interview dread

• Why This Actually Matters Beyond Just Being a Fun Fact

• What Companies Are Actually Doing About This

• The Takeaway

What Your Wearable Is Actually Measuring

Let's get one thing straight first. Neither the ring nor the watch nor the chest strap is measuring "stress" or "excitement". There is no such sensor. All the things it's measuring are the following physical signs:

• Heart rate and heart rate variability (HRV)

• Electrodermal activity, also known as skin conductance or galvanic skin response

• Skin temperature

• Respiratory rate (in case the device measures it)

All those are the consequences of work on your autonomic nervous system. Whenever you activate your sympathetic nervous system, your heart rate increases, heart rate variability decreases, you sweat a little bit, and your skin temperature changes. The process happens no matter whether you have to perform on stage, have a fight with somebody, or to see the letter with rejection on your laptop.

All that your sensor knows is the "sympathetic activation." That's all the information it has access to.

Stress and Valence and Why Your Ring Only Sees Half the Picture

Psychologists have divided emotions into two general dimensions. Arousal refers to your level of excitement. Low arousal means you're calm, while high arousal indicates being highly charged. Valence refers to whether the emotion is felt as pleasant or unpleasant.

For fear and excitement, their locations in terms of arousal are quite close to each other, as they're both high arousal emotions. The difference lies in their valence. But your wearable device can't pick that up because there's nothing you can wear that can tell whether you feel good or bad about the experience.

Basically, your device is the technological equivalent of the age-old Schachter-Singer two-factor theory, which proposes that your body gives you a rush of physiological arousal and then your brain will determine the emotional label based on the context. If it's a roller coaster, you'll feel excited. If it's turbulence in a plane, you'll be afraid. The same level of arousal, but with different labels, determined by your brain, not your body.

You're stuck where your body is before your brain makes its determination.

Real Examples Where This Actually Messes With Your Data

I've seen this play out in my own Oura and Ultrahuman data more times than I can count, and I hear the same thing from people using wearables all the time.

Pre-Race Nerves vs a Genuine Anxiety Spike

The HRV levels of runners preparing for a HYROX competition or a half-marathon will most likely plummet on the morning of a race. This is the same thing that happens when you're dreading a hard conversation with your boss. This is because your heart is going through the same preparation process regardless of what's about to happen.

Your smartwatch will alert you of the low readiness status without giving any information on whether you should expect it to harm your performance or it's just adrenaline making you ready for the race.

A Tough Workout vs a Genuinely Stressful Day

This is a very common mistake. An intense HIIT session and a terrible, stressful day at work can result in very similar elevated heart rate and decreased HRV.

WHOOP and Apple Watch will record strain for both types of activities. But strain from weight lifting and strain from three difficult meetings aren't the same for your recovery despite having the same picture on the graph.

Excitement Before a First Date vs Pre-interview Dread

Both of them come with butterflies in the stomach, sweaty hands, and a racing heart. No matter how cool the smart ring you have on may seem to be, it can't understand whether you're happy about the situation or not.

Why This Actually Matters Beyond Just Being a Fun Fact

This may sound like a minor detail at first, but it has very real consequences for data interpretation. Some apps have already begun labelling elevated arousal readings as “stress". And while I understand the temptation in terms of product design, this has the potential to confuse people.

If your app tells you you're stressed every single time you're simply excited, you begin to second guess feelings which should otherwise be totally normal.

I've heard stories from my colleagues who remember being overjoyed with a situation and their smart ring labeled it as “anxiety”. That's not a great place for a wellness product to put someone in.

This also has some practical consequences in terms of training implications. The treatment of pre-competition arousal as if it was chronic stress may lead to incorrect advice regarding deloading. You certainly don't need the same recovery after performing well in a race you were pumped for as you would if you had a terrible race day.

But you may need recovery after the whole week in which your nervous system was in fight or flight because of the actual stresses, unrelated to your training.

What Companies Are Actually Doing About This

For the record, this is far from being an unknown issue in the industry. So here are a few directions worth knowing about:

1. Context Stacking

Relying not only on the reading of the sensor, some platforms are adding additional information in terms of calendar events, self-reported mood logging, or even location to help interpret what has caused certain reactions that couldn't be easily identified only based on the sensor readings.

The daytime stress feature of Oura Ring relies partially on this approach, prompting users to log what they were doing before a sudden spike.

2. Facial and Vocal Cues, Where Applicable

Some companies developing smart glasses and earbuds are trying to integrate biometric signals with vocal tones and facial muscle activity, as they contain additional valence information.

