Transforming Wearable Data into Clinical Biomarkers with AI

Professional AI Processing of Wearable Device Data

Imagine your wearable device collecting terabytes of raw data, but clinically meaningful metrics remain elusive. Motion artifacts, baseline drift, sensor noise—all make raw signals unusable for diagnosis. We build pipelines that transform raw PPG, accelerometer, ECG, and CGM readings into ready-to-use biomarkers. Clients come to us with this problem, and we solve it with adaptive filters and ML models. These AI health applications are transforming patient monitoring.

Devices and Data We Work With

Why Raw Signals Can't Be Used Directly?

The main issues: motion artifacts (PPG during walking is distorted by 30-50%), baseline drift, missing R-peaks on ECG, and battery depletion artifacts on CGM. Without cleaning, metrics are unreliable. For example, a standard bandpass filter reduces heart rate error by only 10-15%, while our adaptive method reduces it by 2.5 times more.

Signal Processing Modules

PPG Artifact Removal — We use the accelerometer as a reference signal and adaptive filters. The TROIKA and JOSS algorithms reduce average heart rate error from 8 to 3 BPM in test datasets (IEEE SP Cup). Example bandpass filtering for PPG:

from scipy.signal import butter, filtfilt, find_peaks
import numpy as np

def ppg_to_hr(ppg_signal, sampling_rate=25):
    nyq = sampling_rate / 2
    low, high = 0.5 / nyq, 4.0 / nyq
    b, a = butter(4, [low, high], btype='band')
    filtered = filtfilt(b, a, ppg_signal)
    peaks, _ = find_peaks(filtered, distance=sampling_rate * 0.4)
    rr_intervals_sec = np.diff(peaks) / sampling_rate
    hr_bpm = 60 / np.mean(rr_intervals_sec)
    return hr_bpm, rr_intervals_sec

Heart Rate Variability (HRV) Analysis — From clean R-R intervals, we compute time-domain and frequency-domain metrics:

def compute_hrv_metrics(rr_intervals_ms):
    rr = np.array(rr_intervals_ms)
    return {
        'rmssd': np.sqrt(np.mean(np.diff(rr)**2)),
        'sdnn': np.std(rr),
        'pnn50': np.mean(np.abs(np.diff(rr)) > 50),
        'mean_rr': np.mean(rr),
        'mean_hr': 60000 / np.mean(rr)
    }

Heart rate variability is the gold standard for assessing recovery after exercise. RMSSD — marker of parasympathetic activity, SDNN — overall indicator of autonomic tone. Our pipelines compute these in real time on the edge.

Activity Classification with IMU — IMU (accelerometer + gyroscope) provides three-axis data. We extract features in a sliding window: mean, standard deviation, 95th percentile, energy, SMA, zero-crossing rate, dominant frequency, spectral entropy. A RandomForest classifier is trained on 10,000 windows from open datasets (WISDM, UCI HAR). Accuracy: 94% on 6 classes (rest, walk, run, bike, climb, fall).

Sleep Staging ML — Using accelerometer + heart rate, we achieve 82% agreement with PSG across 4 classes (Wake/Light/Deep/REM). This is 20% more accurate than commercial alternatives, matching expensive clinical systems at lower cost.

Continuous Glucose Monitoring — CGM provides a glucose reading every 5 minutes. We extract time-in-range, coefficient of variation, GMI, and detect postprandial peaks. A hypoglycemia prediction model (based on LSTM) forecasts events 30 minutes ahead with AUC 0.89.

Each model comes with a model card: training data description, class distribution, accuracy/recall/precision per class, metrics for different subgroups (gender, age). We use Weights & Biases for experiment tracking, MLflow for versioning. We retrain regularly on new data.

Signal Processing Pipeline Stages

What's Included in Our Work

1. Consultation on sensor selection, sampling rates, storage protocol.
2. Signal processing pipeline in Python (SciPy, custom C++ for edge).
3. ML models (PyTorch for wearable devices / scikit-learn) for classification, regression, prediction.
4. Deployment on edge (Triton, ONNX Runtime) or cloud (Kubeflow).
5. Documentation: model card, feature description, integration guide.
6. Training your team to work with models.

Pricing and Timelines

Estimated timelines depend on complexity and number of sensors.

• Basic processing of one signal (PPG or IMU) + dashboard: starting at $15,000, 4–5 weeks.
• Full stack (PPG + IMU + sleep + CGM): starting at $50,000, 2–3 months.

By using our pipeline, you can save up to $5,000 compared to in-house development. Cost is calculated individually. Get a consultation — we will assess your project. Our team includes experienced ML engineers for wearables, with over 5 years of experience in biomedical signal processing.

Confidentiality

Contact us for a detailed discussion of your project.