Accelerometry-Based Machine Learning Estimation of Energy Expenditure and Physical Activity Intensity Classification in Daily Tasks

Accelerometers are widely used to estimate energy expenditure (EE) and classify physical activity intensity (PAI), although conventional approaches may not capture the complexity of raw acceleration signals. This study evaluated machine learning (ML) models for estimating EE and classifying PAI during daily and locomotor tasks. Forty-nine adults (28.4 ± 8.5 years) completed seven laboratory activities while wearing ActiGraph GT9X Link accelerometers on the right hip and dominant and non-dominant wrists. EE was measured by indirect calorimetry. Time- and frequency-domain accelerometry features from non-overlapping 1-s windows were combined with age, sex, body mass index (BMI), and body mass. K-nearest neighbors (KNN) and random forest (RF) models were developed for each placement using an 80/20 training–test split. RF achieved lower overall EE estimation errors and higher overall PAI classification agreement than KNN across placements. The lowest EE errors were obtained from RF models at the non-dominant wrist (RMSE = 0.67 kcal·min−1; MAE = 0.39 kcal·min−1) and dominant wrist (RMSE = 0.68 kcal·min−1; MAE = 0.40 kcal·min−1). RF classification agreement reached 95.81% (Kappa = 0.943) at the non-dominant wrist and 94.97% (Kappa = 0.931) at the hip. In this laboratory protocol, the non-dominant wrist RF model showed a favorable combination of EE estimation error and PAI classification agreement.