Every digital health product starts with one essential ingredient: raw physiological signals. Whether it’s heart rate (HR), heart rate variability (HRV), skin temperature, or blood oxygen saturation (SpO₂), wearables continuously capture streams of biological information. But beneath the surface, these signals are far from perfect. Movement creates motion artifacts that distort readings. Sensors drift over time. Environmental factors like sweat, temperature, or loose device placement introduce spikes, dips, and gaps. As a result, the data collected directly from a smartwatch or sensor often contains noise that can obscure the biological patterns product teams are trying to measure.
This is where signal smoothing and thresholding come in. Smoothing techniques reduce noise and stabilize the data so meaningful trends can emerge, while thresholding helps distinguish what matters, such as when a heart rate spike is real, or when sleep should be classified as light versus deep. These two processes turn messy, raw sensor outputs into clean, interpretable signals that support digital biomarkers, real-time alerts, activity classification, and reliable health insights.
In this article, we’ll break down the most important smoothing techniques, explore thresholding strategies, explain when each is appropriate, and show how combining them strengthens digital health products, creating the way for clinically valid, trustworthy metrics.
Raw physiological signals from wearables are rich in information, but they are also quite messy. Photoplethysmography (PPG) sensors, which measure heart rate and HRV, are extremely sensitive to motion. Even small wrist movements create light-scattering artifacts that distort pulse waveforms. ECG signals, although more precise, suffer from electrode noise, poor skin contact, or electrical interference. Accelerometers and gyroscopes produce rapid bursts of movement data that can include random spikes or false peaks. Even temperature sensors drift with ambient conditions, sweat, or changes in skin pressure.
These imperfections lead to common challenges: sudden spikes that mimic arrhythmias but aren’t real, dropouts where the signal disappears entirely, outliers that skew averages, and inconsistent sampling rates when devices switch modes to save battery. Without correction, these issues can dramatically distort downstream metrics.
Consider HRV: a single distorted interbeat interval can shift the entire calculation, producing misleading stress or recovery insights. Sleep staging relies on smooth motion and heart rate patterns; noise can cause false awakenings or misclassified REM periods. Step detection algorithms depend on clean accelerometer rhythms; sensor jitter can inflate step counts or miss them entirely.
In short, raw sensor data is not clinically reliable. Without smoothing, filtering, and thresholding, it cannot be used to build accurate biomarkers, trigger alerts, or support meaningful health insights.
Signal smoothing is essential for turning raw, noisy wearable data into clean, interpretable trends. Physiological signals, whether heart rate, HRV, SpO₂, or accelerometer readings—are naturally affected by motion artifacts, environmental interference, and sensor limitations. Modern digital health systems use several smoothing methods, each optimized for different signal types, noise patterns, and responsiveness requirements. Below are the main techniques and their trade-offs.
The moving average is one of the most widely used smoothing techniques. It applies a sliding window and replaces each value with the average of nearby data points. This reduces high-frequency noise and produces stable curves ideal for slow-changing metrics like resting heart rate, daily step counts, or general activity levels.
For example, accelerometer data during walking often includes erratic fluctuations from arm swings or wrist rotation. A moving average reveals the underlying rhythmic pattern, allowing more accurate step detection or gait recognition.
However:
Moving averages are computationally efficient but not suitable for high-precision, real-time applications like arrhythmia detection.
Median filtering replaces each point with the median of nearby values, making it highly resistant to spikes and outliers. Wearable sensors frequently produce abrupt distortions, for instance, a PPG sensor losing contact during a sudden wrist movement.
Median filters:
This technique is essential for stabilizing HR or HRV estimates and preventing noise from being mistaken for physiological abnormalities.
Frequency-domain filters separate desired physiological information from noise.
Each physiological measurement has a characteristic frequency band. Proper filtering ensures that analysis stays within that band, for example, removing high-frequency jitter from heart rate waveforms during running or isolating gait cycles during walking.
EMA gives more weight to recent data points, allowing fast adaptation while still providing smoothing. This is ideal for:
EMA offers a balance between responsiveness and stability, making it a favorite in modern fitness and stress-tracking algorithms.
