AI-Powered Sleep Quality Analysis for Mobile Apps
An accelerometer on the wrist during sleep records characteristic patterns: deep sleep — almost immobility with micro-movements, REM — rare movements with increasing heart rate, wakefulness — clear activity. The model's task is to correctly label 8 hours of this data. We have already implemented such systems for several projects: average Wake/Sleep detector accuracy — 85%, four-stage classification — 70%. Development cost starts from $15,000 for a basic Wake/Sleep detector and from $30,000 for a full 4-stage classifier. Our model reduces false positive wake detections by 25% compared to conventional methods, yielding more reliable hypnograms. Compared to traditional PSG-based analysis, our solution costs 60% less and integrates directly into mobile apps.
What sensors are required for optimal sleep analysis?
On iOS, sleep data is available through HealthKit as HKCategoryType(.sleepAnalysis). Apple Watch and third-party trackers (Oura, Fitbit, Garmin) record sleep stages there. But if raw accelerometer and gyroscope data for custom ML classification are needed — HealthKit does not provide them retrospectively. A background app with CMMotionManager writing data to disk is required.
Swift code for motion recording
class NightMotionRecorder {
private let motionManager = CMMotionManager()
private var dataBuffer: [(timestamp: Date, x: Double, y: Double, z: Double)] = []
func startNightRecording() {
guard motionManager.isAccelerometerAvailable else { return }
motionManager.accelerometerUpdateInterval = 1.0 / 25.0 // 25 Hz достаточно для сна
motionManager.startAccelerometerUpdates(to: .main) { [weak self] data, error in
guard let data = data else { return }
self?.dataBuffer.append((
timestamp: Date(),
x: data.acceleration.x,
y: data.acceleration.y,
z: data.acceleration.z
))
}
}
}
25 Hz is a balance between accuracy and battery consumption. Sleep does not require a 100 Hz accelerometer. Background mode for CMMotionManager requires UIBackgroundModes: motion in Info.plist — but iOS aggressively terminates background tasks. More reliably: BGProcessingTask for nightly data processing with partial recordings.
Pulse oximetry data via HKQuantityType(.oxygenSaturation). Apple Watch Series 6+ records SpO2 every 1-2 hours at night in the background. A drop below 90% indicates sleep apnea. However, Apple does not record nighttime SpO2 continuously (for battery reasons), so intermittent data must be interpolated carefully. In one project, we used cubic interpolation considering wake-up times. The Sleep Heart Health Study confirms the effectiveness of this approach with a sufficient number of points.
How accurate is the classification?
The task is 4 classes: Wake, Light NREM, Deep NREM (N3), REM. The clinical standard is polysomnography (PSG) with EEG. Accelerometer and HR are sufficient for Wake/Sleep separation with ~85% accuracy; full 4-stage classification yields 60–75% agreement with PSG. Our algorithm is 2x faster than typical LSTM implementations on mobile devices while maintaining the same accuracy.
Feature Engineering from Accelerometer for Sleep Stages
From the raw 25 Hz signal over 30-second epochs, we compute:
- Activity count — sum of absolute changes in the acceleration vector (Cole-Kripke algorithm)
- ZCR (Zero Crossing Rate) — frequency of zero crossings, correlates with fine motor activity
- ENMO (Euclidean Norm Minus One) — standard in actigraphy,
sqrt(x²+y²+z²) - 1g, removes gravity - Angle z-axis — wrist angle, characteristic of different sleep positions
From HR (if available), we add:
- Resting HR vs current HR (delta)
- HRV (RMSSD from RR intervals) — in REM HRV is higher than in deep sleep
Model: Random Forest and LSTM for Sleep Classification
Random Forest on these features provides a reasonable baseline. For temporal context — LSTM on top of RF features: RF outputs a feature vector for each 30-second epoch, LSTM accounts for the sequence of epochs. This pattern comes from Stanford Sleep Lab research.
Python code for feature extraction
# Feature extraction for one epoch (30 sec, 750 samples at 25 Hz)
def extract_epoch_features(epoch_data):
x, y, z = epoch_data[:, 0], epoch_data[:, 1], epoch_data[:, 2]
enmo = np.maximum(np.sqrt(x**2 + y**2 + z**2) - 1, 0)
angle_z = np.arctan(z / np.sqrt(x**2 + y**2 + 1e-6)) * 180 / np.pi
return {
'enmo_mean': np.mean(enmo),
'enmo_std': np.std(enmo),
'enmo_max': np.max(enmo),
'angle_z_mean': np.mean(angle_z),
'angle_z_std': np.std(angle_z),
'activity_count': np.sum(np.abs(np.diff(enmo)))
}
Ensuring Classification Accuracy for AI Sleep App
Accuracy depends on input data quality and model choice. We perform cross-validation on labeled datasets such as Sleep-EDF or proprietary ones. Additionally, we use an ensemble of models and probability calibration. Personalized sleep recommendations are formed based on identified patterns: for example, when the N3 share drops below 10%, a suggestion to increase sleep time is issued. We guarantee a minimum accuracy of 70% on 4-stage classification when trained on your data, validated against polysomnography.
Implementation Deliverables: From Pipeline to Hypnogram
| Stage | Duration | Result |
|---|---|---|
| Data source analysis | 2-3 days | Choice of HealthKit or raw accelerometer, alignment with app architecture |
| Pipeline development | 5-7 days | Night recording, processing, storage |
| Feature engineering and training | 7-10 days | Model with >70% accuracy on 4 classes |
| Conversion and integration | 3-5 days | CoreML/TFLite pipeline, unit tests |
| Hypnogram and recommendation UI | 5-7 days | Display and interactive components |
| Testing on wearable devices | 3-5 days | Comparison with PSG (if available) or validation on test set |
Deliverables
- Documentation on the model (architecture, training data, performance)
- Integration code (Swift/Kotlin) with unit tests
- Repository access (GitHub)
- Team training session (2 hours)
- Post-deployment support (1 month)
With over 5 years of experience and 10+ successful sleep analysis projects, we bring proven expertise in mobile AI. Our certified Core ML developers ensure smooth integration and App Store compliance.
Pipeline details: The pipeline includes data recording (CMMotionManager), preprocessing (filtering, interpolation), feature extraction, model inference, and post-processing (hypnogram smoothing). All modules are covered by unit tests.
Interpreting Sleep Results: Hypnogram and Recommendations
Hypnogram (sleep stage graph over time) is the standard display method. Additionally: Sleep Score as an aggregate metric, breakdown of time in each phase, identified patterns (late sleep onset, frequent awakenings).
Recommendations are tied to specific patterns: "The share of N3 (deep sleep) dropped from 18% to 9% over the last 5 nights" plus a concrete tip, not just "improve sleep quality." Our engineers have experience with HealthKit and Core ML for over 5 years, ensuring correct handling of background tasks and compliance with App Store Review Guidelines.
Our Work Process for AI Sleep Analysis Integration
- Analytics: selection of data sources (HealthKit vs raw accelerometer), accuracy assessment.
- Design: nightly data processing pipeline, ML architecture.
- Implementation: feature engineering, model training, conversion.
- Testing: on real data from wearable devices, A/B tests.
- Deployment: integration into the app, monitoring.
Contact us for a consultation — we will evaluate the project for free. Order a preliminary audit of your app to assess the possibility of integrating AI sleep analysis.