AI Mental Health Monitoring via Wearables

Patients with chronic stress often miss deterioration until a crisis. Wearable devices—Apple Watch, Garmin, Oura Ring—capture physiology (HRV, EDA, temperature), but raw data is useless without intelligent interpretation. Our team, with 7+ years of AI/ML experience in healthcare, builds systems that transform raw sensor streams into interpretable stress and anxiety metrics. The key challenge is inter-person variability: RMSSD = 30 ms can be normal for one person and alarming for another. Without personalization, models err in 40% of cases. We offer a solution that adapts to each user within 2 weeks of data collection. The result is an AUC of 0.82–0.88 on tests, 15–20% better than one-size-fits-all models. We assess your project in 2 days and provide a turnkey solution. Contact us for a preliminary analysis.

AI Mental Health Monitoring: Physiological Markers

The autonomic nervous system (ANS) directly reflects the stress response. Sympathetic activation leads to tachycardia, reduced HRV, increased sweating, and peripheral vasoconstriction. Key biomarkers are summarized below:

Feature Engineering from Physiological Data

HRV features in time and frequency domains:

def compute_hrv_stress_features(rr_intervals_ms, window_sec=300):
    """
    5-minute window — standard for HRV analysis (Task Force)
    """
    rr = np.array(rr_intervals_ms)

    # Time-domain metrics
    time_features = {
        '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),
        'cv_rr': np.std(rr) / np.mean(rr)  # coefficient of variation
    }

    # Frequency-domain metrics (PSD via Welch)
    from scipy.signal import welch
    f, psd = welch(rr - np.mean(rr), fs=4.0, nperseg=256)  # upsampled to 4 Hz

    lf_mask = (f >= 0.04) & (f < 0.15)
    hf_mask = (f >= 0.15) & (f < 0.40)

    lf_power = np.trapz(psd[lf_mask], f[lf_mask])
    hf_power = np.trapz(psd[hf_mask], f[hf_mask])

    freq_features = {
        'lf_power_ms2': lf_power,
        'hf_power_ms2': hf_power,
        'lf_hf_ratio': lf_power / (hf_power + 1e-8),
        'total_power': np.trapz(psd, f)
    }

    return {**time_features, **freq_features}

EDA processing using neurokit2 (see https://github.com/neuropsychology/NeuroKit):

import neurokit2 as nk

def process_eda_signal(eda_raw, sampling_rate=64):
    """
    neurokit2: decompose EDA into tonic (SCL) + phasic (SCR) components
    """
    signals, info = nk.eda_process(eda_raw, sampling_rate=sampling_rate)

    return {
        'scl_mean': signals['EDA_Tonic'].mean(),
        'scr_count': len(info['SCR_Onsets']),  # number of acute stress reactions
        'scr_amplitude_mean': signals['SCR_Amplitude'].mean(),
        'scr_recovery_time': signals['SCR_RecoveryTime'].mean()
    }

How AI Analyzes Wearable Data for Mental Health

We build binary stress classifiers using Random Forest or XGBoost, combining HRV, EDA, and actigraphy. Models are pretrained on public datasets (WESAD, DEAP) and adapted to the target population via transfer learning. For production, we use ONNX Runtime with INT8 quantization — latency p99 < 50 ms on device.

Why Personalized Baseline Is Critical

An absolute RMSSD of 30 ms may be normal for one person and low for another. We use a rolling 2-week average per user:

def personalized_stress_score(current_hrv, personal_baseline_hrv):
    """
    Relative deviation from personal baseline
    More interpretable than absolute values
    """
    hrv_deviation = (current_hrv['rmssd'] - personal_baseline_hrv['rmssd_mean']) / personal_baseline_hrv['rmssd_std']
    return -hrv_deviation  # invert: decrease in HRV = increase in stress

Multimodal Fusion

Late fusion combines scores from each sensor, ensuring robustness if one channel is missing (e.g., no EDA):

def fuse_modalities(hrv_features, eda_features, actigraphy_features):
    hrv_score = hrv_stress_model.predict_proba([hrv_features])[0][1]
    eda_score = eda_stress_model.predict_proba([eda_features])[0][1]
    activity_score = activity_stress_model.predict_proba([actigraphy_features])[0][1]

    final_score = 0.45 * hrv_score + 0.35 * eda_score + 0.20 * activity_score
    return final_score

This approach yields AUC 0.82–0.88 on internal tests — 15–20% better than using HRV alone.

