ArticleBiomedical engineering online2018
Estimating metabolic equivalents for activities in daily life using acceleration and heart rate in wearable devices.
Article in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Evaluating Heat Stress in Occupational Setting with No Established Safety Standards Using Collective Data from Wearable Biosensors.Sensors (Basel, Switzerland) · 2025Article
- From Verbal Reports to Personalized Activity Trackers: Understanding the Challenges of Ground Truth Data Collection with Older Adults in the Wild.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Cyber-Physical-Human Systems in Precision Medicine: Advances in Artificial Pancreas for Treatment of Diabetes.Annual reviews in control · 2025Article
- Article
- Commonly Used Subjective Effort Scales May Not Predict Directly Measured Physical Workloads and Fatigue in Hispanic Farmworkers.International journal of environmental research and public health · 2023Article
- Diurnal Profiles of Locomotive and Household Activities Using an Accelerometer in Community-Dwelling Older Adults with Musculoskeletal Disorders: A Cross-Sectional Survey.International journal of environmental research and public health · 2020Article
- Practical Judgment of Workload Based on Physical Activity, Work Conditions, and Worker's Age in Construction Site.Sensors (Basel, Switzerland) · 2020Article
- Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals.International journal of environmental research and public health · 2019Article
- Article
- Adapting a digital monitoring system for self-management to geriatric COPD rehabilitation: A participatory mixed method study.Digital healthArticle
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHerein, an algorithm that can be used in wearable health monitoring devices to estimate metabolic equivalents (METs) based on physical activity intensity data, particularly for certain activities in daily life that make MET estimation difficult.
resultsEnergy expenditure data were obtained from 42 volunteers using indirect calorimetry, triaxial accelerations and heart rates. The proposed algorithm used the percentage of heart rate reserve (%HRR) and the acceleration signal from the wearable device to divide the data into a middle-intensity group and a high-intensity group (HIG). The two groups were defined in terms of estimated METs. Evaluation results revealed that the classification accuracy for both groups was higher than 91%. To further facilitate MET estimation, five multiple-regression models using different features were evaluated via leave-one-out cross-validation. Using this approach, all models showed significant improvements in mean absolute percentage error (MAPE) of METs in the HIG, which included stair ascent, and the maximum reduction in MAPE for HIG was 24% compared to the previous model (HJA-750), which demonstrated a 70.7% improvement ratio. The most suitable model for our purpose that utilized heart rate and filtered synthetic acceleration was selected and its estimation error trend was confirmed.
conclusionFor HIG, the MAPE recalculated by the most suitable model was 10.5%. The improvement ratio was 71.6% as compared to the previous model (HJA-750C). This result was almost identical to that obtained from leave-one-out cross-validation. This proposed algorithm revealed an improvement in estimation accuracy for activities in daily life; in particular, the results included estimated values associated with stair ascent, which has been a difficult activity to evaluate so far.
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