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

Physical activity is a key lifestyle factor for mental health prevention, yet the influence of accelerometer placement on mood prediction remains unclear. We merged high-resolution acceleration data and Ecological Momentary Assessment (EMA) mood reports from 259 healthy participants across three ambulatory studies (SedMood, 24 hrCog, HO). Additionally, 15 min pre-assessment movement windows consisting of raw triaxial acceleration (64 Hz) from hip, thigh, chest, and wrist sensors were paired with six-item mood EMA queries. Features (e.g., mean, entropy, spectral power) were extracted and fed into gradient-boosted decision tree models (XGBoost), trained separately for energetic arousal, valence, and calmness. Performance was measured using the metrics MAE, RMSE and R². Within individual studies, chest and hip sensors achieved the highest performance, followed by wrist and thigh. In the combined dataset, hip sensors again outperformed thigh (R² 0.38 vs. 0.20). Multi-sensor models rarely surpassed the best single-sensor configuration and sometimes reduced accuracy. These results suggest that sensor location modestly impacts mood-prediction performance, with hip and chest offering the most reliable signals, while adding sensors does not reliably enhance predictive power. Future work should explore larger, homogenous datasets and location-specific feature engineering to refine wearable-based mental health monitoring.