But displaying all this information is only one part of health tracking. The other is collecting it accurately and consistently, often without interrupting everyday activities.

That distinction has helped bring screenless wearables into the consumer market. Smart rings and screenless fitness bands can track various health and activity metrics without displaying notifications or constantly asking users to check their wrists. Instead, they collect information in the background, with most detailed insights available through a companion smartphone app.

Still, removing a screen doesn't automatically make a wearable more accurate. A device can collect thousands of readings and still produce unreliable results if its sensors struggle with movement, poor skin contact, or other sources of error. Similarly, a smartwatch with a display can collect reliable health data without requiring users to interact with it constantly.

So the real question is whether (and how) screenless wearables improve data quality or whether they simply make health tracking less distracting.

What We'll Cover

• The Screen Was Never the Sensor

  • What Actually Collects Your Health Data?

  • Why a Screen Has Little to Do With Accuracy

• What Happens When You Remove the Screen?

  • Less Checking, More Living

  • Could Comfort Lead to More Consistent Tracking?

  • The Difference Between Collecting Data and Displaying It

• The Accuracy Problem Nobody Talks About

  • More Measurements Don't Always Mean Better Measurements

  • Sleep Tracking Is a Good Example

  • The Problem With Trusting Every Number

• Can Fewer Notifications Lead to Better Health Data?

  • When Your Wrist Becomes Another Notification Center

  • The Case for Passive Tracking

  • Does Less Interaction Actually Improve Tracking?

• Where Screenless Wearables Fall Short

  • You Can't Always Leave Your Phone Behind

  • Immediate Feedback Has Its Uses

  • Less Distraction Can Also Mean Less Awareness

• So, Are Screenless Wearables Actually Better?

• Conclusion

The Screen Was Never the Sensor

What Actually Collects Your Health Data?

A display only shows information. The heavy lifting happens underneath. The core of most consumer wearables relies on optical heart-rate sensors, known as photoplethysmography (PPG). These sensors shine a light into your skin and measure how much of it bounces back to detect tiny changes in blood volume with every heartbeat.

Alongside those optical components, accelerometers and gyroscopes sense motion. They detect movement patterns to estimate your daily physical activity, count your steps, and record when you toss and turn in bed. Other components, like temperature sensors, feed even more raw signals into the device.

Finally, complex algorithms take all of this electrical noise and translate it into the estimated metrics you actually read, like sleep stages, recovery scores, and activity levels.

Why a Screen Has Little to Do With Accuracy

None of these underlying sensing principles require a display. A screenless ring and a full-featured smartwatch can use the exact same technology to track your pulse, yet deliver different results because of how securely the device fits, where it sits on your body, and the specific algorithms interpreting the data.

A 2024 living umbrella review published in Sports Medicine examined 24 systematic reviews covering hundreds of consumer wearables. It found considerable variation in measurement accuracy across different health metrics and devices. The findings make it clear that rigorous sensor validation, rather than the physical presence or absence of a screen, is what ultimately dictates a device's accuracy.

If the screen doesn't determine accuracy, what exactly changes when manufacturers decide to remove it?

What Happens When You Remove the Screen?

Less Checking, More Living

Removing a screen doesn't change the basic science of measuring the human body, but it fundamentally alters user behavior.

A screenless wearable collects data entirely in the background, without ever encouraging you to repeatedly check your statistics mid-stride.

For instance, athletes looking for the best screenless bands for running often prefer them precisely because they aren't tempted to interrupt their rhythm to analyze their heart rate on a glowing display.

Could Comfort Lead to More Consistent Tracking?

A wearable can only track your health if you're actually wearing it. Smaller devices, such as rings and lightweight fabric bands, are often easier for people to wear continuously throughout the day and sleep with through the night. Continuous wear naturally reduces gaps in your data. Better comfort can lead to more consistent tracking, provided you actually keep the device on.

But consistency isn't guaranteed just because a screen is gone. Comfort is highly subjective. Battery life, skin irritation, awkward charging habits, and individual preferences all influence whether someone abandons a device after a few weeks.

The Difference Between Collecting Data and Displaying It

Screenless devices intentionally shift all detailed analysis away from your wrist and onto a smartphone app or a web dashboard. This design choice separates passive data collection from deliberate data review. You gather the numbers continuously, but you only engage with them when you actively choose to open an app.

A screenless design can completely change the conditions under which your health data is collected, but it can't guarantee that the resulting information will be inherently more accurate or useful.

The Accuracy Problem Nobody Talks About

More Measurements Don't Always Mean Better Measurements

There's a widespread assumption that collecting a higher volume of continuous data automatically results in better insights.

