Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487-0286, USA.
Med Sci Sports Exerc. 2011 Jul;43(7):1312-21. doi: 10.1249/MSS.0b013e318206f69d.
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.
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.
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.
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.
本研究旨在开发和验证一种使用集成加速度计和压力传感器的鞋类系统预测能量消耗（EE）的方法。
我们开发了一种具有嵌入式加速度计和鞋垫压力传感器的鞋类设备，用于预测 EE。该设备的数据可用于准确识别主要姿势和活动，并使用加速计、压力和姿势/活动分类信息在分支算法中估算 EE，而无需单独校准。我们通过间接热量测定法测量 EE，16 名成年人（体重指数=19-39 kg·m）进行了各种低到中等强度的活动，并使用基于加速度和压力信号的几种模型比较了测量值与预测值的 EE。
包括压力数据可提高坐姿和站立等静态姿势下 EE 预测的准确性。与仅基于加速度计的分支模型 BACC（均方根误差（RMSE）=0.77 METs）和非分支模型（RMSE=0.94-0.99 METs）相比，包含来自加速度计和压力传感器的预测因子的基于活动的分支模型 BACC-PS（RMSE=0.69 METs）实现了最低的 EE 预测误差。与使用单腿数据的模型相比，使用双腿数据的 EE 预测模型的比较表明，只需要一只鞋配备传感器。
这些结果表明，当用于特定活动的分支模型时，脚部加速度与鞋垫压力测量相结合，可以准确估计与常见日常姿势和活动相关的 EE。基于鞋类设备的准确性和非侵入性可能使其成为有效的身体活动监测工具。
