Longitudinal Digital Phenotyping for Early Cognitive-Motor Screening

1. 1
   Define Digital Biomarkers and Objectives

   Clearly identify the specific cognitive-motor functions you aim to screen and the digital biomarkers (e.g., gait variability, activity patterns, reaction times) that can indicate atypical development. Understand the longitudinal nature of the data and the need to detect subtle changes over time.
2. 2
   Simulate Longitudinal Sensor Data

   Before real-world data integration, simulate continuous time-series data reflecting typical and atypical cognitive-motor patterns. Include various sensor types (accelerometer, gyroscope, GPS, etc.) and introduce subtle, time-varying deviations to represent early developmental changes. Use the provided starter code to generate a sample dataset.
3. 3
   Extract Time-Series Features

   From the continuous data streams, extract meaningful time-series features. This includes statistical aggregates (mean, standard deviation, variance), spectral features (e.g., FFT components), and temporal features (e.g., auto-correlation, trend analysis, periodicity). Consider features that capture both short-term variability and long-term trends.
4. 4
   Develop Anomaly Detection Models

   Implement machine learning models capable of detecting anomalies or deviations from typical developmental trajectories in the extracted features. Explore techniques like Isolation Forests, One-Class SVMs, Recurrent Neural Networks (RNNs), or statistical process control methods tailored for time-series data. Train models on 'typical' data to identify 'atypical' patterns.
5. 5
   Evaluate and Address Ethical Considerations

   Rigorously evaluate model performance using appropriate metrics for anomaly detection (e.g., precision, recall, F1-score). Critically consider the ethical implications of continuous monitoring, data privacy, consent, potential biases in algorithms, and the responsible communication of screening results to individuals and caregivers.

import numpy as np
import pandas as pd
import datetime

def simulate_cognitive_motor_data(num_days=90, subject_id='S001', seed=42):
    np.random.seed(seed)
    samples_per_hour = 4 # e.g., 4 sensor readings per hour
    total_samples = num_days * 24 * samples_per_hour

    start_date = datetime.datetime(2023, 1, 1)
    timestamps = [start_date + datetime.timedelta(hours=i/samples_per_hour) for i in range(total_samples)]

    # Simulate activity level (e.g., from accelerometer data)
    activity_baseline = 50 + 10 * np.sin(np.arange(total_samples) / (24 * samples_per_hour / (2 * np.pi))) # Daily rhythm
    activity_noise = np.random.normal(0, 5, total_samples)
    activity_level = activity_baseline + activity_noise

    # Simulate motor control metric (e.g., gait variability, reaction time proxy)
    # Introduce a subtle, increasing deviation after 30 days
    motor_control_baseline = 10 + 2 * np.sin(np.arange(total_samples) / (24 * samples_per_hour / (3 * np.pi)))
    motor_control_noise = np.random.normal(0, 0.5, total_samples)
    motor_control_metric = motor_control_baseline + motor_control_noise

    # Introduce a subtle trend representing atypical development
    atypical_onset_day = 30
    atypical_samples_start = atypical_onset_day * 24 * samples_per_hour
    if total_samples > atypical_samples_start:
        atypical_trend_magnitude = 0.05 # Start small, increase
        atypical_trend_slope = np.linspace(0, atypical_trend_magnitude * (num_days - atypical_onset_day), total_samples - atypical_samples_start)
        motor_control_metric[atypical_samples_start:] += atypical_trend_slope

    df = pd.DataFrame({
        'timestamp': timestamps,
        'subject_id': subject_id,
        'activity_level': activity_level,
        'motor_control_metric': motor_control_metric
    })
    return df

# Example usage:
simulated_data = simulate_cognitive_motor_data(num_days=120)
print(simulated_data.head())
print(simulated_data.tail())