3. Just Asking You

Quite simply, there's no substitute for a self-report at the moment. The best information is coming from the user tapping their mood status. While it's unattractive from a marketing perspective, it covers exactly that gap which can't be closed by sensors alone.

Unfortunately, none of these solves the problem yet. Valence is a difficult parameter to deduce from wrist or finger readings and I don't think we're getting a direct measurement of it anytime soon.

The Takeaway

While your wearable may accurately describe your body’s experience, it can't tell what your emotions really are. Your wearable will let you know when your nervous system has been triggered and it will always be accurate in doing so, but it will never be able to tell you why. It will also never be able to say anything about the quality of your experience.

So the next time your ring signals a surge and tags it with the label of stress, take a minute to pause and reflect. What were you actually going through? You may discover that what you were experiencing was actually excitement.

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.

When Apple announced this September that its newest Series 4 Apple Watch includes EKG functionality, I imagined millions of hearts worldwide fluttering with delight.

The news was significant for several reasons. The chief reason is it is the first time a mainstream consumer wearable device will perform a sophisticated medical procedure. This same procedure we need to see the doctor for today.

To understand the significance, you need first to set aside the fact that health and fitness are already key reasons why consumers currently use wearables. Fitness trackers have had heart rate sensors for many years now.

(Heart rate sensors are convenient for fitness purposes, to be sure. But, they’re also primitive. Anyone with a finger can feel for a pulse and calculate a heart rate if necessary.)

An EKG (or ECG/electrocardiogram) is different than a heart rate measurement. For one, because an EKG looks at the heart’s electrical impulses to monitor for and identify specific heart anomalies. These anomalies are the immediate precursor to serious medical conditions. Conditions like strokes or heart attacks.

The technology can and will save lives.

By adding new and substantial medical features, Apple (and others) could be paving the way to a future in which wearables serve as the frontline for the diagnosis, treatment, and even prevention of serious medical conditions.

Hold your finger on the crown of the Series 4 Apple Watch, and you’ll get an EKG reading on the spot.

The Apple Watch is a class II medical device.

Adding an EKG to the Apple Watch wasn’t just an engineering task.

It also required the attention and cooperation of the notoriously finicky Food and Drug Administration (FDA). The FD has cleared Apple’s use of two medical-grade features in the new Apple Watch: 1) the EKG, and 2) the Apple Watch’s ability to identify and notify users of an irregular heart rhythm.

It’s important to note that, with the FDA, “clearing” a device is not the same as “approving” one. The latter is much harder to do and requires much significant collaboration and testing to mitigate potential risks. Clearing the Apple watch took just one month from application to decision. Class II devices like the new Apple Watch don’t require FDA approval because the associated health risks aren’t deemed great enough to warrant a more rigorous vetting process.

It’s also important to understand that the FDA clearance letters came with language stating that the cleared features are not intended for users under the age of 22, and are “not intended to replace traditional methods of diagnosis or treatment.”

Regarding the latter, what this means is that after your Apple Watch tells you it has detected a mild heart arrhythmia, you’ll probably want to follow up with your doctor to get a proper diagnosis.

On the other hand, if your Apple Watch tells you you’ve just had a heart attack, immediate urgency (and common sense) dictates that you’re going to go straight to the ER or call an ambulance, skipping the “traditional method of diagnosis” referenced in the FDA’s letter.

A beachhead for new medical functionality

With the regulatory precedent established, Apple product managers are undoubtedly considering more medical functionality, to double down on this growing notion of the Apple Watch as a medical device.

If so, what might we see from Apple down the road?

Well, as it turns out, Apple has for years been working on developing a blood glucose tracker that ties to the Apple Watch. Such a tracker could help with diagnosis, treatment, and perhaps even prevention of type 2 diabetes.

Diabetes currently affects an estimated 30 million people in the United States alone. The challenge has been creating noninvasive technology that doesn’t require permeating the skin you to get a reading, as current glucose trackers must do. According to a CNBC report last year, Apple has assembled a new team to pursue this “holy grail” breakthrough.

Here’s Apple CEO Tim Cook on the opportunity with glucose trackers:

“It’s mentally anguishing to stick yourself many times a day to check your blood sugar. There is lots of hope out there that if someone has constant knowledge of what they’re eating, they can instantly know what causes the response… and that they can adjust well before they become diabetic.”

Apple CEO Tim Cook appears to be gung-ho about enabling the Apple Watch as a medical device, with CNBC reporting last year that he was personally testing a blood glucose tracker for the Apple Watch.

