ArticleNature communications2021
Sensing leg movement enhances wearable monitoring of energy expenditure.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed.
- A Multimodal Dataset of Full-Body Kinematics and Kinetics from Laboratory- and Wearable-Based Systems.Scientific data · 2026Article
- Use Treadmills with Caution: Walking Energy Expenditure and Metabolic Cost Are Elevated Compared to Overground Across Multiple Speeds in Healthy Young Adults.Journal of functional morphology and kinesiology · 2026Article
- Energy expenditure in women who participate to recreational mini-trampoline activities.BMC sports science, medicine & rehabilitation · 2026Article
- Estimating and interpreting how humans prioritize multiple movement goals during walking.Journal of neuroengineering and rehabilitation · 2026Article
- A Smart Headband for Multimodal Physiological Monitoring in Human Exercises.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review of electroencephalography and electromyography research in robotics: opportunities and challenges.Visual computing for industry, biomedicine, and art · 2026Review
- OpenMetabolics: Estimating energy expenditure using a smartphone worn in a pocket.Communications engineering · 2026Article
- Human motion recognition and prediction using loose cloth.Nature communications · 2026Article
- Comparison of smartwatch-derived energy expenditure estimation during outdoor activities.Frontiers in digital health · 2026Article
- Predicting physical activity energy expenditure, types, and intensity using long short-term memory networks in aged 6-18 cross-stage populations.Frontiers in physiology · 2026Article
- Estimation of walking energy expenditure using a single sacrum-mounted IMU based on biomechanically-informed machine learning.Scientific reports · 2025Article
- Natural variability can increase human walking metabolic costs and its implications to simulation-based metabolic estimation.Journal of the Royal Society, Interface · 2025Article
- 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
- Cam-Based Simple Design of Constant-Force Suspension Backpack to Isolate Dynamic Load.Biomimetics (Basel, Switzerland) · 2025Article
- Materials and device strategies to enhance spatiotemporal resolution in bioelectronics.Nature reviews. Materials · 2025Article
- Integrating personalized shape prediction, biomechanical modeling, and wearables for bone stress prediction in runners.NPJ digital medicine · 2025Article
- Behavioural energetics in human locomotion: how energy use influences how we move.The Journal of experimental biology · 2025Review
- Conducting polymer hydrogels based on supramolecular strategies for wearable sensors.Exploration (Beijing, China) · 2024Review
- Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry.The international journal of behavioral nutrition and physical activity · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Physical inactivity is the fourth leading cause of global mortality. Health organizations have requested a tool to objectively measure physical activity. Respirometry and doubly labeled water accurately estimate energy expenditure, but are infeasible for everyday use. Smartwatches are portable, but have significant errors. Existing wearable methods poorly estimate time-varying activity, which comprises 40% of daily steps. Here, we present a Wearable System that estimates metabolic energy expenditure in real-time during common steady-state and time-varying activities with substantially lower error than state-of-the-art methods. We perform experiments to select sensors, collect training data, and validate the Wearable System with new subjects and new conditions for walking, running, stair climbing, and biking. The Wearable System uses inertial measurement units worn on the shank and thigh as they distinguish lower-limb activity better than wrist or trunk kinematics and converge more quickly than physiological signals. When evaluated with a diverse group of new subjects, the Wearable System has a cumulative error of 13% across common activities, significantly less than 42% for a smartwatch and 44% for an activity-specific smartwatch. This approach enables accurate physical activity monitoring which could enable new energy balance systems for weight management or large-scale activity monitoring.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.