Model Comparison: Which to Choose?

XGBoost offers the best accuracy-to-speed ratio. For edge devices, we quantize to INT8 — size shrinks 4x without AUC loss.

Digital Phenotyping of Mood

Digital phenotyping from the smartphone includes screen time, GPS tracking, social interactions, and sleep patterns. From these, we predict PHQ-9 scores with AUC 0.75–0.82. This does not replace questionnaires but identifies trends without active input.

Limitations and Ethical Aspects

Model accuracy depends on context: laboratory stress (TSST) differs from real-life. We do not diagnose — the system recommends consulting a specialist when stress-score is high. All psychological data is processed on edge to comply with GDPR (Art. 9). Training data contains bias — we actively work on diversification.

Process of Work

1. Analytics: audit your devices and data streams, select biomarkers.
2. Design: pipeline architecture — collection, preprocessing, storage (InfluxDB + pgvector).
3. Development: models and quantization, integration with mobile app.
4. Testing: A/B tests, validation on real users.
5. Deployment: CLI, mobile SDK, or cloud API with monitoring.

Deliverables

We provide: pipeline documentation, trained models with metrics, iOS/Android SDK, Grafana dashboards, and instructions for adapting to new devices. We guarantee 3 months of post-deployment support. Typical project investment starts at $15,000 and can save up to 40% on employee wellness costs through early stress detection. Contact us for a detailed quote.

Timeline

Basic stress monitor (HRV + baseline + mobile app) — from 4 to 5 weeks. Full cycle with EDA, digital phenotyping, and edge processing — from 3 to 4 months. Cost is calculated individually after preliminary audit.

Get a consultation from an AI engineer with 7+ years of experience in healthcare ML. Save up to 40% on budget through early stress detection — assess the benefit for your project.

When does a time series forecasting model fail in production?

The CFO requests a quarterly sales forecast. An analyst builds SARIMA on three years of data, achieves MAPE 8.3% on the test set, and deploys. Two months later, the metric in production jumps to 23%. The root cause: the model was trained on pre‑COVID data, tested on a stable period, but production hit a promotion and supply chain disruption. Data leakage plus distribution shift—perfect notebook numbers, a broken forecast in reality. We have seen this pattern dozens of times across retail, fintech, and IoT. Our team has delivered more than 50 forecasting projects over 5+ years.

Incorrect cross-validation. Standard train_test_split for time series creates data leakage: the model sees future values during training. The correct approach is TimeSeriesSplit or walk‑forward validation with an expanding window.

Multiple seasonality. Hourly electricity consumption has three seasonalities: daily (24h), weekly (168h), yearly (8760h). SARIMA handles only one. Prophet can handle multiple but scales poorly to thousands of series.

Missing values and anomalies. A missing sensor reading is information (the sensor turned off), not NaN. Linear interpolation destroys this signal. Proper handling depends on the missingness mechanism.

Cold start. A new SKU in a 50,000‑item assortment has no history, yet a forecast is needed. Standard approaches fail; cross‑learning or feature‑based methods are required.

Why is model selection critical for your data?

Prophet (Meta) – a solid start for business data with clear seasonality and holidays. Fast setup, interpretable, built‑in outlier detection. Fails on irregular patterns and does not scale beyond ~10k series without parallelization.

Gradient boosting on features (LightGBM, XGBoost) – often underestimated. Engineer lags (t‑1, t‑7, t‑28), rolling means, day‑of‑week, holidays. The model trains on all series simultaneously, solving cold start via transfer learning. MAPE in retail often beats neural nets with proper feature engineering.

TFT (Temporal Fusion Transformer) – a transformer designed for interpretable forecasting with covariates. Built‑in variable selection, temporal attention, quantile outputs. Available in pytorch‑forecasting. Requires ~10,000+ records per series for stable training.

PatchTST – splits the series into patches (like ViT for images), capturing local patterns better than classic transformers. Excellent for long‑horizon forecasting (96–720 steps ahead).