Even if a wearable successfully logs a heart-rate reading every single minute of the day, those readings may still contain fundamental errors. Arm movement, a loose-fitting band, sweat, poor skin contact, and the specific type of activity you're doing can all introduce noise that skews the measurements.

Sleep Tracking Is a Good Example

Consider how wearables estimate your nightly rest. Most consumer devices track sleep using a combination of movement patterns, heart rate variability, and surface temperature.

Unlike laboratory sleep studies that rely on direct brain activity monitoring, wrist and finger wearables are making educated guesses. This means they can easily mistake lying completely still while reading a book for light sleep or struggle to accurately distinguish between deep sleep and REM cycles.

The Problem With Trusting Every Number

This is why proprietary metrics like a daily "recovery score" or a "sleep score" should never be treated as clinical medical measurements. Two distinct devices can produce noticeably different scores even when worn by the same person on the same night. A highly consistent measurement isn't necessarily an accurate one a device might just be making the exact same estimation error every single day.

A 2024 systematic review published in JMIR mHealth and uHealth compared the Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP against clinical polysomnography. The study demonstrated that sleep-tracking performance varies heavily depending on the specific device used and the exact sleep metric being measured.

Even if removing a screen doesn't improve the underlying sensors, it might still change something equally important: how much attention we give the data itself.

Can Fewer Notifications Lead to Better Health Data?

When Your Wrist Becomes Another Notification Center

Smartwatches are designed to do far more than monitor your pulse. They combine health tracking with incoming calls, text messages, calendar reminders and third-party app notifications.

While these features are undoubtedly useful for staying connected, they also train users to engage in frequent, habitual checking. Your wrist effectively becomes an extension of your smartphone screen.

The Case for Passive Tracking

There's a strong argument for collecting personal health data without constantly demanding interaction. A person might find it much easier to leave a low-profile wearable on throughout the day simply because it remains silent.

For instance, wearing a minimalist Garmin Cirqa smart band or a smart ring requires almost zero mental bandwidth compared to managing a smartwatch that buzzes every time an notification happens.

Does Less Interaction Actually Improve Tracking?

It's crucial to make the distinction between capturing human attention and measuring human biology. Fewer notifications certainly reduce daily interruptions, but they don't automatically sharpen optical sensor readings.

The toll of constant alerts is real. A 2026 study about smartphone-style social media notifications caused a measurable, temporary slowdown in cognitive processing that lasted around seven seconds per alert. While this clearly illustrates the cost of digital interruptions, it doesn't mean a silent wearable suddenly measures your biology more precisely. They're two separate issues.

Better data consistency is a distinct possibility, but only if that reduced interaction is what helps you wear the device more regularly.

Where Screenless Wearables Fall Short

You Can't Always Leave Your Phone Behind

Embracing a screenless wearable doesn't actually remove screens from the equation, it just relocates them. Because these devices lack displays, they rely heavily on their companion smartphone apps to communicate any meaningful information.

If you want to check your weekly health trends, review the intensity of a morning workout or simply adjust an alarm, you still have to pull out your phone.

Immediate Feedback Has Its Uses

A glowing smartwatch screen is highly practical in the middle of a strenuous activity. During a long run or a structured interval session, being able to glance down at your wrist to check your current pace, heart rate zone, or total distance without breaking stride is a significant advantage.

Less Distraction Can Also Mean Less Awareness

A screenless device collects information quietly, but many users genuinely benefit from seeing their progress unfold in real-time. Without a display to nudge you, it's easy to overlook useful feedback, like a reminder to stand up after hours at a desk.

Also, removing the screen often limits onboard tracking features, complicates on-the-fly troubleshooting if a device fits poorly and creates a heavy dependency on cloud syncing. Screenless designs involve clear trade-offs, simplifying one aspect of the experience while making daily data access slightly less convenient.

So, Are Screenless Wearables Actually Better?

Bringing this back to the original question: does ditching the display result in superior health tracking? The answer depends entirely on what you expect from a wearable.

Screenless wearables excel at making passive tracking feel less intrusive. If you're someone who easily gets distracted by buzzing notifications, a comfortable device that you actually wear consistently day and night will ultimately provide you with a more complete set of data.

But a screen doesn't automatically compromise a device's accuracy, just as its absence doesn't guarantee precision. The quality of the underlying sensors, rigorous clinical validation, a proper fit and the sophistication of the device's algorithms remain the fundamental pillars of reliable health measurements.

