Estimating metabolic equivalents for activities in daily life using acceleration and heart rate in wearable devices

• Published in: Biomedical engineering online Vol. 17; no. 1; p. 100
• Main Authors: Nakanishi, Motofumi, Izumi, Shintaro, Nagayoshi, Sho, Kawaguchi, Hiroshi, Yoshimoto, Masahiko, Shiga, Toshikazu, Ando, Takafumi, Nakae, Satoshi, Usui, Chiyoko, Aoyama, Tomoko, Tanaka, Shigeho
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28.07.2018; BioMed Central; BMC
• Subjects: Acceleration; Accuracy; Activities of Daily Living; Adult; Algorithms; Analysis; Ascent; Atmospheric pressure; Calorimetry; Classification; Energy; Energy expenditure; Energy expenditure estimations; Equivalence; Female; Health monitors; Heart Rate; Humans; Lifestyles; Male; Metabolic Equivalent; Metabolic equivalents; Metabolism; Middle Aged; Monitoring, Physiologic - instrumentation; Multiple regression models; Physical activity; Physical activity classification; Regression analysis; Sensors; Signal Processing, Computer-Assisted; Triaxial acceleration; Wearable Electronic Devices; Wearable technology; Young Adult; Heart rate; Energy expenditure estimations; Triaxial acceleration; Physical activity; Metabolic equivalents; Physical activity classification
• DOI: 10.1186/s12938-018-0532-2
• ISSN: 1475-925X

Herein, 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. Energy 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. For 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.