Impact on healthcare practices and insurance costs

Cook’s predecessor, the legendary visionary Steve Jobs, reportedly first envisioned the popularization of consumer wearables that could provide blood glucose readings and other health vitals.

If Jobs’ vision of a versatile wearable health tracker comes to fruition, what effect might this have on the time-tested practice of the “annual physical?” For one, we might very well see formal doctor checkups give way to daily or even hourly automated testing through wearables like the Apple Watch.

And if this happens, what implication might widespread use of the device have beyond users’ health, for instance, on healthcare costs and the healthcare system itself? North American healthcare provider United Healthcare is an example of one insurance provider that is already offering financial incentives to customers that use wearable fitness trackers to meet daily fitness goals.

When customers get ahold of devices that also address the medical component of wellness and longevity, I’d certainly expect to see such financial incentives increase and healthcare premiums decrease.

Wearable medical technology is not a one-horse race

FitBit, the other big player in health and fitness wearables, has had a blood oxygen sensor built into its Ionic watch for more than a year. The sensor can be used to detect common breathing related disorders such as asthma, sleep apnea, and even heart arrhythmia conditions such as atrial fibrillation.

There’s a problem, though. At the time of this story’s publication, Fitbit has not yet publicly released software that makes use of the hardware sensor.

Samsung is another company that has demonstrated a commitment to adapting its wearable technologies to support medical use cases.

For instance, Samsung’s Gear VR headset is used by California-based IrisVision to allow legally blind users to see clearly in all aspects of life. Using custom IrisVision software, the IrisVision headset uses proprietary visual acuity algorithms to amplify any remaining useful vision a user still has. The result is clear vision and, for many users, the restoration of independence in their daily lives. IrisVision has the financial support of the National Institute of Health (NIH) and was developed by leading vision experts from Johns Hopkins University, UC Berkeley, Stanford, and UCLA.

Medical wearables will empower clinical researchers

Beyond consumer healthcare, clinical researchers stand to benefit from these recent developments in wearables, too. When doing any sort of clinical research, getting reliable data can be an enormous challenge, especially when a study depends on self-reported data or recollections.

Even for clinical studies that do use lab results, gathering data collection can be a challenge — costly, impractical, or even impossible.

In clinical testing and research, data collection can be a considerable challenge without sophisticated wearables.

But, if these measurements can be taken automatically by a wearable device, that largely addresses the issue, providing rich, reliable, and (perhaps) actionable data to researchers whose work depends on the availability of data they can trust.

Case in point, Johns Hopkins University has for the past three years been conducting a clinical study that allows participants to use their Apple Watches and the university’s EpiWatch app to provide clinical data (heart rate and accelerometer data) to further their understanding of epilepsy.

And, just last September, the NIH granted $2.5M to BioSensics to develop wearable sensors to monitor the symptoms of Huntington’s disease. A pilot study in collaboration with The University of Rochester is now underway.

Wearables provide the sensors and hardware, but user experience is paramount.

The hardware engineering associated with integrating medical sensors into small wearables is just one aspect of the challenge.

Another aspect of the challenge is inventing non-invasive or otherwise “user-friendly” sensors that can provide readings equivalent to what we, for example, currently need to draw blood to obtain. Frankly speaking, consumers will not buy a device that causes them pain or even startles them on a regular basis.

The regulatory hurdles (à la FDA) are another aspect of challenge, albeit one that seems promising based on the recent Apple clearances.

The final challenge is the software and user experience, which bring meaning to the data collected by the sensors. Sure, you can have an EKG provide readings — a line graph of electrical impulses — but how do you deliver that to your end user in a way that is not just useful, but is also intuitive and even enjoyable?

According to CitrusBits CEO, Harry Lee, apps are often the biggest competitive differentiator for wearables companies.

“In the wearables space, the hardware aspect is already somewhat commoditized. Everyone, more or less, has access to the same sensors, microprocessors, and chips. Where companies like Apple, Fitbit, and CitrusBits stand apart is in their ability to design intuitive and robust apps for smartwatches and other devices, given inherent screen-size and control constraints. This is absolutely key to adoption, and we’ve already seen once-promising wearables companies such as Pebble forced to shut down in recent years, in part due to weak app offerings.”

Looking ahead, the realization of Steve Jobs’ vision is clearly upon us: a future in which medical wearables will play a big role, perhaps even reducing the strain on existing healthcare systems.

It just may very well depend on a harmonious blend of computing hardware, non-invasive medical sensors, progressive regulatory oversight, and thoughtfully-crafted applications.