N‑HiTS, N‑BEATS – attention‑free neural architectures, faster than TFT, competitive accuracy. N‑BEATS won the M4/M5 benchmarks for tasks without covariates.

How do we deploy TFT in production?

A typical pipeline via pytorch‑forecasting:

training = TimeSeriesDataSet(
    data,
    time_idx="time_idx",
    target="sales",
    group_ids=["store", "sku"],
    min_encoder_length=max_encoder_length // 2,
    max_encoder_length=max_encoder_length,  # 120 days
    min_prediction_length=1,
    max_prediction_length=max_prediction_length,  # 28 days
    static_categoricals=["store_type", "category"],
    time_varying_known_reals=["price", "promo_flag"],
    time_varying_unknown_reals=["sales"],
    target_normalizer=GroupNormalizer(groups=["store", "sku"], transformation="softplus"),
)

A common mistake: the default target_normalizer (StandardScaler) breaks predictions for series with zero values (no sales on weekends). GroupNormalizer with transformation="softplus" is the correct choice for count data.

Case study: retail demand forecasting

A chain of 120 stores, 8,000 SKUs, 28‑day forecast horizon. The original system: SARIMA per series, MAPE 18.4%, retraining cycle – 6 hours. We replaced it with TFT on PyTorch + pytorch‑forecasting: a single model for all series, MAPE 11.2%, retraining – 40 minutes on an A10G. Feature importance via variable selection revealed that day_before_holiday influences more than the holiday date itself. Annual savings on inference alone exceeded $50,000.

Step‑by‑step configuration

1. Data collection and preparation. Handle missing values (mark NaN, interpolate only for technical failures), aggregate to required frequency, engineer covariates (holidays, promotions, prices).
2. Create TimeSeriesDataSet. Set group_ids (store + SKU), time index, forecast horizon. Choose target_normalizer based on target distribution.
3. Train a baseline. Prophet or LightGBM first – to understand complexity.
4. Train TFT. Use TemporalFusionTransformer with loss=QuantileLoss(), tune learning rate and hidden layer sizes.
5. Validate and interpret. Walk‑forward test, analyze variable selection, build attention heatmaps.

How to properly evaluate forecast quality?

RMSE alone is misleading – it over‑penalizes large values. Our standard set:

• MAPE – interpretable, unstable near zero.
• sMAPE – symmetric, avoids division by small numbers.
• MASE (Mean Absolute Scaled Error) – normalized relative to a naive seasonal forecast, ideal for comparing series of different scales.
• Pinball loss – for probabilistic forecasting, inventory management.

We guarantee a model card with these metrics on the validation set and walk‑forward results on at least 6 months of history.

What deliverables do you receive?

• Documentation of chosen architecture and hyperparameter rationale.
• Reproducible training and inference pipeline (Docker + CI/CD + Airflow/Prefect).
• Committed code with unit tests for key components.
• Team training: retraining, output interpretation, deployment of new versions.
• 3 months of post‑delivery support (consultations, bug fixes, fine‑tuning).

The model is deployed via FastAPI or Triton Inference Server. Retraining is scheduled (e.g., weekly) via Airflow with drift validation and automatic rollback if metrics deteriorate.

Process and timeline

We start with EDA: visualization, ADF test, STL decomposition, analysis of missing values and outliers. This takes 2–3 days but often reveals systemic data issues that block forecasting. Then we build a baseline (naive seasonal, Prophet), engineer features for LightGBM, and select a neural architecture if needed. Walk‑forward validation with a realistic horizon. Deployment via API with automatic retraining scheduled via Airflow or Prefect.

Timeline: MVP forecast on one data type – 3–6 weeks. Hierarchical forecasting system with automation – 2–5 months. Cost is calculated individually based on data volume, number of series, and required accuracy.

Our team consists of certified ML engineers (AWS ML Specialty, GCP Professional ML Engineer) with 5+ years on the market and over 50 completed forecasting projects. Contact us for a free analysis of your data – we will assess the task and provide initial recommendations within 1–2 days. Request a consultation to ensure your forecasts work in production, not just in a notebook.