Kalman filters combine predictive modeling with iterative correction. They estimate the “true” physiological signal by balancing expected behavior with incoming noisy measurements.
Used in:
Kalman filtering is computationally intensive but offers unmatched robustness and accuracy, especially when sensor data is unstable, inconsistent, or influenced by environmental factors.
Thresholding is the process of turning continuous sensor data into clear, actionable decisions. While smoothing cleans the signal, thresholding determines when something meaningful has happened, such as identifying a heartbeat, classifying a step, or detecting abnormal physiology.
Hard thresholds apply strict cutoffs (e.g., “HRV < 20 ms = high stress”), making them easy to interpret but sometimes too rigid for real-world variability. Soft thresholds use ranges, adaptive logic, or probabilistic models to account for differences in age, physiology, or baseline patterns, offering more nuanced classification.
Thresholding is essential in clinical and safety-critical applications. Arrhythmia detection depends on identifying intervals that exceed abnormal thresholds. Inactivity alerts rely on motion thresholds to detect prolonged stillness. Even fall detection combines accelerometer spikes and orientation angles that pass predefined limits. Without thresholding, wearables would collect data but never know when to act. It’s the mechanism that converts raw numbers into meaningful health events.
Thresholding is central to turning continuous biosignals into meaningful events. Different use cases require different types of thresholds, each with unique strengths and limitations. Below are the four main categories used in wearable analytics and digital biomarker development.
Fixed thresholds are the simplest and most widely used. They apply a single rule, such as heart rate > 100 bpm indicates tachycardia, regardless of user variation. These thresholds are intuitive, easy to implement, and computationally efficient, making them popular in consumer wearables and first-pass filtering. Fixed thresholds are best for clear physiological boundaries but are insufficient for personalized insights.
Adaptive thresholds adjust dynamically based on a user’s baseline, trends, and context. For example, instead of labeling “HRV < 20 ms” as high stress for everyone, an adaptive model might detect stress when HRV drops 30% below an individual’s 14-day rolling baseline.
This makes the system more responsive to personal physiology, time of day, sleep quality, and recent activity load. Adaptive thresholds are foundational for personalized medicine and digital therapeutics. HRV-based stress detection that recalibrates daily depending on recovery, sleep, and activity load.
Percentile thresholds classify events by comparing them to population distributions. Instead of fixed rules, values are interpreted based on where they fall relative to a broader dataset.
Identifying users in the lowest 10% of glucose stability or the highest 5% of nighttime heart rate variability. This method is powerful for cohort-level analytics, risk stratification, and public health programs. It highlights anomalies and outliers even when absolute values appear normal, making it especially valuable in preventive health.
ML-driven thresholding uses models that learn patterns rather than rely on preset rules. Anomaly detection algorithms, such as isolation forests, autoencoders, or Bayesian methods, can identify subtle, non-linear deviations far earlier than static thresholds.
These systems excel in detecting complex conditions like arrhythmia risk, sleep disturbances, or early signs of infection, where signals change gradually rather than crossing a single cutoff. Machine-learning-driven thresholds are the future of digital biomarkers, enabling more precise, individualized, and clinically meaningful interpretations of wearable data.
Smoothing and thresholding are not separate steps, they are two halves of the same decision-making pipeline. Smoothing cleans the signal so that thresholding can classify it accurately. If you apply thresholds directly to raw, noisy data, even tiny artifacts can trigger false alerts, misclassify activity, or hide meaningful physiological changes. By first stabilizing the signal, thresholds respond only to true patterns instead of momentary fluctuations.
This is especially important in real-world scenarios:
Detecting nocturnal HR drops: Nighttime readings often contain motion noise from shifting in bed. Smoothing removes spikes so that thresholding can reliably detect sustained declines in heart rate that may signal recovery, overtraining, or illness.
Activity classification: Accelerometer data is highly erratic. Once smoothed, thresholding can distinguish walking, running, or inactivity without misinterpreting random arm movements as steps.
Stress detection via HRV: HRV is extremely sensitive to noise. Smoothing stabilizes short-term variations, enabling adaptive thresholds to identify genuine stress events rather than artifacts from posture changes or faulty readings.
