ArticleJournal of neuroengineering and rehabilitation2019
Rapid energy expenditure estimation for ankle assisted and inclined loaded walking.
Article in Journal of neuroengineering and rehabilitation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- OpenMetabolics: Estimating energy expenditure using a smartphone worn in a pocket.Communications engineering · 2026Article
- Energy-Expenditure Estimation During Aerobic Training Sessions for Badminton Players.Sensors (Basel, Switzerland) · 2025Article
- A time-efficient continuous ramp protocol for data-driven walking energy expenditure estimation across multiple speeds.Journal of neuroengineering and rehabilitation · 2025Article
- Optimizing hip exoskeleton assistance pattern based on machine learning and simulation algorithms: a personalized approach to metabolic cost reduction.Frontiers in robotics and AI · 2025Article
- Estimating within-stride metabolic cost from stride-average data using autoencoders and expander networks.Frontiers in bioengineering and biotechnology · 2025Article
- IMU-Based Energy Expenditure Estimation for Various Walking Conditions Using a Hybrid CNN-LSTM Model.Sensors (Basel, Switzerland) · 2024Article
- Human-in-the-loop optimization of wearable device parameters using an EMG-based objective function.Wearable technologies · 2024Article
- Opportunities and challenges in the development of exoskeletons for locomotor assistance.Nature biomedical engineering · 2023Review
- Muscle coordination and recruitment during squat assistance using a robotic ankle-foot exoskeleton.Scientific reports · 2023Article
- Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns.Journal of medical Internet research · 2022Article
- Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors.Sensors (Basel, Switzerland) · 2022Article
- Sensing leg movement enhances wearable monitoring of energy expenditure.Nature communications · 2021Article
- The exoskeleton expansion: improving walking and running economy.Journal of neuroengineering and rehabilitation · 2020Review
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Authors and funding
5 authors.
Funding
Abstract
backgroundEstimating 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.
methodsWe 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.
resultsThe 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.
conclusionsWe 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.
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