Background Estimating energy expenditure with indirect calorimetry requires expensive equipment and several minutes of data collection for each condition of interest. While several methods estimate energy expenditure using correlation to data from wearable sensors, such as heart rate monitors or accelerometers, their accuracy has not been evaluated for activity conditions or subjects not included in the correlation process. The goal of our study was to develop data-driven models to estimate energy expenditure at intervals of approximately one second and demonstrate their ability to predict energetic cost for new conditions and subjects. Model inputs were muscle activity and vertical ground reaction forces, which are measurable by wearable electromyography electrodes and pressure sensing insoles. Methods We developed models that estimated energy expenditure while walking (1) with ankle exoskeleton assistance and (2) while carrying various loads and walking on inclines. Estimates were made each gait cycle or four second interval. We evaluated the performance of the models for three use cases. The first estimated energy expenditure (in Watts) during walking conditions for subjects with some subject specific training data available. The second estimated all conditions in the dataset for a new subject not included in the training data. The third estimated new conditions for a new subject. Results The mean absolute percent errors in estimated energy expenditure during assisted walking conditions were 4.4%, 8.0%, and 8.1% for the three use cases, respectively. The average errors in energy expenditure estimation during inclined and loaded walking conditions were 6.1%, 9.7%, and 11.7% for the three use cases. For models not using subject-specific data, we evaluated the ability to order the magnitude of energy expenditure across conditions. The average percentage of correctly ordered conditions was 63% for assisted walking and 87% for incline and loaded walking. Conclusions We have determined the accuracy of estimating energy expenditure with data-driven models that rely on ground reaction forces and muscle activity for three use cases. For experimental use cases where the accuracy of a data-driven model is sufficient and similar training data is available, standard indirect calorimetry could be replaced. The models, code, and datasets are provided for reproduction and extension of our results.
This project aims to estimate energy expenditure during ankle assisted and inclined loaded walking from wearable sensor signals. In this work we trained models using input features of muscle activity (EMG) and ground reaction forces to estimate energy exp
This project aims to estimate energy expenditure during ankle assisted and inclined loaded walking from wearable sensor signals. In this work we trained models using input features of muscle activity (EMG) and ground reaction forces to estimate energy expenditure during walking (1) with an ankle exoskeleton and (2) during various loaded and inclined walking conditions. These estimates were made every single gait cycle. We measure the performance of these models for cases where conditions or subjects data are not included in the training data to simulate real use cases. For research or clinical experiments where the accuracy of our models is sufficient these can be used to replace the standard indirect calorimetry methods of estimating energy expenditure which require expensive equipment and provide slow, noisy measurements. This repository contains the relevant formatted datasets, code to train models, and final model parameters to recreate our reported results. This code base should offer a support to apply our techniques to new datasets or utilize our fully trained models.