Together, smoothing provides the clarity — thresholding provides the logic. This combination transforms messy raw sensor streams into reliable, actionable health insights.
Signal smoothing and thresholding are the backbone of most digital health features users rely on daily. Without them, wearable insights would be erratic, inconsistent, and clinically unreliable.
Sleep scoring:
Sleep algorithms depend on clean heart rate, HRV, and movement signals. Smoothing removes short bursts of motion noise, while thresholds classify transitions between light, deep, and REM sleep. Without proper filtering, simply turning in bed could register as “awake,” degrading sleep accuracy and user trust. Find more information on sleep management with wearables!
Stress monitoring:
Stress detection uses HRV, which is highly sensitive to artifacts. Smoothing stabilizes beat-to-beat intervals, and adaptive thresholds help differentiate true physiological stress from minor fluctuations caused by posture changes or sensor drift. This reduces false stress alerts and improves behavioral recommendations. Check our stress management with wearables post!
Movement classification:
Accelerometer and gyroscope signals are notoriously noisy. Smoothing reveals underlying patterns, while thresholding distinguishes steps from random hand movements and classifies gait, running, sitting, or cycling. This ensures step counts and activity summaries feel accurate and consistent.
Recovery metrics:
Recovery scores often rely on nocturnal heart rate, HRV, and temperature trends. Smoothing highlights sustained patterns, and thresholds determine whether recovery is “optimal,” “normal,” or “impaired.” This enables athletes and everyday users to make informed choices.
Chronic disease monitoring:
For long-term conditions such as heart failure, diabetes, or COPD, smoothing stabilizes trends, and thresholds detect significant deviations. For example, smoothed resting heart rate paired with clinical thresholds can identify early deterioration.
When these techniques are implemented correctly, users experience more accurate insights, clinicians gain more trust in the data, and digital products deliver consistent value.
Despite being foundational, smoothing and thresholding are often misapplied in digital health products.
Over-smoothing: Excessive smoothing flattens important physiological changes, hiding meaningful trends such as HR spikes, arrhythmias, or stress responses.
Smoothing on top of smoothing: Layering multiple smoothing methods can distort the signal and create artificial trends, making it nearly impossible to validate biomarkers clinically.
Misaligned sampling rates: Algorithms built for high-resolution data often fail when applied to lower-frequency signals from wrist-based wearables. This leads to inaccurate HRV, step detection, or respiratory metrics.
One-size-fits-all thresholds: Applying the same threshold across all users ignores differences in age, sex, fitness level, and baseline physiology. This results in incorrect stress, sleep, or recovery classifications.
Ignoring clinical context: Some teams optimize for “clean-looking” charts without validating whether the output reflects true physiological meaning. Clinical relevance must always guide signal processing decisions.
By avoiding these pitfalls, product teams can deliver more accurate, trustworthy, and clinically aligned digital biomarkers.
High-quality digital biomarkers depend on precise signal smoothing and intelligent thresholding. These processes transform noisy raw sensor streams into insights that users, clinicians, and researchers can trust. When done right, they improve accuracy, reduce false alerts, enhance user experience, and support medical-grade applications. When done poorly, they lead to misleading metrics and unreliable outcomes.
As digital health continues to advance, mastering these techniques becomes essential for building safe, clinically meaningful products. Thryve provides teams with the infrastructure and validation-ready pipelines needed to handle this complexity without reinventing the wheel. Our API offers:
If you’re working on digital biomarkers or need reliable physiological data streams, Thryve can help you build, test, and scale with confidence.
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Paul Burggraf, co-founder and Chief Science Officer at Thryve, is the brain behind all health analytics at Thryve and drives our research partnerships with the German government and leading healthcare institutions. As an economical engineer turned strategy consultant, prior to Thryve, he built the foundational forecasting models for multi-billion investments of big utilities using complex system dynamics. Besides applying model analytics and analytical research to health sensors, he’s a guest lecturer at the Zurich University of Applied Sciences in the Life Science Master „Modelling of Complex Systems“
