Accurate prediction of energy expenditure using a shoe-based activity monitor.
OBJECTIVE
The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors.


METHODS
We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals.


RESULTS
Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors.


CONCLUSIONS
These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.
Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487-0286, USA. nasazonova@bama.ua.edu
Nadezhda
N
Sazonova
Raymond C
RC
Browning
Edward
E
Sazonov
D000054:Acceleration; D000293:Adolescent; D000328:Adult; D000465:Algorithms; D015992:Body Mass Index; D002153:Calorimetry, Indirect / Q000295:instrumentation* / Q000379:methods; D004734:Energy Metabolism / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D009043:Motor Activity / Q000502:physiology; D011312:Pressure; D015203:Reproducibility of Results; D012775:Shoes; D055815:Young Adult