Smartwatches still hold distinct practical advantages for users who actively train and require immediate, mid-workout feedback.

The real question may not be whether a wearable has a screen, but whether it collects reliable information without making you feel that you have to spend your entire day looking at it. Sometimes, a device that simply stays out of the way is the most useful tool you can own. But the numbers it quietly collects in the background still need to earn your trust.

Conclusion

Screenless wearables show that tracking your health doesn't always require a screen. They can make data collection less distracting and may encourage people to wear their devices more consistently.

But removing a display doesn't make the data more accurate. That still depends on the sensors, algorithms, and how well the device fits.

Smartwatches and screenless wearables simply offer different ways to track health. While some people may prefer the convenience of a screenless device, others may find immediate access to their health data more useful.

Ultimately, better health tracking isn't about having fewer screens, it's about collecting reliable data and making it useful in everyday life.

I know you must be thinking that doesn't make sense: two different people are never going to have the same heart rate numbers anyway. Different resting baseline, different fitness levels, different everything. And yes, that part has nothing to do with wearables at all. But it's almost a competition at this point.

The real question here arises when you take the comparison of the same heart rate metric with different wearables but on the same person. The heart rate reading will be different – again because it will depend upon the type of sensors used in each case, the speed of sampling, and the software algorithms of the sensors.

After you learn how they work internally, you'll finally be able to understand why the numbers are different.

Table of Contents

• They're All Measuring the Same Thing, Just Not in the Same Place

• Why Your Heart Rate Isn’t Constant

• Sampling Speed Changes What the Device Actually Catches

• The Algorithm Matters as Much as the Sensor

  • Skin Tone and Wrist Size Add Another Layer

• Activity Type Throws Everything Off Differently for Each Brand

• What This Means If You're Comparing Numbers With a Friend

They're All Measuring the Same Thing, Just Not in the Same Place

All of the wearables worn on your wrist or finger use the same technique known as photoplethysmography (PPG). An LED shines light into your skin while a sensor measures how much light bounces back with each heartbeat. The technology remains the same among all manufacturers, but the difference here lies in the place of measurements.

Finger-based wearables measure arteries located closer to the surface of the skin than the ones on your wrist. This gives finger-based devices like the Oura Ring a real advantage.

This advantage becomes clear even in the sleep stage, since the movements of your fingers are smaller than the movements of your wrist during sleep. Research done to test the accuracy of the heart rate reading by the Oura Ring has been able to show high consistency with the ECG measurements.

This is one reason Oura tends to perform well in nocturnal heart rate verification. A comparative study conducted in 2025 in the journal Physiological Reports pitted 5 devices (the Oura Ring Gen 3, Oura Ring Gen 4, Whoop 4.0, Polar Grit X Pro, and Garmin Fenix 6) against a medical-grade ECG for 536 nights of sleep. The Oura Ring 3 and 4 correlated the most accurately with ECGs, WHOOP correlated moderately, and Polar had the lowest accuracy.

Why Your Heart Rate Isn’t Constant

One thing you need to know is that your heart rate isn’t constant even at rest.

It varies all the time with your breathing movement or any other stress response, even when you're sitting completely still. 68 bpm can become 72 bpm after just a few seconds and nothing out of the ordinary happened.

It's enough for two wearables to have slightly different readings just because they were measuring at different times. Before sensor location, sampling rate, or any other algorithm comes into play, what's being measured is already constantly changing.

Sampling Speed Changes What the Device Actually Catches

Position of the sensor is important, but frequency of readings is much more important than you might think. The PPG sensor from WHOOP 5.0 and WHOOP MG records reading 26 times per second. This is considered to be quite a good rate of recording from a continuous wearable.

Other smart devices, on the contrary, work every few seconds and not continuously. As a result, if your Heart Rate suddenly spikes for any reason, like standing up too quickly or being startled, the device will miss that spike and fill in the gap to replace a missing value.

The Apple Watch also has a different mechanism: it has a high hardware sampling rate, but that doesn't mean that its heart rate sensor works continuously. In reality, it works periodically depending on your activity, and only works continuously while working out and for several minutes after the exercise.

None of this is necessarily better in an objective sense. The continuous heart rate frequency sampling is fantastic for capturing all those short-term spikes, but results in more raw data that needs to be filtered and sorted out by the algorithm.

Garmin takes a middle ground here, depending on the model of the watch. In general, most Garmin devices take continuous samples at low frequencies all day long until they detect the start of your exercise and increase sampling frequency automatically.

And there's another trade-off here: the device saves battery when you're working at your desk, but this also means that the device is essentially betting on its own movement detection being accurate before it decides that your heart rate actually matters more right now.

