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Predicting Momentary Mood in Daily Life from Accelerometer Data: Evaluating Single vs. Multiple Sensor Locations

Simon Woll1, Julius Müther1, Dennis Birkenmaier2

  • 1Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
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This summary is machine-generated.

Accelerometer placement affects mood prediction accuracy. Hip and chest sensors provide the most reliable signals for mental health monitoring, while adding more sensors does not consistently improve results.

Area of Science:

  • Wearable technology
  • Digital mental health
  • Biomedical engineering

Background:

  • Physical activity is crucial for mental health.
  • Understanding how sensor placement impacts wearable-based mood prediction is essential for accurate mental health monitoring.

Purpose of the Study:

  • To investigate the influence of accelerometer placement on mood prediction accuracy.
  • To determine optimal sensor locations for wearable mental health monitoring.

Main Methods:

  • Utilized high-resolution acceleration data and Ecological Momentary Assessment (EMA) mood reports from 259 participants.
  • Extracted movement features from hip, thigh, chest, and wrist sensors using gradient-boosted decision tree models (XGBoost).
  • Assessed model performance using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²).
Keywords:
accelerometermachine learningmental healthmoodwearable

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Main Results:

  • Chest and hip sensors demonstrated the highest performance in individual studies and the combined dataset.
  • Hip sensors outperformed thigh sensors (R² 0.38 vs. 0.20) in the combined analysis.
  • Multi-sensor models did not consistently improve accuracy and sometimes reduced performance compared to single-sensor configurations.

Conclusions:

  • Sensor location significantly, though modestly, impacts mood prediction performance.
  • Hip and chest sensors offer the most reliable data for mood prediction.
  • Future research should focus on larger datasets and location-specific feature engineering for enhanced wearable mental health monitoring.