Browsing by Author "Olschewski, Marie"

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Open Access
Machine learning-based metabolic rate estimation from wearable sensors
(2025) Olschewski, Marie
Adaptive devices such as exoskeletons and prostheses can enhance human physical capabilities or replace the functionality of missing body parts. However, adjusting these devices for the specific needs of an individual remains a time-consuming and costly procedure. A key objective in optimizing these devices is minimizing the user’s energy expenditure (EE), a metric closely related to metabolic cost. Traditional methods for estimating metabolic cost, such as indirect calorimetry, are performed in controlled environments, limiting real-world applicability. This study aims to bridge this gap by exploring the use of traditional machine learning (ML) methods to estimate metabolic cost in real-time environments, utilizing wearable sensors integrated into adaptive devices. Using the dataset from Ingraham et al. (2019), which includes data from ten healthy subjects performing various exercises, the study investigates how different sensor combinations impact prediction accuracy. This thesis evaluated multiple ML models, including Random Forest (RF), Support Vector Machines (SVM), Linear Regression (LR), Decision Trees (DT), and Multilayer Perceptrons (MLP), within two cross-validation methods: Leave-One-Subject-Out (LOSO) and Leave-One-Time-Out (LOTO). Key findings from this evaluation include: In the LOSO setting, RF outperformed other models, achieving the lowest RMSE in several sensor regions, including Hexoskin, EMG Pants, and Best Combination, with the ’Best Combination’ region showing the best results. In contrast, MLP performed well in the LOTO setting, with its strongest performance observed in the ’Best Combination’ region. SVM demonstrated robust performance when all sensor data was combined, emphasizing the potential of multimodal sensor fusion. Hyperparameter tuning and sensor feature selection were crucial factors in optimizing model performance, particularly for more complex models like RF and MLP. The results suggest that while traditional ML methods can estimate EE effectively, challenges remain in refining preprocessing techniques, tuning hyperparameters, and optimizing sensor combinations. This thesis outlines the importance of model selection, sensor fusion, and parameter optimization in developing more accurate and real-time energy expenditure prediction systems for wearable technologies.