Development and Independent Validation of Energy Expenditure Models Using SmartStep

被引：0

作者：

Hegde, Nagaraj ^{[1
]}

Swibas, Tracy A. ^{[2
]}

Melanson, Edward L. ^{[2
,3
]}

Sazonov, Edward ^{[1
]}

机构：

[1] Univ Alabama, Dept ECE, Tuscaloosa, AL 35487 USA

[2] Univ Colorado Anschutz Med Campus, Div Geriatr Med, Denver, CO USA

[3] Univ Colorado Anschutz Med Campus, Div Endocrinol Diabet & Metab, Denver, CO USA

来源：

2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22) | 2022年

关键词：

Activities of daily living (ADL); activity transition; energy expenditure; physical activity (PA); wearable sensors; RECOGNITION; ACCURACY;

D O I：

10.1109/BHI56158.2022.9926944

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this work we developed and validated a method to capture the activities of daily living (ADL), transitions between ADL, and the associated Energy Expenditure (EE) using a novel insole based wearable system (SmartStep). A 15-participant study was conducted in a controlled laboratory environment while participants wore the SmartStep and performed various ADL. Machine learning models were developed using 4-branched and 8-branched steady-state activities to estimate the total energy expenditure (TEE) and physical activity energy expenditure (PAEE). Additional models accounting for transitions between activities were also developed. These models were validated in an independent study with 8-participants, performed in a whole room indirect calorimeter. In the controlled study, the 8-branched models had a lower root mean square error (RMSE, 0.58 vs. 0.67 kcal/min) and lower total error (-1.5% vs. 3%). In the validation study, the 8-branched models also had a lower RMSE (0.9 kcal/min vs. 1.2 kcal/min) and lower total error (-4.5% vs 11%). Accounting for activity transitions reduced the total error in the EE estimation to -1.3%. The results suggested that SmartStep can be used to accurately monitor the EE of the wearers in their daily living. The validation study results suggested that 8-branched models more accurately predict EE than 4-branched models and that accounting for activity transitions improves the estimation of EE in daily living.

引用

页数：8