The Algorithm Matters as Much as the Sensor

But here's the thing: the heart rate monitor only receives a raw signal of light intensity. And then something else must translate this information into a proper value. This translation is made using proprietary software for each brand separately. This is what results in the daily scores and graphs that we're all so obsessed with.

The task of this software is to determine whether the signal represents the heartbeat or some noise created by physical activity, skin contact, or ambient light.

A 2024 study published in Sensors concluded that the heart rate readings for the Oura Ring remained accurate compared to ECG when lower quality readings were included.

The case of heart rate variability was quite different. Heart rate variability measurements became less accurate when the device used lower-quality data, especially for people over the age of 45.

The heart rate algorithm used in WHOOP underwent a huge update in February 2026. The company used the training data collected at its own research facility for a wide range of skin tones, body types, and activity levels. Also, it used cloud-based processing for data analysis after the workout or a sleep session instead of edge processing.

This is a completely different strategy from having the watch do all the calculations on the device itself. Therefore, two devices that have identical hardware will give different figures from the same data depending on the interpretation of the software.

That’s also the reason why your figures might be altered by the brand itself even when it hasn't altered any of its hardware. With the release of the February 2026 update by WHOOP, there were some changes seen in heart rates and recovery scores for all 4.0 and 5.0 users, although none of the users’ activity had changed at all. The sensors remained the same, but the math behind them wasn’t.

The same happened to me with my step count on my Ultrahuman Ring AIR after a firmware update. If you've noticed your own numbers drift after an app update with no real change in your routine, this is usually why.

Skin Tone and Wrist Size Add Another Layer

There are more than just sensors, positioning, and algorithms involved, of course. There's also the human who wears the device, plus PPG sensors do not work the same for all people.

The amount of light absorbed by the skin is dependent upon the amount of melanin in the skin, meaning you'll get different results on different skin tones.

One of the most extensive analyses is offered by Koerber et al. (2023). They showed that this may lower the accuracy of reading in darker skin tones, not just for a particular brand, but for many brands.

PPG works by shining light into the skin and measuring the light reflected back. Since melanin absorbs more light, darker skin tones can reduce the amount of reflected light that reaches the sensor. This can lower the signal quality and, in some situations, reduce measurement accuracy.

The same goes if you have any tattoos on the area where the wearable sensor is placed. Dark or densely pigmented tattoo ink may absorb or scatter the emitted light, making it more difficult for the sensor to obtain a clean signal.

This doesn't necessarily prevent measurements altogether, but it can increase the likelihood of inaccurate or inconsistent readings.

In addition to this, the circumference of your wrist along with the device's tightness and fit will also have an impact on the amount of light that leaks out of the sensor rather than being reflected back into it. A watch that's too loose allows ambient light to enter and reduces the amount of reflected light captured by the sensor, while one that's too tight can alter blood flow beneath the sensor. Smaller wrists may also make it harder for the sensor to maintain consistent contact with the skin.

For these reasons, manufacturers generally recommend wearing the device snugly, but not tightly and positioning it slightly above the wrist bone during measurements.

Activity Type Throws Everything Off Differently for Each Brand

Resting heart rate during sleep is one thing, but your heart rate while working out is a totally different ball game for the sensors. This is where the difference becomes most evident between gadgets.

Motion artifacts are caused by movement, which means that your swinging arm interferes with the ability of the device to measure the reading the sensor is trying to capture. All companies have their algorithms to filter these movements and some handle certain movement patterns better than the others.

That's also the reason why chest strap monitors (that treat electrical signals directly from your heart rather than relying on light through skin) still outperform every wrist or finger-based wearable during high-intensity training.

What This Means If You're Comparing Numbers With a Friend

When you and your friend are wearing different brands of wearables and your numbers don’t line up, that’s perfectly normal and not an indicator that your device is faulty. All that really matters is consistency over time.

The resting heart rate trend on your Oura or Garmin should be giving you an accurate picture of your recovery and stress based on your personal baseline. Consistently improving your workouts and striving towards a better trend in HRV is better than a single measurement.

My honest takeaway here is that heart rate tracking on consumer wearables has definitely improved, especially in terms of resting and steady state numbers. But there are still significant differences between brands and these are very much real. They come from design choices, not manufacturing defects.

The final number relies on three factors: the placement of the sensor, how often readings are taken, and how the softtware processes that information.

But will I stop arguing with my partner on how he stresses me out, which is why my heart rate drops later than his at night? Probably not.

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.