Sleep Pattern Analysis Using ML and Wearables

Sleep is crucial for physical and mental health. Wearable fitness devices capture detailed data like sleep duration, REM cycles, disturbances, and body movements. Analyzing this time-series data manually is challenging due to its volume and complexity. Machine learning and time-series analytics enable automated analysis of sleep behavior, allowing users to better understand their sleep quality and improve their health based on actionable insights derived from real-world wearable data.

Using datasets collected from wearable devices, machine learning models like LSTM networks (for time series), clustering (for sleep stage categorization), or statistical analysis can uncover hidden sleep patterns. Sleep metrics such as sleep onset time, sleep efficiency, deep sleep percentage, and wake after sleep onset (WASO) are computed. These insights are visualized to recommend personalized improvements in daily habits, sleep schedules, and lifestyle modifications for better rest.

Start by collecting sleep records from fitness trackers or simulated datasets. Preprocessing involves handling missing values, normalizing timestamps, and extracting sleep stages and movement patterns. Machine learning models classify sleep stages, detect disturbances, and predict optimal sleep/wake times. Visualizations like sleep efficiency charts, nightly trend graphs, and personalized recommendations help users improve their overall sleep hygiene and recovery strategies.

• Collect sleep logs from wearable devices (Fitbit, Apple Watch, Garmin, etc.) or public sleep datasets.
• Preprocess data by resampling timestamps, imputing missing data, and engineering features like total sleep time, REM cycles, and wake periods.
• Apply machine learning models like clustering, LSTM, or CNNs to classify sleep stages and detect anomalies in sleep patterns.
• Evaluate results using sleep quality metrics like Sleep Efficiency, Sleep Onset Latency (SOL), and WASO (Wake After Sleep Onset).
• Present results in interactive dashboards showing trends, personalized feedback, and actionable suggestions for better sleep.

• Sleep-EDF Expanded Dataset (Public)
• Wearable Sleep Data Analysis Techniques
• Building Time Series Models with LSTM