Estimating Energy Expenditure Using Wearable Sensors During Locomotion
Bibliographic record
Abstract
Introduction. Measures of metabolic energy expenditure can provide valuable insight into healthy and impaired gait, the design and control of assistive devices, and rehabilitation progress. The gold standard for estimating energy expenditure during locomotion is indirect calorimetry, where oxygen use is captured at the mouth. Although accurate, indirect calorimetry systems are expensive, cumbersome, and often limited to lab settings.
 Objective. The aim of our research is to develop a lightweight, portable, and low-cost method for accurately estimating energy expenditure using wearable sensors. Our method must meet the following design criteria: i. estimate walking and running energy expenditure within 5% error of gold standard measures, ii. maintain accuracy given changes to terrain and external loads, iii. provide a continuous estimate with estimate intervals a maximum of one minute apart, and iv. cost under $1000.
 Methods. In pilot testing, we instrumented two participants (male, 21-22 years, 84-90 kg, 1.88-1.90m) with indirect calorimetry to measure gold standard energy expenditure, as well as the following wearable sensors: an accelerometer at the pelvis and foot, a heart rate monitor, and a respiratory belt. The participants walked and ran on a predefined outdoor route on Queen’s campus, including sections with distinct average inclines (0% and 5%). Participants also wore ankle weights (3% body weight) for particular sections of the route. We will use a multiple regression analysis, with cross-validation design, to predict energy expenditure using custom metrics derived from the wearable sensors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".